What is WebSocket?

WebSocket is a web communication protocol standardized as RFC 6455 by the IETF in December 2011, enabling persistent, full-duplex communication over a single TCP connection (docs.spring.io). Unlike HTTP’s half-duplex, request-response model, WebSocket begins with an HTTP handshake that is “upgraded” to a custom framing protocol, allowing both client and server to send messages at any time, bypassing the need for repeated HTTP connections (pages.ably.com). Due to its compatibility with ports 80 and 443 and seamless operation through proxies, it’s well suited for modern web environments and supports secure communications via wss:// (developer.mozilla.org).

One of WebSocket’s core strengths lies in its low latency and minimal overhead. Once the handshake is complete, the connection remains alive, eliminating repetitive TCP handshakes, SSL negotiations, and HTTP headers in subsequent messages. This efficiency makes it ideal for interactive, real-time user experiences.

Major Use Cases of WebSocket

WebSocket powers a wide array of real-time, bidirectional applications, fundamentally transforming digital interactions:

Chat and Messaging Apps

Modern chat applications—such as Slack, WhatsApp Web, and in-game messaging—rely on WebSocket to immediately transmit messages between clients and servers without delay (linkedin.com).

Collaborative Editing Tools

Platforms enabling real-time collaboration, like Figma or Google Docs, use WebSocket to synchronize edits, cursor positions, and selections among multiple users simultaneously (developer.mozilla.org).

Live Dashboards and Analytics

Financial dashboards streaming constant market data, IoT sensor networks, and monitoring systems frequently use WebSocket to push updates instantly, supporting timely decision-making .

Online Gaming and Immersive Experiences

Real-time multiplayer games demand sub-100ms latency to maintain game state consistency, a use case where WebSocket excels .

IoT Device Control and Monitoring

For devices that stream sensor data or require immediate remote control, WebSocket enables efficient bidirectional communication where every message counts.

Notifications and Streaming Updates

Real-time updates—such as stock tickers, sports scores, and social alerts—are delivered seamlessly over WebSocket, eliminating the need for inefficient polling mechanisms .

How WebSocket Works: Architecture & Protocol

The elegance of WebSocket lies in its structured yet flexible architecture:

a. Handshake and Protocol Upgrade

Communication starts with a standard HTTP GET request containing headers like Connection: Upgrade, Upgrade: websocket, and Sec‑WebSocket‑Key. The server responds with a 101 Switching Protocols, validating the request, returning a Sec‑WebSocket‑Accept key, and finalizing the switch to WebSocket (datatracker.ietf.org, pubnub.com).

b. Persistent TCP Connection and Full‑Duplex Communication

Post-handshake, a single TCP connection remains open. Either party can send framed messages independently, enabling true real-time interaction without the overhead of new connection setups (linkedin.com).

c. Frame Structure, Masking & Control Frames

WebSocket messages are structured in frames, each with fields for a FIN bit, opcode (text, binary, ping, pong, close), payload length (7–64 bits), and optional masking. Clients must mask payloads with a 4-byte key, ensuring intermediary proxies cannot misinterpret the data (en.wikipedia.org). Control frames include ping and pong for heartbeating, and close codes like 1000 (normal) and 1006 (abnormal closure) to manage connection health (medium.com).

d. API and Browser Support

The WebSocket API—exposed via new WebSocket(url) in browsers—supports event-based interaction using onopen, onmessage, onerror, and onclose. Advanced stream control is possible with the WebSocketStream API, while emerging standards like WebTransport add features like unordered delivery and datagrams that WebSocket lacks (developer.mozilla.org).

Basic Workflow of WebSocket

1. Connection Initiation:
   Client sends HTTP handshake.
2. Handshake Completion:
   Server responds with 101, switches protocol.
3. Connection Open:
   readyState transitions to OPEN, enabling bidirectional message exchange.
4. Message Exchange:
   Data is sent using ws.send() and received via onmessage, in frames that may carry text, binary, or control signals.
5. Keepalive & Health Checks:
   ping/pong control frames are used to maintain the connection, especially through firewalls or proxies (medium.com).
6. Graceful Closure:
   Either client or server sends a close frame. Upon reply, the connection is terminated cleanly, ensuring no message loss.

