The new record on the global Microservices market gives snippets of data concerning the techniques for information assortment from different areas and information collecting units nearby social gathering information on different applications and conversing with different industry prepared experts and others who hold express degree and incorporation with the field and offer true information to the examination associations over the new occasions.

It gives data on pieces of a few boss models and perspectives that on a huge level effect the business share. Further, the record shows the total of the key affiliations that are working in the business space nearby their valuation, market share, encounters about the social affair units and present day workplaces of the relationship to the degree their zones and production worth and volume.

Leading Key Players of Microservices Industry. Analyzed Globally:

IBM
Microsoft
Salesforce
Infosys
NGINX
Oracle
Pivotal Software
Syntel
SmartBear Software
Marlabs
RapidValue Solutions
Kontena
Macaw Software
Unifyed
RoboMQ
Idexcel

Further, the record supports misleads and tips to the affiliations that are truly arising in the business space and helps the financial accomplices in settling on solid choices.

Market Segmentation by Type Consists:

On-Premise
Cloud Based

Market Segmentation by Applications Consists:

Retail and Ecommerce
Healthcare
Media and Entertainment
Banking, Financial Services, and Insurance
IT and ITes
Government
Transportation and Logistics
Manufacturing
Telecommunication
Others

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Moreover, the data on the global Microservices market is contained with thorough analysis of the production patterns, latest mergers, acquisitions, buyers, vendors, partnerships that have taken place in the market and hold a major impact on the industry share of the segments such as applications, products, competitors, and regional overview of the industry space. Further, it contains data on several CEOs and industry experts that have a major say in the events happening in the industry.

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As the one-year anniversary of the pandemic declaration has come and gone, it is getting more difficult to remember what business was like before March 11, 2020. Although we miss the human interaction of office work; some things should be left in the past. Even though vaccinations have created a light at the end of the tunnel, what it illuminates will not be like the past. The top 7 tech trends for 2021 not only reflect the journey to the light but also what lies beyond it.

In short, the 7 tech trends are the way forward for business. In tandem, they are what you can use to thrive in a post-pandemic world. However, you can’t do this with an infrastructure that is decades old. In this series of blogs, we have been showing you how modernization in the cloud is the foundation for adopting the 7 trends. We have already tackled customer experience (CX), data integration, digital culture, digital innovation, the composable enterprise, and automation. This blog looks at the final trend, microservices and service mesh.

Angels in the architecture: Microservices
Microservices have been around since 2009 at least. Recently, as more tech giants have publicized their moves to microservices, they have really taken off. This is because they resolve a lot of the problems of monolithic application architectures, which develop performance issues when they are scaled and are hard to change quickly. So, what are microservices exactly?

Microservices.io has this definition: “an architectural style that structures an application as a collection of services.” Software functionality is isolated into multiple independent modules that are individually responsible for specific standalone tasks. The modules communicate with one another via simple, universally accessible application programming interfaces (APIs). As a result, one team’s changes won’t break an entire app, unlike in monolithic application architectures. Microservices can be deployed in virtual machines or containers; however, containers are becoming the microservices deployment method of choice.

Amazon is a microservices pioneer. It wanted a more robust application that could handle new features and functions quickly. So, it moved from a monolith to a fully distributed, decentralized services architecture. Another pioneer is Netflix, who has built one of the most well-known examples of a modern microservices architecture – one that features an API gateway that handles about two billion API edge requests a day, all supported by over 500 microservices.

Communication breakdown: Service mesh
As the Netflix example shows, in the microservices world, an application can consist of hundreds of services. Each service could have thousands of instances. If the container deployment method is being used, a container orchestrator like Kubernetes is constantly changing the instances. This creates incredibly complex service-to-service communication, which is essential to microservices during runtime.

To ensure end-to-end performance, reliability, and security, organizations are increasingly turning to a service mesh, which adds observability, security, and reliability features to applications by inserting them at the platform layer rather than the application layer. As a result, the application doesn’t need to implement these features, or even be aware of the service mesh.

Lyft is an example of company that uses a service mesh it developed itself. Lyft’s Envoy, one of the earliest service meshes, enables services to communicate with one another through a proxy that is outside the application. It supports L3/L4 filter architecture like a TCP/IP proxy, HTTP/2 based L7 filter architecture, service discovery and active/passive health checking, load balancing, authentication and authorization, and observability. Microsoft, HP, Walmart, and Nordstrom use another service mesh called Linkerd. Cash App, eBay, Giphy, and Intuit use Istio, Google’s and IBM’s joint mesh service.

Microservices, service mesh, and modern cloud infrastructures
Microservices, service mesh, containers, and Kubernetes all enable you to offer differentiated digital services to your customers or consumers. Gartner predicts that these technical advancements will be in place in 70% of organizations by 2022. They are a key aspect of digital transformation, and they can also help you make other tech trends, such as customer experience and automation, a reality.

What should not go unnoticed is these modern application structures were not built for the infrastructures of yesteryear. Although microservices and containers can run on virtual machines, they are more efficient, scalable, and powerful in the cloud. Plus, Kubernetes and service meshes need the cloud. Therefore, an on-premises data center that houses an aging mainframe, legacy applications, and a rigid database with high licensing costs is going to severely inhibit their use.

Instead, you should migrate your mainframe and legacy applications to a cloud infrastructure designed for microservices, service mesh, and more. To do this, you need a legacy modernization platform. With a legacy modernization platform, you move your applications to a cloud-based environment with no reformatting, code changes or user impact. Once in this environment, you can modernize your apps and by breaking them into faster, responsive, agile microservices, and set up your service mesh. To enable your service mesh to help your microservice applications share data with one another, you should also consider a modern RDBMS that runs in the cloud.

Ready to break free from monoliths?
Two TmaxSoft products, OpenFrame and Tibero, can provide the legacy modernization platform and RDBMS you need to make microservices and service mesh part of your digital transformation strategy. Both solutions make it easy to transition your application architectures to the cloud. Learn more about modernizing your legacy applications and moving away from on-premises architectures by reading our eBook on proven modernization strategies.

Kelly McClure
Kelly McClure is the Vice President of Global Marketing for TmaxSoft. Her 20-year marketing career spans both Fortune 1000 companies and fast growth technology startups. Kelly is responsible for leading TmaxSoft’s marketing strategy. She is experienced in aligning marketing and sales, building relevant content and messaging and developing integrated lead generation campaigns. Before joining TmaxSoft, Kelly served as the Vice President of Marketing for 10th Magnitude and held senior marketing roles with DataStax, BMC Software and Micro Focus. Kelly has a bachelor’s degree from Purdue University and an MBA from Loyola University Chicago.

A new report on global Cloud Microservices market assessing various business strategies, cost stratification, raw material sourcing, manufacturing capabilities, cost assessment, along with demand and supply channel assessment are maneuvered in great detail. The report incorporates a figurative review of various DROT elements comprising potential propellants, growth deterrents, ample opportunities, indicative trends that structure out a detailed assessment of the market.

Market Segmentation Assessment
The report inculcates a thorough evaluation of the factors and developments that replicate triggers comprising inventory management, pricing and payment assessment, customer relationship management as well as growth navigation that incorporate ample competitive advantage.

Vendor Profiling: Global Cloud Microservices Market, 2020-26:

AWS
CA Technologies
IBM
Microsoft
Salesforce
Infosys
NGINX
Oracle
Pivotal Software
Syntel
SmartBear Software
Marlabs
Further in the report, readers are also evaluation of cost and pricing strategies, technological entrusted with specific details on payment and pricing trends, inventory management, support of logistics management to incorporate high revenue generation throughout the forecast trajectory. The report identifies dynamic segments such as product and service elements followed by market sub-segments that influence growth. A thorough evaluation of the segments is highly indispensable to undermine the implications of segments that allow market participants to prepare, realign and demonstrate high potential growth in global Cloud Microservices market.

Analysis by Type: This section of the report includes factual details pertaining to the most lucrative segment harnessing revenue maximization.

Public Cloud
Private Cloud
Hybrid Cloud
Analysis by Application: Further in the subsequent sections of the report, research analysts have rendered precise judgement regarding the various applications that the Cloud Microservices market mediates for superlative end-user benefits.

Retail and Ecommerce
Healthcare
Media and Entertainment
Banking
Financial Services
and Insurance
IT and ITes
Government

Regional Overview: Global Cloud Microservices Market
The following sections of the report also deals with thorough analysis and evaluation guide featuring geographical developments across various countries, proceeding beyond local developments across North and South American countries, Europe, MEA, and APAC. Mexico, Brazil, Argentina, France, Germany, UK, Singapore, South Korea, India, Japan, and India.

Cloud Microservices market is evaluated based on two segments i.e., by type and application and covers essential statistics and details for current and future market scenarios. The report actively includes informative aspects relating to product developments, launches, and trends, to assist market players, shareholders, and investors in strategic decision making.

The report also puts forward the current and future market scenario derived precisely from a thorough analysis of the markets across five major regions: North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA), and South & Central America. The exhaustive PEST analysis is done for each region to assess major external factors which may influence Cloud Microservices Market in the coming years.

The market study also includes the key drivers expected to boost the market growth and restraining factors along with future opportunities during the forecast. Cloud Microservices Market is likely to showcase symbolic growth opportunities owing to growing demands for Industrial Wax across the globe. The market report highlights top ten companies’ operation strategy in the market through a descriptive SWOT analysis of each company, business overview, products and services, financial statements of last three years, and key developments and product launches by the company in recent years.

The List of companies covered in this Reports are:

CA Technologies, Inc.
2. Infosys Limited
3. IBM Corporation
4. Microsoft Corporation
5. NGINX Inc.
6. OpenLegacy
7. Pivotal Software, Inc.
8. Salesforce.com, Inc.
9. Software AG
10. Syntel, Inc. (Atos SE)
The insights also cover the production, value, market share, and growth rate of these top manufacturers and investigate the competitive landscapes for a better understanding of the market scenario. The report is intended to help the readers develop a practical and intelligent approach to market dynamics and exploit opportunities accordingly.