Step-by-Step Getting Started Guide

✅ Step 1: Initialize Node.js Project

npm init -y
npm install ws

✅ Step 2: Implement WebSocket Server

server.js

const WebSocket = require('ws');
const wss = new WebSocket.Server({ port: 8080 });

wss.on('connection', ws => {
  console.log('Client connected');

  ws.on('message', msg => {
    console.log('Received:', msg);
    ws.send(`Echo: ${msg}`);
  });

  ws.on('close', () => console.log('Client disconnected'));
});

Run: node server.js

✅ Step 3: Build Web Client (index.html)

<!DOCTYPE html>
<html>
<head><title>WebSocket Demo</title></head>
<body>
  <input id="msg" placeholder="Type message" />
  <button onclick="send()">Send</button>
  <div id="log"></div>

  <script>
    const ws = new WebSocket('ws://localhost:8080');
    ws.onopen = () => log('Connected');
    ws.onmessage = e => log('Server: ' + e.data);
    ws.onclose = () => log('Connection closed');
    function send() {
      const msg = document.getElementById('msg').value;
      ws.send(msg);
      log('You: ' + msg);
    }
    function log(text) {
      const div = document.getElementById('log');
      div.innerHTML += `<p>${text}</p>`;
    }
  </script>
</body>
</html>

Open in a browser and test sending messages in real time.

✅ Step 4: Add Enhancements

• Secure with wss:// and SSL/TLS
• Automate reconnections with exponential backoff
• Monitor and manage heartbeats, buffer sizes, and idle sessions
• Scale using load balancers like NGINX or HAProxy with sticky sessions, horizontal clustering, and Pub/Sub mechanisms such as Redis (linkedin.com).

Best Practices & Production Considerations

WebSocket deployments require thoughtful architecture to ensure efficiency, security, and scalability:

• Security: Validate Origin headers, use TLS encryption, and implement token or cookie authentication to prevent CSWSH attacks (linkedin.com, en.wikipedia.org).
• Scaling: Use sticky sessions, load-balanced deployment, and Pub/Sub for synchronization across instances (medium.com).
• Resource Management: Regularly clean up idle connections, monitor heartbeat frequency, implement backpressure, and avoid buffer saturation (dev.to).
• Data Efficiency: Use the permessage-deflate extension for large payload compression.
• Fallback Mechanisms: Integrate libraries like Socket.IO to support automatic fallback to HTTP polling or SSE where WebSocket isn’t available (en.wikipedia.org, interactiveimmersive.io).

Horw, Switzerland — Toradex is extending its Torizon operating system for use as a full IoT development and operations (DevOps) platform for Linux devices. The operating system will now include free remotely hosted updates, device monitoring features, and a fleet management solution.

Torizon enables modern, iterative product development by seamlessly integrating hardware, a Linux OS, development tools, remote updates and fleet operations. The result is a scalable solution for devices requiring high reliability and security.

In the ‘Smarter Products Need Smarter Development’ paper from May 2021, Forrester Consulting found that traditional embedded development practices are insufficient when building smart, digital products. They also reported that that 79% of decision-makers say challenges with smart product development and delivery negatively impact their firms in delivering digital products. (*Footnote: Smarter Products Need Smarter Development)

Torizon brings state-of-the-art product development principles to demanding applications in such industries as industrial automation, healthcare, smart cities and transportation. Torizon allows businesses to ship products quickly, continuously improve the user experience, fix bugs, and patch security vulnerabilities securely and robustly.

Software developers can take advantage of TorizonCore, a ready-to-use Linux distribution that eliminates the complexity of maintaining a custom-built embedded Linux distribution. Smart options allow developers to tailor TorizonCore to accommodate purpose-built hardware and special product needs. Torizon provides optional integration with modern tools, such as Visual Studio and Visual Studio Code, to speed up development.

Today, Toradex is announcing the extension of the Torizon platform to support not only development for IoT devices, but also the operation and maintenance of these devices. Torizon is seamlessly integrated with the latest Computer on Modules featuring NXP® i.MX 8, i.MX 7 and i.MX 6. Tight hardware and software integration offers excellent quality control and speeds up product development.

Torizon reduces the risk of device recalls: The device monitoring feature facilitates early detection of potential issues, and the upcoming remote access feature makes it possible to address those issues before they lead to device failures. Torizon also makes new business models possible, since businesses can offer software and features as paid add-ons, allow customers to monitor device fleets, and much more.

Toradex’s Computer on Modules and Linux offerings are proven in thousands of applications, many of which require extreme reliability. Torizon targets these same applications, and to meet these challenging requirements, Torizon offers a wide range of security and reliability features.

Toradex:
Toradex specializes in embedded hardware and software, offering Arm®-based system-on-modules (SoMs), customized single-board computers (SBCs), and industrial-grade software. Toradex’s software offerings include Torizon, an easy-to-use, open-source embedded Linux distribution.

Toradex’s offerings are an ideal fit in fields like healthcare, transportation, industrial automation, testing and measurement, and smart cities. Its off-the-shelf, commercial SoMs for embedded computing are easy to use and pin-compatible for improved scalability. They also come with free premium support and long-term product availability. Through innovation, Toradex performs market-leading integration of hardware, software and services to reduce complexity, time-to-market, risk and costs for its customers.

Founded in 2003 and headquartered in Horw, Switzerland, the company’s network stretches across the globe, with offices in the U.S., Vietnam, China, India, Japan and Brazil

SAN FRANCISCO–(BUSINESS WIRE)–Element, a leading software provider in IT/OT data management for industrial companies, today announced a new offering featuring an API integration between its Element Unify product and AWS IoT SiteWise, a managed service from Amazon Web Services, Inc. (AWS), that makes it easy to collect, store, organize, and monitor data from industrial equipment at scale. The API integration is designed to give customers the ability to centralize plant data model integration and metadata management, enabling data to be ingested into AWS services, including AWS IoT SiteWise and Amazon Simple Storage Service (S3) industrial data lake.

Available in AWS Marketplace, the Element Unify AWS IoT Sitewise API integration is designed to allow engineers and operators to monitor operations across facilities, quickly compute performance metrics, create applications that analyze industrial equipment data to prevent costly equipment issues, and reduce gaps in production.

“We are looking forward to bridging the on-premises data models we’ve built for systems like OSIsoft PI to AWS for equipment data monitoring using Element Unify,” said Philipp Frenzel, Head of Competence Center Digital Services at Covestro.

Element also announced its ISO 27001 certification proving both the security controls protect customer data and the Information Security Management System (ISMS) provide governance, risk management, and controls required for modern SaaS applications. Element Unify also supports AWS PrivateLink to provide an additional level of network security and control for customers.

“Our customers are looking for solutions that can help them improve equipment uptime, avoid revenue loss, cut O&M costs, and improve safety,” said Prabal Acharyya, Global Head of IoT Partners for Energy at AWS. “Now, the Industrial Machine Connectivity (IMC) on AWS initiative, along with Element Unify, makes possible a seamless API integration of both real-time and asset context OT data from multiple systems into an industrial data lake on AWS.”

Industrial customers need the ability to digitally transform to maximize productivity and asset availability, and lower costs in order to remain competitive. Element Unify, which aligns IT and operational technology (OT) around contextualized industrial IoT data, delivers key enterprise integration and governance capabilities to industrials. This provides them with rich insight, enabling smarter, more efficient operations. By integrating previously siloed and critical time-series metadata generated by sensors across industrial operations with established IT systems, such as Enterprise Asset Management, IT and OT teams can now easily work together using context data for their entire industrial IoT environment.

“The tie-up between Element and AWS IoT SiteWise not only enhances AWS’s ability to manage IT and OT data together in an industrial data lake, but further demonstrates Element’s leading DataOps infrastructure solution for industrial companies,” said Joe Perino, Principal Analyst for LNS Research.