Premium Market Insights is a one stop shop of market research reports and solutions to various companies across the globe. We help our clients in their decision support system by helping them choose most relevant and cost effective research reports and solutions from various publishers. We provide best in class customer service and our customer support team is always available to help you on your research queries. Our commitment to customer service is best exemplified by free analyst support that we offer to our clients which sets us apart from any other provider

New Jersey, United States,- The report, titled “Cloud Microservices Market Size By Types, Applications, Segmentation, and Growth – Global Analysis and Forecast to 2019-2027” first introduced the fundamentals of Cloud Microservices: definitions, classifications, applications and market overview; Product specifications; Production method; Cost Structures, Raw Materials, etc. The report takes into account the impact of the novel COVID-19 pandemic on the Cloud Microservices market and also provides an assessment of the market definition as well as the identification of the top key manufacturers which are analyzed in-depth as opposed to the competitive landscape. In terms of Price, Sales, Capacity, Import, Export, Cloud Microservices Market Size, Consumption, Gross, Gross Margin, Sales, and Market Share. Quantitative analysis of the Cloud Microservices industry from 2019 to 2027 by region, type, application, and consumption rating by region.

Impact of COVID-19 on Cloud Microservices Market: The Coronavirus Recession is an economic recession that will hit the global economy in 2020 due to the COVID-19 pandemic. The pandemic could affect three main aspects of the global economy: manufacturing, supply chain, business and financial markets. The report offers a full version of the Cloud Microservices Market, outlining the impact of COVID-19 and the changes expected on the future prospects of the industry, taking into account political, economic, social, and technological parameters.

In market segmentation by manufacturers, the report covers the following companies-

Amazon Web Services
Pivotal Software
Infosys
Oracle Corporation
Idexcel
Microsoft
CA Technologies
IBM
Salesforce.com
Tata Consultancy
Services Limited
How to overcome obstacles for the septennial 2020-2027 using the Global Cloud Microservices market report?

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The Dynatrace Application Security Module leverages existing Dynatrace tracing and artificial intelligence (AI) technologies to detect threats in real-time and then apply appropriate policies to automatically secure applications.

Andreas Lehofer, chief product officer for Dynatrace, said that with the rise of highly dynamic microservices-based applications, the time has arrived to extend the company’s platform into the realm of application security. In contrast, there aren’t typically as many changes to monolithic applications to justify adding a security module to an application performance management (APM) platform, he said.

Lehofer said the Dynatrace Application Security Module has also been designed to reduce false positives by only surfacing alerts concerning code that is actually running. Rival RASP platforms will detect modules and start generating alerts about libraries that may be present in an application environment but are not actually employed, said Lehofer.

The Dynatrace Application Security Module working with the Smartscape topology mapping and PurePath tools for analyzing code also make it possible for DevSecOps teams to better understand dependencies between microservices, he added.

At the core of the Dynatrace approach to DevSecOps is its OneAgent software for application instrumentation, which collects data and feeds it back to an observability platform that includes an artificial intelligence (AI) engine dubbed Davis. As the Dynatrace Application Security Module continues to evolve, IT organizations should expect Dynatrace to apply AI capabilities to application security more broadly, Lehofer said.

The ability of DevSecOps teams to automatically remediate applications that are discovered to have significant security issues depends on their ability to roll back applications after they have been deployed. Most IT organizations at the moment still have to manually roll back an application after it’s been deployed, he noted.

Longer-term it’s not clear to what degree AI might advance DevSecOps; however, the more automated the process of identifying vulnerabilities becomes, the easier it should be to automate remediation.

In the meantime, as the observability capabilities of platforms such as Dynatrace extend into application security, the easier it should become for organizations to embrace best DevSecOps practices by employing a module that is added on to an existing APM platform.

Dynatrace, of course, is betting microservices-based applications will increase the adoption of its observability platform. Historically, IT teams tended to employ an APM only to instrument their most mission-critical applications because of the costs involved. However, as IT environments become more complex in the age of microservices, platforms that can observe all the dependencies that exist in a distributed computing environment are now critical. Extending that platform to include application security further extends the economic case for investing in an observability platform.

It may be a while before most organizations reach a level of DevSecOps maturity that requires continuous observability, but as responsibility for application security continues to shift further left, it’s now only a matter of time before more automation is applied.

IBM has made available Quick Start for IBM Cloud Pak for Integration based on the Red Hat OpenShift platform on the Amazon Web Services (AWS) cloud.

Savio Rodrigues, vice president for integration and application platform offering management for IBM, says the Quick Start capability that IBM added to its Cloud Pak for Integration offering makes it easier possible to deploy IBM middleware encapsulated in containers on the AWS cloud.

Announced at the ongoing online AWS re:Invent conference, the Quick Start capability that IBM created is already available for IBM Cloud, with support for other clouds such as Microsoft Azure planned. Quick Start gives IT teams a set of best practices for deploying a set of IBM middleware that spans everything from application programming interface (API) integration and application data integration to messaging, event-driven publish/subscribe tools and file-transfer capabilities.

Rodrigues says it’s now apparent cloud computing is by definition hybrid. As such, IT teams will need a federated approach to integration that leverages the same middleware running on multiple clouds.

At the foundation of the IBM middleware strategy is an instanced of Red Hat OpenShift based on Kubernetes that can be deployed anywhere. IT organizations need platforms such as IBM Cloud Pak for Integration to better manage all the microservices that make up a cloud-native computing environment, he notes.

IT teams will also be able to more flexibly deploy a mix of applications constructed using microservices alongside legacy monolithic applications that can access the same middleware capabilities enabled by IBM, Rodrigues says.

In addition, IT teams will be able to flexibly manage varying service levels by controlling application traffic flows across between API endpoints, he says. As cloud-native applications become more distributed, Rodrigues notes that level of control will enable IT teams to meet service level agreements (SLAs) spanning different classes of cloud-native applications.

In addition, IT teams will be able to map dependencies between microservices more easily, he says.

IBM is positioning IBM Cloud Pak for Integration as a complement to the open source Istio service mesh it created in collaboration with Google and Lyft. IBM Cloud Pak for Integration will be able to, for example, invoke Istio as a security gateway that validates whether a specific microservice should be trusted, says Rodrigues.

Over time, he notes, IBM also plans to apply machine learning algorithms and other forms of artificial intelligence to make it easier to manage increasingly complex IT environments consisting of hundreds, possibly even thousands, of microservices.

IBM is not the only provider of integration software that views the rise of microservices based on containers as a major new opportunity. However, the company is fairly far down the path toward making integration software available that runs natively on Kubernetes clusters.

It may take some time for the bulk of organizations to deploy the number of microservices that would justify an investment in IBM Cloud Pak for Integration. However, it’s now more a question of when that middleware will be needed rather than if.

Breaking business actions into their smallest possible units through microservices seems like a wonderful idea. However, in business-critical scenarios, the architecture needs to align and coordinate a half-dozen different distributed services and systems. If any step fails, an entire business process may need to roll back and remediate, much like a failed database transaction.

Microservices, by default, have no coordinating or controlling function: Individual services should inherently have no knowledge of the others outside of them. As such, maintaining smooth process flows between systems is much easier said than done without some kind of supervising controller mechanism. Fortunately, we can accomplish this through an architectural design known as the saga pattern.

What is the saga design pattern?
Picture an electronic data interchange (EDI) system interface that takes orders from customers and passes it to a product ordering system. That ordering service then sets off a chain of service calls that alert downstream manufacturing and shipping systems to finish the transaction. The diagram below shows a sample pattern illustrating this, where services call each other in a “round robin” style. Once the entire transaction chain is finalized, shipping sends a message to the product order service to confirm its completion. This can be thought of as a service choreography method.

Typically, the application will perform each one of these actions one at a time. If both the manufacturing order and the shipping order go through, but the payment transaction fails, either a team member or system must send the preceding services an alert to roll back.

Unfortunately, things get a little more complex when large-scale business transactions span significant periods of time. If any of those systems fail along the way, or the order is cancelled, we need a system that can perform a logical rollback and reset all those systems and transactions. For instance, a single failure during the payment transaction could very well require teams to roll back dozens of previous transactions that were completed by dozens of separate systems.

Airline ticketing systems are a perfect example of this problem. An unexpected event can cause people to cancel a trip minutes before the plane takes off. That single cancellation will cause ticketing systems to adjust seat availability, luggage to reroute, payment systems to issue any necessary refunds — and those are likely just a few of the steps involved.

As you can imagine, this system will need some way to “backwash” itself by reversing some of the earlier messages between the web services. Unfortunately, our transactions are just too complex and long-running to try to simply call all the services and commit them. This requires a controller with a little more nuance than a master program. This requires a controller that can take ownership over the entire process, which is where the saga pattern steps into the picture to deliver service orchestration.

Implementing saga for web services
To illustrate the saga design pattern, imagine that your team implements an enterprise service bus that listens for particular transaction events, and then forwards messages to the systems to start their operation. Once the bus creates message that represents the event, it is sent to any service associated with that event. In this case, the controller is a web service that is triggered by that event. That controller makes function calls to the next business web service in the queue.

Note this gives us two kinds of services:

The controllers, which receive events in the form of messages and then relay functional instructions to other services; and
The services that carry out the actual business process that needs to happen, and then communicate their completion to move the transaction along.

To implement these controller services, you can essentially create an event handler for an event-driven application, or introduce a finite state machine that simulates sequential logic. That component can then take the message, determine where it came from, analyze its status, and then process the next command. This can be accomplished simply through a switch statement, a set of nested IF commands, or even a single database lookup.

Keep in mind that implementing this design can still be tricky if the overall application demands high reliability. For example, imagine the controller service crashes after it triggers an ‘order placed’ event, but before it can pass a ‘payment complete’ event. When that service restarts, it will need to access some sort of transaction log, find the unprocessed transactions, resubmit the event (or events), and mark the work as done. This leaves the possibility that if a commit fails, a system will shut itself down after it sends the event, but before it actually confirms the commit. There are a number of architectural patterns that resolve this specific issue, but the simplest by far is to allow redundant messaging, but program the services to ignore them as needed.

Should you implement the saga design pattern?
The goal of the saga design pattern is primarily to take long-running, multisystem business processes and add the ability to roll back failed systems in an intelligent manner. However, it does add more code, which means new layers of complexity, debugging challenges, bandwidth requirements and processing power.

Put bluntly, an orchestration-focused saga pattern will usually prove to be overkill for simple application-based transactions. Unless your organization is notably struggling to manage large chains of business processes, the code complexity involved in a saga design pattern may cause more problems than it solves. But, if long-running transactions keep you up at night — especially when it comes to handling failures — the saga pattern may be the answer you’ve been looking for.