Though the API integration, AWS IoT SiteWise customers can now:

Centralize plant data model integration and metadata management into a single contextual service for consumption by AWS IoT SiteWise.
Ingest data directly into AWS services, including AWS IoT SiteWise and Amazon S3 data lake.
Integrate and contextualize metadata from IT/OT sources in single-site, multi-site, and multi-instance deployments, and deploy the data model(s) to AWS IoT SiteWise.
Enable metadata to be imported into AWS IoT SiteWise from the customers systems through Inductive Automation Ignition and PTC KEPServerEX to create context data.
Keep both greenfield and brownfield AWS IoT SiteWise asset models and asset hierarchies updated as the Element Unify models adapt to changes in the underlying data.
Close the gap between raw, siloed, and disorganized IT/OT data and enriched, contextualized, and structured data that can be easily paired with business intelligence (BI), analytical tools, and condition monitoring tools like AWS IoT SiteWise.
Empower the user to build complex asset data models with ease.
Build and deploy data models to AWS IoT SiteWise and keep them up to date with changes happening in source systems.
Easily create the underlying data models to power AWS IoT SiteWise and many other analytical systems including Amazon SageMaker, Amazon QuickSight, and Amazon Lookout for Equipment.
“Operations data must be easy to use and understand in both the plant and in the boardroom. Industrial organizations who transform their plant data into context data for use in AWS IoT SiteWise, and other AWS services, will drive greater operational insights and achieve breakthrough business outcomes,” said Andy Bane, CEO of Element.

Element Analytics

Element is a leading software provider in IT/OT data management, digitally enabling operations for industrial organizations. Element Unify transforms siloed IT/OT data into context data by enabling contextualize once, compute anywhere, consume everywhere data that is used to achieve cleaner, safer, healthier, more profitable operations. Element’s customers represent over $750 billion in revenue, $500 billion in fixed assets and 450,000 employees.

A lot of noise and misunderstanding exists in the user marketplace about what digital transformation is, and the roles that artificial intelligence (AI), machine learning (ML) and the Internet of Things (IoT) play in implementing it.

Digital transformation is of major importance to businesses and organizations. Alphabet, a driving force in the digital marketplace, reported record profits in the second fiscal quarter of 2021 based on very strong digital ad sales, internet searches, and the growth of cloud services. Many companies have recognized the importance of digital transformation as a key strategy to gain a competitive edge in the marketplace.

Digital Transformation

Digital transformation is a process that has been ongoing for decades whether businesses have focused on it as a paradigm shift in the way business is conducted or not. Email, ERP platforms, cloud computing, digital advertising, online retailing, telehealth, working remote and video streaming are a few examples of businesses and processes that have undergone digital transformation. All these changes were made possible by transformation to digital processes.

Businesses and organizations have begun to realize that digital transformation can be planned and implemented proactively to be more competitive, enhance customer experiences, and improve processes that are customer touch points. AI and ML tools and the IoT channel are readily available for the planning and implementation of a proactive digital transformation strategy.

Role of AI and ML

AI and ML are tools that can be used to plan and implement a digital transformation that focuses on what is needed to gain a competitive edge with customers now and in the future. They help companies develop comprehensive strategies for data transformation based on real-time market intelligence rather than piece-meal tactics of disjointed updating of systems and processes.

ML uses big data to analyze and correlate patterns which can offer insights into customer behavior and other events. It provides insights on solutions to improve business processes and customer experiences. Instead of finding out about a change in customer behavior after sales have already declined, ML can help to identify shifts in behavior and events as they happen so solutions can be devised to head off a decline in sales.

AI uses algorithms to create or adjust programs to take advantage of the insights gained with ML. AI mimics human intelligence to identify and react to behavior and events. Programs based on insights to behavior and events from ML can improve customer experiences and business processes.

Using AI and ML in planning and implementing a digital transformation puts businesses and organizations in position to stay ahead of the curve. With AI and ML information is analyzed in real-time to increase responsiveness to changes in customer behavior and events, and make business processes including IT operations more efficient.

Digital transformation directed and driven by AI and ML enables the gathering and analysis of data to better understand customers to help accelerate introduction of new products and services.

With AI and ML data transformation has become a constantly evolving process, providing insights and responses to the ever changing dynamics of customers and the marketplace.

Role of IoT

The IoT is the network of objects that have software, sensors, smart devices and other instruments incorporated in them to link and exchange data with other systems and devices over the internet. It is the channel for gathering data used in AI and ML, and for interacting with customers by way of the “things” in IoT to improve customer experiences and business processes. The internet and all the things it connects provides the ability to implement digital transformation. IoT is the conduit that makes data-driven, intelligent data transformation possible.

Businesses and organizations face two choices when they contemplate digital transformation. They can make decisions on digital transformation that are not coordinated or supported by market data, or base digital transformation strategies on the constantly evolving real-time data gathered via the IoT and analyzed and responded to with AI and ML. The organizations that choose the latter approach will gain competitive advantage in the marketplace.

Open source has permanently altered how we work and conduct business. Linux, and thousands of other open source projects and open standards, now dominate the software landscape, supporting emerging technologies like artificial intelligence and edge computing. Without open source, the DevOps tooling ecosystem would lack Docker, Kubernetes, service mesh, popular databases, and many CI/CD tools used by DevOps teams daily.

Now, more than ever, companies are searching for ways to meet escalating user demands. The pandemic has amplified this urgency, accelerating existing digital transformation initiatives. Arguably, open source is the engine that now powers the digital innovation required to compete. Leveraging open source could also help check rising cloud costs. With proprietary solutions falling out of favor, many enterprise leaders are adopting open source alternatives for infrastructure modernization and application development.

Red Hat recently released its third annual State of Enterprise Open Source report. The report surveyed 1,250 IT leaders worldwide on their open source adoption and usage patterns. The findings validate what most of us already know — open source is now fundamental to most technology stacks. Without it, most development projects would fall flat in an instant.

Open Source Use Cases
Open source is at the heart of modern software development. According to the report, 90% of enterprise IT leaders are using open source. So, where is open source being applied? Well, 64% of enterprises use open source software for IT infrastructure modernization. The report surmised that this could be due to Linux and open source infrastructure replacing proprietary systems.

The report also found 54% of enterprises use open source for application development. This is no surprise, as engineers routinely adopt open source libraries and packages to construct B2B and user-facing experiences. In a digital-only environment, applications are the lifeblood for many businesses, and open source is critical scaffolding.

The third top use case for enterprise open source is digital transformation, at 53%. This figure has increased by 11 percentage points in the last two years alone. Open source will likely continue to replace non-digital or manual processes with software-based, automated processes.

In the enterprise, open source is used in networking, adopted by 54% of enterprises, databases, at 53% and security, at 52%. Other areas mentioned in the report include big data analytics and cloud management tools.

In recent years, multi-cloud has become a notable trend. The idea is that organizations will seek out multiple cloud vendors to support different use cases. The Red Hat report confirmed that hypothesis, finding that 69% of respondents prefer to use multiple vendors for their cloud. As hybrid, multi-cloud setups profligate, cloud expertise and smart multi-cloud economic practices continue to gain importance.

Containers and K8s
Open source components are the foundation for containers and container management utilities. As reliance on containers grows, so will the dependence on open source technology. The report found just under half of enterprises now use containers in production. An additional 37% also use containers for development-only purposes.

To orchestrate these containers, IT teams have coalesced around Kubernetes. Sixty-six percent of respondents view Kubernetes as very or extremely important. As Kubernetes becomes ubiquitous, growth in container usage is slated to continue — about one in three respondents reported their container usage will increase significantly in the next 12 months.

“Kubernetes has become the de facto standard for Linux container development,” said Michael Hinterland, team lead, ICS cloud & automation and ICS system & middleware, Porsche Informatik.

While Kubernetes is flourishing, there are certainly variances across industries. For example, 62% of financial services and telecommunications respondents adopted containers in production, while other sectors, such as health care and retail, saw a lower adoption of containers in production, at 47% and 50%, respectively. The highly-regulated financial services sector also scored high on Database DevOps, a separate report recently concluded.

Open Source Benefits and Barriers
There are numerous benefits to using enterprise open source software. For one, a commitment to open source brings transparency and collaboration improvements. Respondents also cited higher quality software, access to the latest innovations and improved security as being among the top benefits to adopting open source software.

But open source has its drawbacks, too; 42% of tech leaders reported a lack of support as a top hurdle to enterprise open source adoption. This checks out – community-driven projects don’t typically offer traditional support systems. Without accessible frameworks for learning, creating a Center of Excellence at scale becomes difficult.

Other top barriers to enterprise open source adoption include compatibility, code security and a lack of internal skills. Introducing open source within an enterprise means upskilling the workforce and encouraging secure-by-default settings to avoid costly misconfigurations. It could also mean working with trusted allies that further open source initiatives, the report recommended.