WEST HARTFORD, Conn.–(BUSINESS WIRE)–OWIT Global, a provider of insurance-specific microservices solutions for the global insurance industry, announces an advanced release of their Bordereaux Management Solution. The new release expands insurance-specific business rules, furthers functionality to support incoming and outgoing data feeds, and contains additional pre-defined performance dashboards.

The Bordereaux Management Solution solves the insurance industry’s underserved need to automate bordereaux processing for both MGAs and Carriers. Today, the handling of policy, claims, and cash data is onboarded manually or by software development, both of which are quite time-consuming.

“We continue to advance our solution with input from our existing customers. For the carrier, we support complex data sets from multiple formats, such as excel spreadsheets with multiple cross-referenced tabs and delimited/fixed-length file formats. For our MGAs, we support extraction and mapping of the data from multiple databases to various carrier contract formats,” said Wendy Aarons-Corman, Chief Executive Officer of OWIT Global. “Our solution also has an analytics service built-in, providing our customers with operational dashboards to evaluate the data running through the solution.”

The OWIT Global Bordereaux Management Solution is a no-code, configurable environment, giving the power of onboarding and managing bordereaux data streams to the business user. Data is consumed, cleansed, and transformed into a desired output file. Rules and 3rd party calls to validate data is also supported.

“Our customers are experiencing an immediate ROI. The time to configure and test has a dramatic reduction compared to the traditional model,” Aarons-Corman continued. “We encourage the industry to take some time to investigate our solution. There is a huge advantage in leveraging an insurance-specific solution that is continually advancing with input from other insurers. It is a great opportunity to get a feature-rich solution at a reasonable cost.”

About OWIT GLOBAL

OWIT is a global insurance technology provider offering solutions built on a modern architecture designed to simplify innovation and integrate with existing customer environments to maximize their investments. The OWIT portfolio is comprised of discrete, value-add insurance-based microservices for Bordereaux, Rules, Rating, Portal Configuration, A&H, and Specialty Point of Sale and Document Generation with future capability to tailor a full suite of Policy Administration System (PAS) microservices. The company’s offerings can be deployed standalone or integrated with an insurer’s existing environment. OWIT’s architecture is also designed to support emerging technologies and functionality

Take a random sample of internet traffic or traffic flowing across the average enterprise network, and you will probably find that the majority (some estimates put the figure as high as 70%) of packets are destined for, coming from, or are related in some way to API activity.

Even applications that could be described as monolithic usually comprise discrete modules, services, microservices, and so on, which function internally by means of APIs. Regardless of design, even an individual application’s topology often spans multiple public clouds, on-premise resources, edge-based facilities, leased real estate in data centers, and so on. Multiply the complexity of a single application or service by a large number, and the predominance of API-related traffic is explained.

Therefore, machine-to-machine data can be regarded as comprising critical traffic, and managing such traffic comprises essential activity for IT departments. As well as handling structural issues, there are also considerations of security, traffic prioritization, routing issues, API call SLAs, and much, much more. Little wonder that products that rather modestly describe themselves as “API gateways” or “API management suites” are now considered pivotal parts of the entire IT stack.

Here at TechHQ, we consider three vendors of API Management systems that go beyond monitoring and maintaining APIs. Each is capable of deployment across hybrid environments and offers scalability and elasticity that will fit in with teams’ agile approach to providing the services demanded of them.

Simplifying and securing API estate needs to be a short- to medium-term goal, and with the products we feature here, achieving this alone should provide a return on investment.

Furthermore, because data/information is now rightfully regarded as the most valuable commodity an organization can access, each of the featured vendors also goes beyond providing the basics of reliability and security. Examples include API change audits that correspond with the business’s change management procedures in ITIL workflows or consolidate data from multiple gateways.

There is also a case for leveraging these platforms for analysis to discover use trends across APIs to allocate attention and resources better. The same metrics can be used by BI and reporting suites to create viable SLAs for internal stakeholders and external partners alike. And as organizations make deeper forays into microservices as part of their development activities, there will be a greater need for dependency tracking to keep applications running (obviously) and ensure continuing compliance with data regulations.

It’s sometimes difficult to take a step back from the technical complexities of the solutions we feature below and try to look more objectively at the desired outcomes for enterprise IT decision-makers considering their options. Keeping a business-first mentality that’s focused on outcomes required by the greater enterprise is the end goal, but we feel the vendors covered here are beyond the point of pushing a product for its technical “smarts.” Each is conversant with the pressures on corporate-level IT today and offers answers to the questions being posed to IT professionals in boardrooms across the world. Given the immense importance of APIs in today’s technology-driven world, finding long-term partners for this area of the IT stack is probably one of the more important decisions you will take in your career.

NEVATECH

Since its first offering, the Sentinet API Management platform has been designed to offer a unified solution in very diverse networks.

Any Sentinet component works the same way regardless of where they are deployed: on-premise, in hybrid environments, and cloud settings. Deployments are not limited by their particular subnet, so an on-premise Sentinent can oversee APIs in the DMZ, or highly-restricted testing environments, or full production settings, using the same interface and the same toolset.

Use-case aggregation includes providing the same utility to all areas of the wider network so that production systems can be monitored for SLA violations. At the same time, alerts are given to DevOps teams to help them manage APIs actively and pro-actively to anticipate and remedy operational malfunctions. Sentinet also supports microservice-based applications by offering aggregated access and central view on the isolated microservices.

Nevatech aims to simplify API infrastructure and reduce the number of vendors on which the IT department depends for this mission-critical area of the enterprise. That has obvious security and reliability plus-es while reducing overheads and resource-drain. The Sentinet platform’s interoperability gives decision-makers the ability to migrate environments without massive reconfiguration of API management nodes creating roadblocks, and the same ability also ensures an unchanging security posture.

Traffic flow monitoring and predictive analysis mean procurement and resource planning for expansion or new projects becomes simpler. At the same time, security standards remain high thanks to granular access, security tokens and key management systems.

AXWAY

The AMPLIFY API Management platform helps teams create and publish APIs from the cloud or on-premise server instances and then provides management frameworks to monitor and control all APIs across the enterprise.

There are built-in analysis tools in AMPLIFY so that users can see live operational data or even developer uptake metrics (API “popularity contests,” if you will).

The analysis routines can flag anomalies and report on overall API stack health, or users can use “traditional” logfile analysis, Splunk, and so on — the AMPLIFY API Management system is as extensible as an API.

Axway is a heavily business-focused outfit, and the AMPLIFY platform can be used to monetize API use easily, making APIs reusable: develop once, deploy (and charge) multiple times. That facility is via AMPLIFY Central Service, which centralizes monetary issues like charges and subscriptions of every and any API across the enterprise.

Axway is building a complete developer and user community for its products, with an internal marketplace available to all subscribers where code and methodologies can be shared, thus vastly improving time to market for new services’ APIs.

The recent study report on Microservices market aims to provide an end-to-end analysis of this industry vertical with respect to drivers, challenges, opportunities that will influence the business growth in coming years. Furthermore, the report elaborates the industry segmentation in great length to uncover the top growth prospects for the stakeholders in the upcoming years.

According to industry analysts, the Microservices market is predicted to garner considerable gains with a CAGR of XX% during the forecast period 2020-2026.

Considering the latest updates, the outbreak of COVID-19 has severely impacted several businesses worldwide, leading to uncertainties in economic conditions. Although the pandemic hasn’t affected some industries, a significant number of businesses are being forced to cut down on costs and alter their strategies. Our detailed insights into the changing market dynamics post the COVID-19 pandemic aims to help the partakers develop strong contingency plans to ensure strong returns in the future.

Prediction of growth rate of the market and its sub-markets during the analysis timeframe.
Global COVID-19 impact on industry growth trends.
Major opportunities.
Statistical coverage of overall sales volume and revenue.
Advantages and disadvantages of indirect and direct sales channels.
Vitals regarding the top traders, dealers and distributors.
Microservices market segments covered in the report:

Regional bifurcation: North America, Europe, Asia-Pacific, South America and Middle East and Africa

Country-wise analysis.
Figures pertaining to total sales and returns captured by each geography.
Market share held by each region.
Information on estimated growth rate values as well as revenue secured by each region during the forecast period.
Product types: On-Premise and Cloud Based

Product pricing patterns and market share accounted by each product type.
Sales and revenue generated by each product category.
Application spectrum:

Retail and Ecommerce
Healthcare
Media and Entertainment
Banking
Financial Services
and Insurance
IT and ITes
Government
Transportation and Logistics
Manufacturing and Telecommunication
Pricing of the given products based on their application scope.
Sales volume and revenue of each application segment.
Competitive backdrop: The major players covered in Microservices are:, IBM, Syntel, Infosys, Microsoft, Pivotal Software, Salesforce, Marlabs, Oracle, NGINX, SmartBear Software, RoboMQ, RapidValue Solutions, Idexcel, Unifyed, Kontena and Macaw Software

Products and services offered by major players.
Manufacturing units and operational regions of leading competitors across the regional markets.
Evaluation of each participant using SWOT analysis.
Critical information regarding the pricing model, revenue, sales, gross margins and industry share held by each company.
Analysis of commercialization rate, well-known business strategies, market concentration ratio, and other business-centred aspects.
Major Points in Table of Contents:

1 Microservices Market Overview
2 Microservices Market Company Profiles
3 Market Competition, by Players
4 Microservices Industry Size by Regions
5 North America Microservices Revenue by Countries
6 Europe Microservices Revenue by Countries
7 Asia-Pacific Microservices Revenue by Countries
8 South America Microservices Revenue by Countries
9 Middle East & Africa Revenue Microservices by Countries
10 Market Size Segment by Type
11 Global Microservices Market Segment by Application
12 Global Microservices Market Size Forecast (2021-2026)

Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations comprise a vast array of applications and systems, many of which have turned to APIs to exchange data as the glue to hold these heterogeneous artifacts together. APIs have begun to replace older, more cumbersome methods of information sharing with lightweight, loosely-coupled microservices. This gives organizations the ability to knit together disparate systems and applications without creating the technical debt inherent in custom code or proprietary, unwieldy vendor tools. APIs and microservices also allow companies to establish standards and govern the interoperability of applications—both new and old—creating modularity. Additionally, they broaden the scope of data exchange with the outside world, particularly mobile technology, smart devices, and the Internet of Things, because organizations can securely share data with non-fixed location consumers and information producers.

The popularity and proliferation of APIs and microservices has created a need to manage the multitude of services that companies rely on—both internal and external. APIs themselves vary greatly in their protocols, methods, authorization/authentication schemes, and usage patterns. Additionally, IT needs greater control over their hosted APIs, such as rate-limiting, quotas, policy enforcement, and user identification, to ensure high availability and prevent abuse and security breaches. Also, APIs have enabled an economy of their own by allowing businesses to transform into a platform (as well as the opposite—a platform into a business). Exposing APIs opens the door to many partners who can co-create and expand the core platform without even knowing anything about the underlying technology.

APIs gain prominence as part of the SOA movement. Initially, APIs primarily used heavier, more complex XML protocols (e.g. SOAP), which eventually became a bottleneck to adoption across enterprises. APIs using HTTP protocols (REST) have become more popular and are critical parts of most enterprise architectures.

However, REST is only one piece of the puzzle. As architectures and the APIs underlying them continue to evolve, APIs and API management that encompass high-performance protocols (like gRPC) and streaming (kafka) will become essential.

Still, many organizations depend on their apps, APIs, and microservices for high performance and availability. For this paper, we define “high performance” as companies that experience workloads of more than 1,000 transactions per second (tps) and require a maximum latency of fewer than 30 milliseconds across their landscape. For these organizations, their need for performance is tantamount to their need for management because they rely on these API transaction rates to keep up with the speed of their business. For them, an API management solution must not become a performance bottleneck. On the contrary, many of these companies are looking for a solution to load balance across redundant API endpoints and enable high transaction volumes. Imagine a financial institution with 1,000 transactions happening per second—this translates to 86 million API calls in a single 24-hour day! Thus, performance is a critical factor when choosing an API management solution.

The rise of containers and Kubernetes has increased the need and importance of high-performance, lightweight solutions. The ability of gateways to work natively and effectively with these platforms (K8s and containers) is critical to maximizing their value. Gateways that aren’t able to perform at the level required by modern architectures can become a bottleneck to transformation efforts.

In this paper, we reveal the results of performance testing we completed across three API and Microservices Management platforms: Kong Enterprise, Apigee Edge, and Apigee Edge Microgateway. In this performance benchmark, Kong came out a clear winner—particularly at the higher rates of transaction volume per second.

We experimented with syslog on and off and with different authentications, including none, and consistently received better transactions per second from Kong over Apigee Edge and Apigee Edge Microgateway. For example, with syslog on and authentication off, Apigee Edge Microgateway produced more than 10 times the latency of Kong at the 99.9th percentile and beyond.

Kong recorded 40,625 maximum transactions per second of throughput, achieved with 100% success (no 5xx or 429 errors) and with less than 30ms maximum latency. Apigee Edge, by contrast, produced 2,650 transactions per second, while Apigee Edge with Microgateway produced 12,950 transactions.

Testing hardware and software in the cloud is very challenging. Configurations may favor one vendor over another in feature availability, virtual machine processor generations, memory amounts, storage configurations for optimal input/output, network latencies, software, operating system versions, and the workload itself. Even more challenging is testing fully managed, as-a-service offerings where the underlying configurations (processing power, memory, networking, etc.) are unknown. Our testing demonstrates a narrow slice of potential configurations and workloads.

As the report’s sponsor, Kong opted for a default Kong installation and API gateway configuration out of the box—the solution was not tuned or altered for performance. The fully managed Apigee Edge was used “as-is,” since, by virtue of being fully managed, we have no access, visibility, or control of its respective infrastructure. GigaOm selected a similar hardware configuration for Apigee Edge Microgateway.

We hope this report is informative and helpful in uncovering some of the challenges and nuances of security architecture selection.

We have provided enough information in the report for anyone to reproduce this test. You are encouraged to compile your own representative workloads and test compatible configurations applicable to your requirements.

What Are Microservices in Healthcare?

Microservices is an architectural approach in which a large application is decomposed into small, independently deployable services that communicate over well-defined APIs. In healthcare, this model enables hospitals, insurers, laboratories, and life-science companies to build and update specific capabilities — such as patient scheduling, claims processing, or lab result delivery — without redeploying an entire monolithic system. The result is faster iteration, better fault isolation, and easier scaling of individual workloads.

Market Segmentation

The microservices-in-healthcare market is typically analyzed across four dimensions: component, deployment mode, end-user, and geography.

By Component

Offerings in this market fall into two broad component categories:

• Platforms — orchestration and runtime environments (container platforms, API gateways, service meshes) that underpin microservices deployments. Platform vendors currently hold a large share of overall market revenue.
• Services — professional and managed services that help healthcare organizations plan, integrate, and sustain microservices environments:
  • Consulting services
  • Integration services
  • Training, support, and maintenance services

The services segment is expected to grow quickly as healthcare IT teams seek external expertise to navigate complex migration projects and regulatory requirements.

By Deployment Mode

• On-premise — preferred by organizations with strict data-sovereignty or compliance requirements.
• Cloud-based — the faster-growing category, further divided into:
  • Public cloud
  • Private cloud
  • Hybrid cloud

Hybrid cloud models are particularly relevant in healthcare because they allow sensitive patient data to remain on-premise or in a private environment while less sensitive workloads run on public infrastructure for cost and scalability benefits.

By End-User

• Healthcare payers — insurers and managed-care organizations modernizing claims, prior-authorization, and member-portal systems.
• Healthcare providers — hospitals and clinics improving EHR interoperability, patient engagement, and operational workflows.
• Clinical laboratories — labs automating result delivery, instrument integration, and quality-management pipelines.
• Life science organizations — pharmaceutical and biotech companies accelerating drug-development data pipelines and regulatory submissions.

By Region

• North America — projected to lead adoption, driven by a mature cloud ecosystem, significant healthcare IT investment, and regulatory incentives around interoperability (such as HL7 FHIR mandates).
• Europe — growth supported by digital-health initiatives and GDPR-compliant cloud strategies.
• Asia-Pacific — rapid expansion tied to telemedicine growth and government-led healthcare digitization programs.
• Latin America — emerging adoption as healthcare providers invest in modern infrastructure.
• Middle East & Africa — early-stage but growing, particularly in Gulf Cooperation Council countries with large-scale smart-hospital projects.

Key Market Drivers

• Faster project delivery — independent deployability of services shortens release cycles, allowing healthcare organizations to ship features and fixes without system-wide downtime.
• Interoperability requirements — regulatory pressure to expose data through standardized APIs fits naturally with microservices patterns.
• Scalability of critical workloads — services such as appointment booking or real-time monitoring can be scaled independently without over-provisioning the entire application.
• Digital health acceleration — telehealth, remote patient monitoring, and AI-assisted diagnostics all benefit from the modular, API-first approach that microservices enable.

Key Challenges and Restraints

• Security and compliance complexity — distributing an application across many services expands the attack surface and complicates HIPAA, GDPR, and other regulatory compliance efforts. Each inter-service communication channel must be secured and audited.
• Operational complexity — managing dozens or hundreds of independent services requires mature DevOps practices, container orchestration (typically Kubernetes), service-mesh tooling, and robust observability stacks. Organizations without these capabilities face a steep learning curve.
• Data consistency — microservices favor decentralized data management, which can make maintaining data integrity across services more difficult than in a monolithic database.
• Vendor and skills shortage — healthcare IT teams often lack the cloud-native expertise needed to design and operate a microservices environment safely.

Notable Vendors Active in This Space

A range of cloud, platform, and software vendors offer products and services relevant to microservices deployments in healthcare settings, including:

• Amazon Web Services (AWS) — ECS, EKS, API Gateway, and a broad set of HIPAA-eligible services
• Microsoft Azure — AKS, Azure API Management, and Azure Health Data Services
• IBM — hybrid-cloud integration and Red Hat OpenShift
• Oracle Corporation — Oracle Cloud Infrastructure and health data platforms
• Salesforce — Health Cloud built on a microservices-oriented platform
• NGINX (now part of F5) — API gateway and load-balancing layer commonly used in microservices architectures
• Pivotal Software (now VMware Tanzu) — application platform and developer tooling
• CA Technologies (now Broadcom) — API management and DevOps toolchains
• Syntel (now Atos Syntel) — healthcare IT integration and managed services

This list is not exhaustive; the ecosystem also includes numerous health-IT specialists, system integrators, and open-source communities (CNCF, HL7 FHIR implementers) that contribute to microservices adoption in the sector.

Practical Considerations for Healthcare DevOps Teams

If your organization is evaluating or actively adopting microservices in a healthcare context, the following engineering considerations deserve early attention:

1. Define service boundaries around business capabilities, not technical layers. Align services with clinical or operational domains (e.g., patient identity, lab orders, billing) to reduce inter-service coupling.
2. Implement a zero-trust security posture from the start. Mutual TLS between services, fine-grained authorization policies, and comprehensive audit logging are non-negotiable in regulated environments.
3. Invest in observability before scaling. Distributed tracing (OpenTelemetry), structured logging, and centralized metrics are essential for debugging failures across service boundaries.
4. Choose your orchestration platform early. Kubernetes is the dominant runtime for microservices in production; evaluate managed offerings (EKS, AKS, GKE) against your compliance and operational constraints.
5. Establish API governance. Consistent API versioning, documentation (OpenAPI), and deprecation policies prevent breaking changes from cascading across dependent services.

Microservices adoption in healthcare is not a quick transformation. Organizations that succeed treat it as a multi-year journey — starting with new greenfield services or a strangler-fig migration of high-value monolithic components — rather than a wholesale rewrite.

What Are Microservices in Healthcare?

Microservices architecture breaks a large application into small, independently deployable services that communicate over well-defined APIs. In healthcare, this approach is increasingly applied to electronic health record (EHR) platforms, claims-processing engines, laboratory information systems (LIS), and patient-engagement portals. Each service — scheduling, billing, lab results, medication management — can be developed, scaled, and updated without touching the rest of the platform.

The shift matters in healthcare because regulatory requirements (HIPAA, HL7 FHIR interoperability mandates) and patient-safety stakes make monolithic, hard-to-change systems increasingly costly. Microservices let engineering teams release compliant updates faster and contain the blast radius of any single failure.