Open Source Fuels Emerging Tech
“Open source has solidified itself as an innovation engine for the software industry,” wrote Paul Cormier, president and CEO, Red Hat.

Indeed, open source is at the heart of most cutting-edge emerging technologies. For example, 55% of respondents report using enterprise open source in edge computing and internet of things (IoT) settings. Simultaneously, 48% of respondents use open source for artificial intelligence and machine learning (AI/ML). Amid open source projects like OpenAI and GPT–3, AI is eating the software world, Forbes reported.

Both categories are slated to increase by nearly 20 percentage points in the next two years, whereas the use of proprietary solutions for AI/ML will decrease. Open source will continue to affect buying decisions, as 83% of IT leaders are more likely to choose a vendor that has contributed to the open source community.

Within a COVID-19-influenced world, “the problems are too big for one person, one company or one organization to solve,” said Cormier. “But it’s in moments like this where open source truly shows its power.”

Despite the unrelenting turmoil currently defining the year 2020, the software development industry has collectively continued to make significant progress. As CDC-recommended social distancing guidelines force industries to adapt to these unprecedented times, companies’ reliance on SaaS programs continues to grow, allowing software industry leaders to capitalize on business owners’ shifting needs.

Late in 2019, experts made bold predictions regarding the software industry’s future in 2020, without any knowledge of a global pandemic lurking around the corner.

Calls for increased digital transformation related to A.I. and machine learning
Increased development of software directed at automated processes
Focus on time-stamping of all data
More focus on voice and speech recognition software
During 2020, some of these trends came to fruition, while a host of other trends appeared on the software development horizon. Some of the trends that came into focus during 2020 include:

Increased spending on A.I. technology (hardware and software)
Increased focus on blockchain technology as a means of tracking data
The development of software to assist in the capturing of big data
The transformation of the entertainment industry by combining AR (Augmented Reality) and VR (Virtual Reality) technologies
A more concentrated focus on continuous integration + continuous deployment (CI/CD) to aid in faster dispersion of mobile app software
Introduction of Internet of Things (IoT) technology guided by software and devices
With the pandemic’s end nowhere in sight, software industry leaders must step up to the plate and respond to the COVID-19 era’s debilitating uncertainty with resilience. As software developers nationwide adopt an out-with-the-old-and-in-with-the-new mindset, software developers should strive to remain competitive in the industry. How can developers attain that competitive edge? By tracking these expert-predicted software development trends of 2021. Additionally, those in the tech sector should harness the power of a reliable software QA company.

Before software hits the marketplace, there needs to be absolute assurance the software will perform at optimal efficiency. An unwavering dedication to QA (Quality Assurance) and QC (Quality Control) is critical in maximizing company time and resources. After all, a well-oiled quality assurance strategy will help save time and money, protect the software company’s reputation, and confirm that necessary security features are in place.

Software trends to watch for in 2021
As we stumble into the year 2021, there’s no reason to expect that software advancements will wind to a halt. With that in mind, here are ten must-watch trends to look for as we ring in the new year.

Native app development
With people living their lives and earning a paycheck through their mobile devices, software developers will strive to create native apps specifically designed for the iOS and Android operating systems.

The Internet Of Things (IoT)
In 2021, experts expect that the demand for IoT hardware (devices), software, system integration capabilities, and data and telecom services will continue to surge.

Big data computation
Software platforms that perform calculations will garner more attention, as they’ll be able to complete real-time calculations on-demand without resorting to data storage.

Human Augmentation
The world can expect to see further advancements in human augmentation software and devices. These advancements’ primary focus will be on improving bodily functions such as seeing and hearing.

Artificial Intelligence (AI) expansion
The AI development train will continue rolling as more industries look to increase productivity and improve the customer experience through machine learning and speech processing.

Introduction of progressive web apps
With users moving away from websites and dedicating concentrated focus on mobile apps, software developers will continue to develop progressive web apps that are easier to access.

Continuous Integration and Deployment (CI/CD)
CI/CD technology will allow software developers to fix bugs and have the fixes ready in real-time. That way, developers can avoid productivity-stalling and sales-stifling updates.

Low-code development platform (LCDP)
LCDP is a trend that will allow software developers to address complicated programming issues in the production of simpler software interfaces.