Key Market Segments

By Deployment Model

• On-premise: Preferred by large hospital systems and regulated payers that must keep patient data within their own data centers to satisfy data-residency or contractual requirements.
• Cloud-based: The faster-growing segment, driven by reduced infrastructure overhead, elastic scaling, and native support for container orchestration platforms such as Kubernetes.

By Application

• Healthcare Payers: Insurers and managed-care organizations use microservices to modernize claims adjudication, member portals, and prior-authorization workflows.
• Healthcare Providers: Hospitals and outpatient clinics adopt microservices to integrate EHR data, telemedicine modules, and revenue-cycle management without replacing their core systems wholesale.
• Clinical Laboratories: High-throughput labs benefit from independently scalable services for order management, result delivery, and instrument integration.
• Life Science Organizations: Pharmaceutical and biotech companies apply microservices to clinical-trial data pipelines, regulatory-submission platforms, and genomics data processing.

By Geography

Market analysis typically covers North America, Europe, China, Japan, Southeast Asia, India, and Central & South America. North America has historically led adoption, driven by large-scale EHR consolidation and strong cloud-provider ecosystems. Asia-Pacific regions are growing rapidly, fuelled by government digital-health initiatives and expanding hospital networks.

Market Drivers

• Interoperability mandates: Regulations such as the US ONC Cures Act Final Rule require open FHIR APIs, rewarding architectures that can expose discrete, versioned endpoints — a natural fit for microservices.
• Scalability demands: Telehealth surges (accelerated by the COVID-19 pandemic) exposed the limits of monolithic platforms; microservices allow individual high-load components to scale horizontally.
• DevOps and CI/CD adoption: Healthcare IT teams are increasingly adopting continuous delivery pipelines. Microservices align naturally with independent service pipelines, enabling faster and safer releases.
• Cloud-native infrastructure maturity: Managed Kubernetes services (EKS, AKS, GKE) lower the operational barrier to running containerized microservices in production.
• Vendor ecosystem growth: A broadening market of API gateways, service meshes (Istio, Linkerd), and observability tools reduces the engineering burden of operating many services.

Market Restraints and Challenges

• Operational complexity: Distributed tracing, service discovery, and inter-service authentication add significant operational overhead compared with monolithic deployments.
• Security surface area: Each service-to-service API call is a potential attack vector. Healthcare organizations must implement mTLS, zero-trust networking, and rigorous secrets management across every service boundary.
• Regulatory compliance: HIPAA audit trails, data-at-rest encryption, and breach-notification obligations must be enforced consistently across all services — a non-trivial governance challenge.
• Legacy integration: Many hospitals still run HL7 v2 interfaces and legacy COBOL billing systems. Wrapping these in microservices facades adds integration complexity without eliminating the underlying technical debt.
• Skills gap: Expertise in container orchestration, service mesh configuration, and distributed-systems debugging remains scarce in many healthcare IT departments.

Common Questions for Healthcare Engineering Teams

When evaluating a microservices strategy, healthcare engineering and architecture teams typically need to answer:

1. What is our service decomposition boundary? Domain-driven design (DDD) bounded contexts — patient identity, clinical documentation, scheduling, billing — provide a principled basis for splitting services.
2. How do we handle distributed transactions? Patterns such as the Saga pattern replace ACID transactions across service boundaries, but require careful choreography or orchestration design.
3. How do we enforce HIPAA compliance at the API layer? An API gateway with OAuth 2.0 / SMART on FHIR scopes, combined with centralized audit logging, is a common approach.
4. What observability stack will we use? Distributed tracing (OpenTelemetry), centralized log aggregation, and per-service SLOs are essential for diagnosing failures across many services.
5. On-premise, cloud, or hybrid? The answer usually depends on data-residency requirements, existing infrastructure investment, and staff capabilities.
6. What is the migration path from our monolith? The Strangler Fig pattern — incrementally routing traffic to new microservices while leaving the monolith in place — is a widely used incremental migration strategy.

Architecture Considerations for Healthcare Microservices

FHIR-Aligned Service Design

Structuring microservices around HL7 FHIR resources (Patient, Encounter, Observation, Claim) aligns internal service boundaries with the external interoperability model, reducing the impedance mismatch when exposing public APIs to third-party apps.

Event-Driven Integration

Healthcare workflows are inherently event-driven: a lab result triggers a clinician notification; a prescription triggers a pharmacy order. An event bus (Apache Kafka, AWS EventBridge) decouples producers and consumers, improves resilience, and provides a durable audit trail — valuable for compliance.

Container Security Hardening

Healthcare microservices containers should run as non-root, use read-only file systems where possible, have minimal base images, and be scanned for CVEs as part of the CI pipeline. Runtime security tools (Falco, Aqua, Prisma Cloud) can detect anomalous behavior in production pods.

Summary

Microservices adoption in healthcare is being driven by interoperability regulation, cloud-native infrastructure maturity, and the need for faster, safer software delivery in a patient-safety-critical domain. The market spans on-premise and cloud deployments across payers, providers, clinical labs, and life science organizations globally. The primary challenges — operational complexity, security surface area, and legacy integration — are real but addressable with proven patterns (service mesh, FHIR alignment, Saga, Strangler Fig). Engineering teams evaluating this transition should prioritize clear service-boundary definition, robust observability, and a compliance-by-design approach from day one.

Why Microservices Change the Security Equation

The shift from monolithic applications to microservices architectures has fundamentally changed how software is built and deployed. Rather than tightly coupled components communicating through fixed internal channels, microservices expose discrete units of functionality through RESTful APIs. Container runtimes such as Docker and orchestration platforms such as Kubernetes have accelerated this trend by making it practical to run hundreds of loosely coupled services at scale.

From a developer perspective, this model is powerful. Versioned APIs allow new functionality to be added without rearchitecting or recompiling an existing codebase. Services remain agnostic about each other’s internal implementation details. Teams can iterate independently and release continuously.

However, scale introduces complexity. Imagine an environment with 500 RESTful APIs. A broad operational change — enabling mutual TLS (mTLS) authentication across all services, for example — or a targeted breaking change, such as renaming a frequently called service endpoint, can ripple unpredictably through the entire application. Microservices architectures are designed to contain this blast radius, but they don’t eliminate it.

What Is a Service Mesh and Why Does It Matter for Security?

A service mesh addresses the operational complexity of managing large numbers of microservices. Instead of requiring each service to implement its own traffic management, observability, and security controls, a service mesh offloads those responsibilities to a dedicated infrastructure layer.

The most common implementation uses a sidecar proxy pattern. A proxy container runs alongside each service container and intercepts all inbound and outbound traffic. Because services communicate through these proxies rather than directly with each other, the mesh layer can enforce policies — such as mTLS, rate limiting, circuit breaking, and header manipulation — without touching application code or reconfiguring individual containers.

Popular service mesh implementations include Istio, Linkerd, and Consul Connect. Each takes a slightly different approach to the control plane and sidecar proxy, but the architectural principle is consistent: decouple traffic policy from application logic.

For operations teams, this is a significant improvement in manageability. For security teams performing threat modeling, it introduces a new class of challenge: the communication path through the application is no longer static or predictable.

The Core Problem: Static Threat Models Break in Dynamic Environments

Traditional application threat modeling begins by decomposing an application into its component parts and drawing a data flow diagram (DFD) — a map of how data moves between components, trust boundaries, and external entities. From this diagram, security analysts systematically examine each interaction from an attacker’s perspective.

A common framework used in this process is STRIDE, which categorizes threats across six dimensions:

• Spoofing — impersonating a user, service, or component
• Tampering — unauthorized modification of data in transit or at rest
• Repudiation — performing actions that cannot be traced back to an actor
• Information Disclosure — exposing data to unauthorized parties
• Denial of Service — degrading or disabling service availability
• Elevation of Privilege — gaining permissions beyond what is authorized

This methodology works well when an application’s topology is relatively stable. The problem with microservices — and service mesh in particular — is that the topology is intentionally mutable. A service that is internal-only today may be exposed to external traffic tomorrow. Security controls such as mTLS may be toggled on or off at the mesh layer without any change to application code. New services are introduced continuously as part of CI/CD pipelines.

A threat model built against a single, static snapshot of a microservices application can become stale within days. Security teams need a modeling approach that is as dynamic as the environment itself.

Best Practice 1: Modularize Your Threat Modeling Process

The most effective adaptation is to modularize threat modeling to match the modular structure of the application. Rather than treating the entire application as a single unit of analysis, security teams should analyze each service independently.

There are two key reasons for this approach:

1. Services change context rapidly. A service that has no direct internet exposure today may be fronted by an API gateway tomorrow. Its threat surface changes even if its code does not. Modeling it in isolation means the security analysis remains valid regardless of how the surrounding topology changes.
2. It scales with continuous delivery. When a new service is introduced, the security team only needs to threat model that service — not re-analyze the entire application. This removes a bottleneck that would otherwise slow down DevOps pipelines.

In practice, this means assigning a threat model artifact to each API or service, documenting its inputs, outputs, trust boundaries, and applicable STRIDE threats. This artifact travels with the service through its lifecycle.

Best Practice 2: Analyze Each API in Isolation Using STRIDE

For each service or API endpoint, work through the following steps:

1. Define inputs and outputs. What data does this service accept? What does it return? What external systems or services does it call?
2. Identify trust boundaries. Does this service accept unauthenticated requests? Does it call downstream services with elevated privileges?
3. Apply STRIDE systematically. For each interaction point, work through all six threat categories. Document applicable threats and the controls (or lack thereof) that address them.
4. Document residual risk. Note threats that are accepted, transferred, or mitigated by compensating controls at the mesh layer.

The output of this process is a small, self-contained threat model for each service — a hardened specification of how an attacker might abuse that API and what defensive measures are in place. As the service evolves, the threat model is updated incrementally rather than rebuilt from scratch.

Best Practice 3: Do Not Abandon Full Application Decomposition

Analyzing services in isolation is necessary but not sufficient. Some classes of vulnerability — particularly business logic flaws — only become visible when you trace data flows across multiple services.

For example, consider a scenario where input validation is applied at Service A, but the validated output is passed to Service B, which makes authorization decisions based on that data. A flaw in the validation logic at Service A creates a downstream privilege escalation risk at Service B. Neither service appears vulnerable when analyzed in isolation.