Cross-platform and hybrid development
While experts anticipate that native apps will receive more attention in the software industry, that’s not to imply that hybrid platforms will be laid to rest. Some industries will still need the flexibility granted by having access to hybrid platforms, as these hybrid platforms will allow business owners to conserve time and financial resources.

SQL Databases
Moving forward, more companies will begin to accept that SQL databases are essential for the development of business applications.

Final word
Much like in other neighboring industries, industry leaders should avoid stagnation at all costs. By tuning into upcoming trends, business owners can outrank their competitors and stay relevant among their target audience.

SEATTLE–(BUSINESS WIRE)–Today, Amazon Web Services, Inc. (AWS), company (NASDAQ: AMZN), and Standard Chartered Bank plc (LON: STAN), a leading international banking group, announced a five year, strategic global agreement, which deepens the existing relationship between the two companies, and includes the bank running its strategic banking systems and customer facing applications on AWS. Standard Chartered Bank is collaborating with AWS to drive its digital transformation and deliver new personalized banking services in the bank’s 60 markets worldwide. Using AWS’s reliable infrastructure and cloud services across its entire business will enable the bank to be more responsive to customer needs and create new applications. Standard Chartered will adopt AWS to improve resiliency, security, and privacy, while meeting compliance requirements across the bank’s global footprint.

Adopting a cloud-first approach, Standard Chartered will use the breadth and depth of AWS services, including database, containers, compute, networking, storage, and security, to reinvent the digital banking experience, accelerating the design and deployment of new applications and digital services for its individual and corporate clients. Standard Chartered is using Amazon Elastic Kubernetes Service (Amazon EKS) to run significant applications and quickly introduce new services with the utmost security, reliability, and scalability. For example, the bank launched its nexus banking-as-a service solution and Mox, its new virtual bank in Hong Kong, on AWS and continues to leverage AWS services to add new functionality to both. The bank’s award-winning global payments system, SC Pay, and core banking system, eBBS, are cloud-native services that use Amazon Aurora, a cloud-native relational database, to store details of banking and e-commerce transactions, including micropayments, resulting in faster and more secure transfer of funds with reduced cost per transaction. Standard Chartered’s Financial Market business, which includes risk management, financing, and investment services, uses AWS to run algorithms that assess market risk and leverages Amazon Elastic Compute Cloud (Amazon EC2) to enable the bank to flexibly scale up those workloads during peak demand.

“Our continued work with AWS demonstrates our commitment to putting innovation and security at the heart of Standard Chartered’s digital transformation strategy,” said Dr. Michael Gorriz, Group Chief Information Officer of Standard Chartered. “A cloud-first strategy allows us to be more agile and client-focused so our customers have better experiences and faster access to innovative new products. At the same time, we are improving our operational efficiency and resilience by using the best-in-class security, privacy, and compliance delivered through cloud infrastructure. We are pleased to collaborate with AWS as a long-term strategic cloud provider and take advantage of their broad portfolio of cloud services and proven expertise to bring secure cloud solutions to the financial services industry.”

“Adopting a cloud-first approach makes our vision for next generation financial services – like virtual banking, next-generation payments, open banking, and banking as a service – a reality,” said Bhupendra Warathe, Chief Technology Officer, Cloud Transformation, Standard Chartered. “A significant number of Standard Chartered’s flagship applications, like our core banking system, eBBS, global payment system, SC Pay, as well as our new digital banking services, Mox and nexus, are already cloud-native. We look forward to our continued partnership with AWS to deliver new products and solutions to our clients across the 60 markets we serve.”

“Standard Chartered Bank has been a trusted financial institution for the last 160 years and we are pleased that they have selected AWS as a strategic cloud provider to drive their digital transformation, while operating more efficiently and securely in the cloud,” said Frank Fallon, Vice President, Financial Services at Amazon Web Services, Inc. “With this agreement, Standard Chartered is relying upon the most secure, reliable, and flexible cloud computing environment in the world. AWS’s unparalleled experience in powering global financial institutions will enable Standard Chartered to transition to the cloud with confidence and innovate faster than ever before.”