The recommended approach is therefore additive: perform per-service threat modeling as the primary ongoing activity, and complement it with periodic full-application decomposition reviews that focus on cross-service data flows and business logic. The frequency of full-application reviews should be driven by the rate of significant architectural change.

Best Practice 4: Account for Mesh-Layer Security Controls in Your Model

Service mesh introduces a set of security controls that exist outside the application code and outside individual container configurations. When threat modeling in a service mesh environment, security teams must explicitly document which controls are enforced at the mesh layer and which are the responsibility of the application itself.

Key mesh-layer controls to document include:

• Mutual TLS (mTLS): Is mTLS enforced between all services, or is it selectively applied? What happens if the mesh configuration is changed to disable it?
• Authorization policies: Does the mesh enforce service-to-service authorization, or is access control entirely within the application?
• Rate limiting: Is rate limiting enforced at the mesh layer, at the application layer, or both?
• Observability: Are request logs, traces, and metrics captured at the mesh layer in a way that supports incident investigation?

Because these controls can be changed without touching application code, threat models must treat mesh configuration as a separate attack surface. A misconfigured or compromised control plane could disable security controls across the entire mesh simultaneously — a risk that warrants explicit treatment in any threat model.

Best Practice 5: Integrate Threat Modeling into CI/CD Pipelines

For organizations practicing continuous delivery, threat modeling should not be a one-time activity performed at project inception. Instead, it should be integrated into the development workflow so that security analysis keeps pace with code changes.

Practical integration points include:

• New service onboarding: Require a completed per-service threat model as a condition for deploying a new service to production.
• API contract changes: Trigger a threat model review when the API contract for an existing service changes — particularly when new inputs are accepted or new downstream calls are introduced.
• Mesh policy changes: Treat changes to service mesh configuration (authorization policies, mTLS settings, traffic routing rules) as requiring security review, in the same way that infrastructure-as-code changes are reviewed.

Summary

Service mesh and microservices architectures offer significant operational benefits, but they fundamentally change the assumptions on which traditional threat modeling is built. Static data flow diagrams and single-pass application decomposition are insufficient for environments where topology, security controls, and service exposure can change continuously.

The most effective adaptation is to modularize the threat modeling process to match the modular structure of the application: analyze each service independently using a structured framework such as STRIDE, maintain per-service threat model artifacts that travel with each service through its lifecycle, and complement per-service analysis with periodic full-application reviews focused on cross-service business logic. Mesh-layer security controls should be explicitly documented and treated as a distinct attack surface. Finally, threat modeling should be integrated into CI/CD workflows so that security analysis scales with the pace of delivery.

Overview

Amdocs (NASDAQ: DOX), a leading provider of software and services to communications and media companies, announced in February 2020 that Bell Canada — Canada’s largest communications company — had successfully implemented RevenueONE, a core component of Amdocs’ cloud-native customer experience suite CES20, running on Amdocs Microservices360. The deployment builds on a long-standing managed services relationship between the two companies and marks a significant step in Bell’s ongoing digital transformation strategy.

What Was Implemented and Why

Bell’s implementation centered on two key Amdocs technologies:

• RevenueONE: A billing and revenue management building block within the CES20 suite, designed to simplify retail engagement and support complex product and pricing models for communications service providers (CSPs).
• Microservices360: Amdocs’ core microservices platform that, combined with DevOps and Site Reliability Engineering (SRE) practices, is intended to shorten the code-to-production cycle and accelerate time-to-market for new products and services.

The goal was to give Bell both the technical agility to adapt to rapidly changing business conditions and the operational tooling to continuously deliver improvements to its digital retail experience for customers.

DevOps and CI/CD at the Core

A defining aspect of this implementation is its emphasis on DevOps culture and continuous integration/continuous delivery (CI/CD) methodology. Communications service providers like Bell are increasingly expected to behave like software companies — shipping features rapidly, recovering from failures quickly, and scaling infrastructure on demand.

Microservices360 is designed to serve as the enabling engine for exactly this kind of transformation:

• Microservices architecture: Breaking monolithic BSS/OSS systems into independently deployable services reduces the blast radius of changes and allows teams to deploy components on their own release cadence.
• CI/CD pipelines: Automated build, test, and deployment workflows reduce manual handoffs and enable frequent, lower-risk releases.
• Site Reliability Engineering (SRE): Applying SRE principles helps balance the pace of feature delivery against system reliability — a critical concern for a national carrier like Bell.
• Cloud-native design: CES20’s open, modular component model means Bell can adopt new capabilities incrementally without requiring a full platform replacement.

Industry Context: CSPs Transforming to DevOps Organizations

Telecommunications companies have historically operated large, tightly coupled BSS (Business Support Systems) and OSS (Operations Support Systems) stacks that are difficult and slow to modify. As competition from digital-native players intensifies and customer expectations rise — particularly in the lead-up to 5G service rollouts — CSPs like Bell face pressure to modernize at speed.

Adopting a cloud-native, microservices-based architecture directly addresses several of the most common pain points:

• IT velocity: Independent services can be updated without taking down the entire platform.
• Business agility: New product configurations, pricing models, and customer journeys can be launched faster.
• Scalability: Cloud-native workloads can scale horizontally to handle peak demand without over-provisioning permanent infrastructure.
• Vendor flexibility: Open, modular designs reduce lock-in and allow best-of-breed integrations.

Quotes from Key Stakeholders

“As the pace of digital transformation continues to accelerate, it’s critical to adopt cloud-native, microservices-based technologies and DevOps best practices and tools. Together with Amdocs, we are rapidly developing and deploying new capabilities to support Bell’s customer service leadership in a new era of integrated digital communications.”

— Saad Saade, Vice President of IT Delivery, Customer Experience, Bell Canada

“The ability to use open, modular components to enrich existing IT capabilities is a fundamental design principle of CES20 aimed at improving business agility and ensuring focus on areas of greatest business opportunity.”

— Anthony Goonetilleke, Group President, Amdocs Media, Network & Technology

Key Takeaways for DevOps Engineers

This implementation illustrates several broader principles relevant to DevOps practitioners working in or adjacent to the telecommunications industry:

1. Modernization doesn’t require a big bang. Bell extended an existing managed services relationship incrementally, adopting cloud-native capabilities as modular additions rather than replacing everything at once.
2. Platform and practice go together. Microservices360 is presented not just as infrastructure but as a combined platform-plus-methodology that bundles DevOps and SRE practices alongside the tooling.
3. SRE is becoming standard in telecoms. The explicit mention of Site Reliability Engineering alongside DevOps signals that reliability engineering is now expected even in traditionally operations-heavy industries.
4. CI/CD is the mechanism, not the goal. The pipeline exists to serve a business outcome — faster product launches and a better customer retail experience — not as an end in itself.

About Amdocs

Amdocs is a software and services company focused on the communications and media sector. As of fiscal year 2019, the company reported revenue of $4.1 billion and employed approximately 25,000 people serving customers in over 85 countries. Amdocs is listed on the NASDAQ Global Select Market (ticker: DOX). More information is available at www.amdocs.com.

This article is based on a press release issued by Amdocs on February 4, 2020, distributed via GlobeNewswire. Financial figures and employee counts reflect conditions at the time of the original announcement.

What Are Microservices and Why Do They Matter in Healthcare?

Microservices architecture is a style of software design derived from service-oriented architecture (SOA). Instead of building one large monolithic application, developers decompose functionality into small, independently deployable services. Each service owns a specific business capability — for example, patient scheduling, claims processing, or lab result retrieval — and communicates with other services through well-defined APIs.

In healthcare IT, this pattern addresses several long-standing problems: tightly coupled legacy systems, slow release cycles, and the difficulty of integrating data across providers, payers, and regulators. Because individual microservices can be deployed, scaled, or replaced without touching the rest of the application, engineering teams can ship fixes and new features far more quickly than with traditional monoliths.

Core Benefits for Healthcare Engineering Teams

• Portability and interoperability: Services built around standard APIs (REST, gRPC, HL7 FHIR) can be consumed by different front-ends — web portals, mobile apps, partner systems — without duplicating business logic.
• Accelerated time to market: Independent deployment pipelines mean a change to the billing service does not require a full regression cycle on clinical modules.
• Independent replaceability: A legacy claims-adjudication component can be rewritten and swapped out without a “big-bang” migration of the entire platform.
• Targeted scalability: High-throughput workloads such as real-time eligibility checks can be scaled horizontally without over-provisioning the entire application stack.
• Fault isolation: A failure in one service does not cascade to bring down unrelated services, improving overall system resilience.

Market Context (c. 2019–2026)

According to the Data Bridge Market Research report that prompted this article, the global microservices in healthcare market was valued at approximately USD 132 million in 2018 and was projected to reach roughly USD 644 million by 2026, representing a compound annual growth rate (CAGR) of around 21.9%. The report attributed growth primarily to demand for faster software delivery cycles and the need to integrate disparate healthcare data sources.

Reported demand-side growth drivers included rising government initiatives around health data interoperability, growing healthcare infrastructure investment, and increasing awareness of cloud-native architecture patterns among health system IT leadership.

Market Segmentation

The report segmented the market across three primary dimensions: component type, deployment model, and end-user category.

By Component

• Platforms — middleware and orchestration tooling (e.g., service meshes, API gateways, container orchestrators)
• Services
  • Consulting Services
  • Integration Services
  • Training, Support, and Maintenance Services

By Deployment Model

• Cloud-Based
  • Public Cloud
  • Private Cloud
  • Hybrid Cloud
• On-Premises

Hybrid cloud deployments are particularly relevant in healthcare, where regulatory requirements (HIPAA in the US, GDPR in Europe) may mandate that certain patient data remain on-premises or within a controlled private environment, while less sensitive workloads run on public cloud infrastructure.

By End-User

• Healthcare Providers (hospitals, clinics, physician groups)
• Healthcare Payers (insurers, managed care organizations)
• Life Sciences Organizations (pharmaceutical and medical device companies)
• Clinical Laboratories

Key Technology Vendors

The report identified the following companies as significant participants in the microservices tooling and services space relevant to healthcare:

• Amazon Web Services (AWS) — cloud infrastructure, container services (ECS, EKS), and AWS App Mesh
• Microsoft Azure — Azure Service Fabric, AKS, API Management
• IBM — hybrid cloud integration, IBM Cloud Pak for Integration
• Oracle — Oracle Cloud Infrastructure, API Gateway
• Salesforce.com — Health Cloud, platform APIs
• Pivotal Software (now VMware Tanzu) — Spring Boot/Spring Cloud microservices frameworks, Cloud Foundry
• Infosys — digital transformation and microservices consulting for healthcare clients
• Cognizant — healthcare IT services and cloud-native application modernization
• Syntel (now Atos Syntel) — application modernization services
• MuleSoft (a Salesforce company) — API-led connectivity, Anypoint Platform
• NGINX / F5 — reverse proxy, API gateway, and service mesh components
• Software AG — integration platform and API management
• CA Technologies (now Broadcom) — API management and DevOps tooling
• Datawire — Ambassador API gateway (open-source, Kubernetes-native)

Notable Vendor Developments (2018)

The original report highlighted two specific developments from late 2018:

• AWS App Mesh (November 2018): Amazon Web Services launched AWS App Mesh, a service mesh that provides application-level networking to make it easy for services to communicate with each other across multiple types of compute infrastructure. It captures metrics, logs, and traces from every microservice, helping operators monitor and control traffic between services without modifying application code.
• Infosys–Hitachi joint venture (December 2018): Infosys announced a joint venture with Hitachi, Panasonic, and Pasona to expand its presence in Japan. The arrangement was framed as accelerating business process transformation for clients in the region, though the direct healthcare-specific implications were not detailed in the source material.

Architectural Considerations for Healthcare Microservices

Implementing microservices in a healthcare context introduces challenges beyond those found in typical enterprise software projects. Engineering teams should plan for the following:

Interoperability Standards

Healthcare data exchange standards such as HL7 FHIR (Fast Healthcare Interoperability Resources) and HL7 v2 are central to any microservices design that must share clinical data across organizations. Building FHIR-compliant APIs into service contracts from the start reduces integration friction with EHR systems, payer platforms, and government registries.

Security and Compliance

Every service boundary is a potential attack surface. mTLS (mutual TLS) between services, enforced through a service mesh such as Istio or AWS App Mesh, ensures that only authorized services can communicate. OAuth 2.0 and SMART on FHIR provide authorization frameworks for patient-facing applications. Audit logging at the API gateway and at the service level is typically required for HIPAA compliance.

Data Consistency

Each microservice ideally owns its own data store (the “database per service” pattern). In healthcare, this must be balanced against the need for consistent, accurate patient records. Saga patterns and event-driven architectures using message brokers (Apache Kafka, Amazon SNS/SQS) are commonly used to maintain eventual consistency across service boundaries without introducing tight coupling.

Observability

Distributed tracing (OpenTelemetry, AWS X-Ray, Jaeger), centralized logging, and metrics aggregation are non-negotiable in production healthcare environments where a degraded service can directly affect patient care workflows. Establishing SLOs and alerting before going live is strongly recommended.

Summary

Microservices architecture offers healthcare IT organizations a practical path to modernizing legacy systems, accelerating feature delivery, and achieving the interoperability that regulators and patients increasingly demand. The technology vendor landscape is well-established, with both hyperscale cloud providers and specialist integration platforms offering mature tooling. The key challenges — security, compliance, data consistency, and observability — are solvable with established patterns, but require deliberate planning from the start of any migration effort.

For teams evaluating a microservices migration, start with a bounded, non-critical service to validate your deployment pipeline, observability stack, and security controls before decomposing core clinical or financial systems.

Open Source at a Crossroads

Open source has shaped software development for decades. Foundations like the Apache Software Foundation have shepherded projects that now underpin applications used by billions of people worldwide. But as cloud computing matured and enterprise adoption deepened, the open source movement entered a period of structural tension heading into 2020. Questions around licensing, sustainability, tooling complexity, and community governance all demanded attention.

Here are four predictions — written at the close of 2019 — that capture the key forces shaping open source at that moment, along with context that remains relevant for DevOps practitioners today.

Prediction 1: The Tension Between Cloud Providers and Open Source Projects Won’t Go Away

In 2019, several high-profile open source companies — including Confluent, Elastic, and MongoDB — revised their software licenses specifically to prevent cloud providers from commercialising their projects without contributing back to the community. The moves sparked significant debate: Were these changes philosophically consistent with open source principles? Would they discourage community involvement? Would they ultimately harm or protect the projects themselves?

The practical impact of those licensing shifts was still unclear at the time of writing. Some cloud providers responded by forking older, permissively licensed versions of the software — effectively sidestepping the new restrictions while leaving the upstream project without a revenue share. Others chose to partner with open source vendors as a competitive differentiator.

The likely outcome going into 2020 was uneven: some projects would grow despite restrictive licenses because their value proposition was strong enough. Others would find that tighter licensing dampened adoption, particularly in cloud-native contexts where developers expect low-friction access to tooling. The core conflict — between the open source ethos of free use and the commercial reality of cloud monetisation — was not going to resolve itself quickly.

Why This Still Matters

The Business Source License (BSL), Server Side Public License (SSPL), and similar instruments remain contentious in the DevOps community. Engineering teams evaluating open source components should always review license terms carefully, especially when deploying on managed cloud services, to understand whether usage restrictions apply.

Prediction 2: Kubernetes Growth Would Drive Demand for Better Open Source Tooling

By the end of 2019, Kubernetes had become the de facto standard for container orchestration. Its adoption was growing rapidly, yet it consistently outpaced the broader industry’s ability to operationalise it effectively. Kubernetes is architecturally complex — managing networking, storage, scheduling, and workload lifecycle across potentially multi-cloud clusters — and integration with existing IT operations tooling was a known friction point.

The prediction was that Kubernetes Operators — software extensions that encode operational knowledge for managing stateful or complex applications — would become increasingly important. Operators allow teams to automate tasks like database provisioning, backup, failover, and scaling using Kubernetes-native patterns. Without a growing ecosystem of well-maintained Operators, running complex workloads (particularly stateful ones) on Kubernetes would remain difficult to scale reliably.

From an open source perspective, the development and sharing of Operators within communities was identified as critical infrastructure — as important as application drivers or client libraries for enabling real-world adoption.

Practical Implication for DevOps Teams

When evaluating whether a data store or messaging system is suitable for a Kubernetes-based architecture, check whether a production-grade Operator exists and is actively maintained by the upstream project or a reputable contributor. Poorly maintained Operators can introduce operational risk that negates the benefits of containerised deployment.

Prediction 3: The Commercial Role in Open Source Communities Would Become More Transparent

There is a persistent myth that open source contributions are broadly distributed across independent volunteers. In reality, the majority of meaningful contributions to most major open source projects come from developers employed by, or directly sponsored by, companies with a strategic interest in those projects. This dynamic was expected to become more visible — and more openly discussed — in 2020.

Companies like Confluent (Kafka), DataStax (Apache Cassandra), Red Hat (Linux, Kubernetes), and Canonical (Ubuntu/Linux) invest heavily in engineering, documentation, and community management for projects they depend on commercially. That investment funds the health of those projects — but it also creates questions about governance and direction.

The healthier model, as described in the original article, involves formal governance structures that separate commercial interests from community decision-making. Apache Cassandra’s introduction of the Cassandra Enhancement Process (CEP) was cited as a constructive example: it formalises how major features are proposed, discussed, and approved, ensuring that contributors from organisations like Netflix, Apple, Instagram, and Uber — not just DataStax — have a structured voice in the project’s direction.

Beyond governance, documentation was highlighted as an underinvested area where companies can meaningfully contribute. Good documentation lowers the barrier to adoption and reduces the support burden on core maintainers.

What to Look for When Evaluating an Open Source Project

• Is there a defined governance model (foundation, steering committee, or formal RFC/CEP process)?
• Are contributions coming from multiple organisations, or is the project de facto controlled by a single vendor?
• Is documentation actively maintained and versioned alongside code releases?
• Are release decisions made transparently, with public discussion and documented rationale?

Prediction 4: Microservices Architecture Would Accelerate Open Source Adoption — Including for Databases

The microservices pattern — breaking applications into small, independently deployable services — continued to grow in 2019 and was expected to accelerate further. As services became more granular and specialised, teams needed equally specialised open source components to back them: lightweight message queues, purpose-built data stores, and well-defined APIs that could be consumed without tight coupling.

An important nuance in the original prediction: while event streaming (consuming data as it flows through a system) is valuable, it is not sufficient for all use cases. Some application functions require direct, low-latency access to stored data — a role better suited to database technology than to stream processing alone. As microservices proliferated, this was expected to drive broader adoption of open source databases designed for horizontal scalability and high availability.

The underlying logic holds: when a service needs to scale independently, its data layer must scale with it. Monolithic relational databases become bottlenecks in this model, which is why distributed open source databases have grown in relevance alongside the microservices pattern.

The Bigger Picture: Open Source and Cloud Are Inseparable — But Not Without Friction

The through-line across all four predictions is that cloud computing and open source are deeply intertwined, but that relationship is not frictionless. Cloud providers benefit enormously from open source software. Open source projects benefit from cloud-scale deployment. But the commercial and governance structures required to sustain that relationship were — and continue to be — works in progress.

For DevOps engineers, the practical takeaways are straightforward: understand the licenses of the tools you depend on, evaluate whether the Kubernetes tooling ecosystem around those tools is mature, prefer projects with transparent governance, and choose components whose data models and scaling characteristics align with a microservices architecture.

Original analysis by Patrick McFadin, VP Developer Relations, DataStax. First published on IDG Connect, December 2019.

Digital Transformation Is a Journey, Not a Destination

For banks and financial institutions, digital transformation is less a discrete project and more an ongoing evolution. Every organisation starts from a different point, and the barriers to progress are significant: institutional inertia, entrenched organisational structures, and gaps in knowledge of modern technologies like microservices, containers, and CI/CD pipelines.

Understanding why these barriers exist — and how microservices can help overcome them — is the first step toward meaningful, sustainable change.

Why Bank Technology Is Rooted in the Past

The technology infrastructure of most established banks is a product of their history. Banks grew organically along product lines — from cash and cheques to increasingly complex financial instruments — and their IT systems grew alongside them. Core banking platforms were originally designed to support physical products distributed through branch networks, and they reflect the analogue world in which they were built.

Over the decades, new channels were layered on top: telephone banking, ATMs, connected trading systems, and eventually internet and mobile banking. Each channel typically acquired its own dedicated support team, reinforcing a siloed culture that makes coordinated, cross-functional change extremely difficult. The cumulative result is a deeply conservative engineering culture that gravitates toward incremental, low-risk changes rather than first-principles architectural thinking.

The Skills Gap: A Real Barrier to Microservices Adoption

Microservices architecture is not a plug-and-play solution. Implementing it effectively requires engineers with specific skills: distributed systems design, API gateway management, container orchestration (Kubernetes, Docker), service mesh configuration, observability tooling, and DevSecOps practices. This talent pool is limited, and much of it is absorbed by technology companies that offer more competitive compensation and a stronger engineering culture than traditional financial institutions.

Banks face a compounding challenge: they need engineers who understand both modern distributed systems and complex financial domain logic. This combination is rare, and building such a team takes time and sustained investment.

Greenfield Digital Banks: A Partial Solution

Many incumbent banks have responded to these challenges by launching separate digital or direct banks on greenfield infrastructure — either as new brands or wholly owned subsidiaries. This approach buys time and delivers faster customer-facing improvements, but it has a significant limitation: greenfield digital banks rarely offer the full product depth of a traditional bank. As customer expectations rise, this product gap can become a competitive liability rather than an advantage.

Why Standing Still Is Not an Option

Banks face competitive pressure from multiple directions simultaneously:

• Traditional competitors who are further along their digital transformation journey.
• FinTechs offering targeted, API-first financial products with low overhead and high UX quality.
• Big Tech platforms — companies like Amazon, Google, and Apple — whose potential moves into retail financial services represent an existential threat to banks still running on legacy stacks.

Organisations that have not modernised their delivery capability will find it nearly impossible to respond quickly to any of these threats. The question is not whether to adopt microservices, but how to do so without disrupting existing customers or destabilising production systems.

How Continuous Delivery Changes the Software Model

Tech giants such as Amazon and Google have demonstrated that software delivery is a continuous process, not a periodic event. These organisations make thousands of production deployments per day. Each release is small, well-tested, and independently deployable — making rollbacks straightforward and blast radius manageable when issues arise.

Microservices architecture makes this model achievable for other organisations by decomposing a monolithic application into independently deployable services. Each service:

• Has a clearly defined, bounded responsibility.
• Exposes a versioned API contract.
• Can be deployed, scaled, and updated independently of other services.
• Can be owned end-to-end by a small, cross-functional product team.

The result is a dramatically shorter feedback loop between engineering and business value: features reach customers faster, defects are caught earlier, and the platform becomes more stable over time — not less stable, as the traditional view of frequent releases would suggest.

DevOps as a Business Function, Not an IT Silo

Agile principles and continuous delivery are not purely technical concerns. They represent a cultural shift: engineering teams move from being a cost centre within an IT department to being embedded in the business as a driver of commercial outcomes.

In a mature microservices organisation, a DevOps team:

• Works directly with product owners and business stakeholders to define and prioritise delivery.
• Owns the full lifecycle of a service — from design through operation.
• Uses tooling for version control, automated testing, deployment pipelines, and observability to enforce safety at speed.
• Ships to production with zero downtime using techniques such as blue-green deployments, canary releases, and feature flags.

This model fundamentally contradicts the traditional assumption that frequent releases and platform stability are in tension. With the right architecture and tooling, they are complementary.

The Compounding Benefits of a Microservices Architecture

Once the initial investment in decomposition and tooling is made, microservices create a compounding cycle of benefits:

• Reduced coupling: Services have fewer dependencies on one another, so changes in one area do not cascade unpredictably into others.
• Independent scaling: High-traffic services can be scaled horizontally without scaling the entire application.
• Faster innovation: New capabilities — including AI-driven personalisation and real-time analytics — can be integrated as discrete services without rebuilding core systems.
• Improved resilience: A failure in one service does not necessarily bring down the entire platform, provided services are designed with circuit breakers and graceful degradation in mind.
• Talent attraction: Engineers prefer working on modern, well-structured codebases with clear ownership and frequent deployments.

The ability to incorporate technologies such as machine learning models for customer behaviour analysis, fraud detection, or personalised product recommendations becomes significantly more practical when these capabilities can be delivered as independently deployable services.

Migration Strategy: Timing and Prioritisation Matter

A full big-bang migration from monolith to microservices is rarely advisable, and for most banks it is not feasible. A more pragmatic approach involves:

1. Identifying bounded contexts within the existing monolith — areas of functionality with clear domain boundaries that can be extracted as services.
2. Prioritising by business value — targeting the domains where faster, more frequent releases will have the greatest commercial impact.
3. Applying the Strangler Fig pattern — incrementally routing traffic to new microservices while the monolith shrinks over time, rather than replacing it wholesale.
4. Building delivery infrastructure in parallel — CI/CD pipelines, container orchestration, observability, and API gateway configuration must be in place before microservices can be deployed safely at scale.

The migration timeline will differ for every institution. Not every application is a good candidate for microservices decomposition, and forcing the pattern onto the wrong problem can create unnecessary complexity. The goal is to identify where microservices deliver genuine business value, not to adopt the architecture as an end in itself.

Preserving What Makes the Bank Unique

Technology transformation must not erase what differentiates one bank from another. Product richness, relationship history, regulatory expertise, and brand trust are competitive assets that have been built over decades. A well-executed microservices migration builds on these strengths, exposing them through modern channels and APIs, rather than replacing them with generic commodity components.

Critically, changes must be made in a way that is invisible to customers. Downtime, degraded functionality, or data inconsistency during migration will erode the trust that the transformation is intended to protect. Zero-downtime deployment strategies, thorough automated testing, and robust rollback procedures are non-negotiable requirements.

Key Takeaways

• Legacy banking infrastructure is the product of decades of organic growth, and it was never designed for the pace of change that modern markets demand.
• Microservices architecture, combined with continuous delivery and DevOps practices, enables banks to ship smaller, safer changes far more frequently.
• The skills required to execute this transition are scarce; building the right team is as important as choosing the right technology.
• A phased migration strategy — using patterns like Strangler Fig and starting with high-value bounded contexts — reduces risk and demonstrates early business value.
• The end goal is not just technical modernisation: it is a more agile, customer-centric organisation capable of anticipating and responding to market change.

Overview

Portshift announced an addition to its Cloud Workload Protection Platform: a Policy Advisor control interface designed to help organizations identify, enforce, and manage secure communication paths between container-based microservices. The feature extends the platform’s existing zero-trust model — which works by assigning cryptographic identities to applications — into the microservices layer running on Kubernetes clusters.

What Is the Portshift Policy Advisor?

The Policy Advisor is a control interface that enforces a default-deny posture for microservice-to-microservice communication. According to Zohar Kaufman, Portshift’s VP of Research and Development, any communication between microservices that is not explicitly permitted by the Policy Advisor is automatically blocked. This aligns with a strict zero-trust networking model: nothing is trusted by default, even traffic that originates inside the cluster.

The Policy Advisor is activated by installing a lightweight agent on a Kubernetes cluster. Once in place, it surfaces suggested policies based on observed traffic patterns, allowing administrators to act on recommendations rather than building rules entirely from scratch.

How It Integrates with CI/CD and Kubernetes

The broader Portshift platform attaches identities to applications at build time, as code moves through a CI/CD pipeline. The Policy Advisor builds on this foundation by extending identity-based access control to individual microservices at runtime.

A notable usability feature is the use of plain-English policy commands. When a DevOps team defines a rule using this interface, the platform automatically translates it into the appropriate YAML configuration that governs the Kubernetes environment. This deterministic mapping has two practical benefits:

• Security professionals can participate in policy creation without needing deep Kubernetes or coding expertise.
• The intent of a policy is preserved clearly from human-readable definition to machine-enforceable configuration, reducing the chance of misconfiguration.

Policy Creation Workflow

The Policy Advisor gives administrators two paths for creating rules:

1. Suggested rules: The platform surfaces recommendations based on observed traffic. Each suggestion shows the source service, destination service, traffic details, and pending actions.
2. Custom rules: Administrators can define rules manually to meet specific runtime requirements not covered by automated suggestions.

Before a rule is applied, the Policy Advisor shows how many existing connections would be affected, helping teams avoid unintended disruptions. Once a rule is saved, it appears in the connection policy window and can be modified or deleted at any time.

Why Microservice Security Is a Hard Problem

Containerized, microservices-based applications offer real security advantages over monolithic architectures. When a vulnerability is found in a single container, developers can replace just that component rather than patching an entire application. However, this architectural model introduces its own risks:

• A single production application may be composed of hundreds of microservices, dramatically expanding the attack surface.
• Any one of those microservices could potentially communicate with a malicious external endpoint that appears legitimate — an avenue that cybercriminals can exploit.
• Lack of visibility into inter-service communication makes it difficult for security teams to evaluate and approve applications for production deployment.

This visibility gap has historically created friction between development teams and security teams. Developers want to ship; security teams are reluctant to approve deployments they cannot fully inspect. Tools like Policy Advisor aim to close this gap by giving both teams a shared, observable policy layer.

The Broader Zero-Trust Microservices Landscape

Portshift was not the only vendor pursuing zero-trust security for microservices at the time of this announcement. The problem space — enforcing least-privilege communication between services in dynamic container environments — attracted multiple startups and established security vendors. Organizations scaling their use of Kubernetes and cloud-native architectures should evaluate how their security toolchain handles:

• Service identity and authentication
• Automated policy generation from observed traffic
• Integration with CI/CD pipelines (shift-left security)
• Runtime enforcement and alerting
• Policy-as-code with auditability

Practical Takeaway for DevSecOps Teams

For teams operating microservices at scale, the key lesson from the Portshift approach is that zero-trust is not just a network perimeter concern — it applies between services inside your own cluster. Adopting a default-deny posture and enforcing explicit allow rules for service communication is increasingly considered a baseline security requirement for production Kubernetes environments.

Developers working in organizations with strong security review gates will benefit from proactively demonstrating how inter-service communication is controlled and audited. Platforms that make these policies visible and manageable in plain language help bridge the gap between development velocity and security assurance.

Note: Portshift was subsequently acquired by Cisco. The Policy Advisor feature described here reflects the product as it existed in September 2019. Readers should consult current Cisco documentation for up-to-date product information.