For 14 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 175 fully featured services for compute, storage, databases, networking, analytics, robotics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 77 Availability Zones (AZs) within 24 geographic regions, with announced plans for 15 more Availability Zones and five more AWS Regions in India, Indonesia, Japan, Spain, and Switzerland. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. Customer reviews, 1-Click shopping, personalized recommendations, Prime, Fulfilment by Amazon, AWS, Kindle Direct Publishing, Kindle, Fire tablets, Fire TV, Amazon Echo, and Alexa are some of the products and services pioneered by Amazon

Why Microsoft and VMware Are Joining Forces on Azure VMware Solution

Enterprise IT teams are under constant pressure to expand infrastructure faster than on-premises hardware allows. Azure VMware Solution (AVS) is Microsoft and VMware’s co-engineered answer to that pressure: a fully managed service that lets organizations run VMware workloads natively on Azure dedicated bare-metal infrastructure, without rewriting applications or retraining operations teams.

The solution is built by Microsoft and endorsed by VMware — a distinction that matters operationally. Customers retain the native VMware experience (vSphere, vSAN, NSX-T) while gaining direct access to Azure services such as Azure Monitor, Azure Security Center, Azure Backup, and ExpressRoute for private connectivity.

“The very name of this solution — Azure VMware Solution — reflects just how collaborative this effort is across engineering, sales, and marketing functions in both companies.”

Eric Lockard, Corporate Vice President, Azure Dedicated, Microsoft

What Azure VMware Solution Actually Delivers

At its core, AVS provisions VMware SDDC (Software-Defined Data Center) clusters on Azure bare-metal nodes. The key operational characteristics for engineering teams include:

• Native VMware stack: vSphere, vCenter, vSAN, and NSX-T run as they do on-premises. Existing runbooks, automation scripts, and monitoring integrations continue to work.
• Azure Portal integration: Provisioning, scaling, and lifecycle management are handled through the Azure Portal, CLI, or ARM templates — the same control plane used for every other Azure resource.
• Direct connectivity to Azure services: AVS private clouds connect to Azure Virtual Networks over a managed ExpressRoute, enabling low-latency access to PaaS services without traffic traversing the public internet.
• VMware HCX for migration: The VMware HCX service is included to simplify live migration and bulk migration of on-premises VMs to AVS with minimal downtime.
• Microsoft SLA and support: Microsoft operates and supports the underlying infrastructure, including hardware replacement and software patching of the VMware components.

How Microsoft Positions AVS for Different Customer Profiles

According to Lockard, AVS is deliberately designed to serve three distinct architectural postures rather than forcing a single migration path:

1. All-in on a single cloud with VMware compute: Customers who want to standardize entirely on Azure while preserving VMware as their compute abstraction layer.
2. Abstraction-first: Organizations that want to run on a consistent VMware platform to avoid cloud vendor lock-in at the workload level, while still consuming Azure networking and storage services.
3. Multi-cloud: Enterprises that know they will operate across multiple public clouds and want VMware’s consistent operational model to span them.

“We are laser-focused on making Azure the best cloud for VMware, and we engineered Azure VMware Solution with the expectation that customers can utilize it as just another flavor of Azure Compute.”

Eric Lockard, Microsoft

What Is Driving Cloud Demand: Common Enterprise Use Cases

Lockard identified several distinct drivers pushing enterprises toward cloud adoption, each reflecting a different business or operational concern:

• OpEx over CapEx: Organizations prefer consumption-based billing (daily or monthly) over large upfront hardware investments.
• Hardware offload: Eliminating the procurement, maintenance, and end-of-life management of physical servers and storage.
• Elastic capacity (“burstiness”): The ability to spin up additional compute for short-duration projects — batch processing, seasonal peaks, or model training — without permanent infrastructure commitment.
• Disaster recovery and failover: Using Azure as a DR target for VMware workloads without building and maintaining a secondary data center.
• Access to cloud-native capabilities: Augmenting existing VMware application estates with Azure-native services such as machine learning, IoT Hub, and data analytics pipelines.

The Broader Trend: Every Company Is a Software Company

Beyond specific use cases, Lockard pointed to a structural shift in how industries consume compute. Sectors traditionally seen as non-technology industries — pharmaceuticals, manufacturing, logistics, retail — are increasingly running their core competitive functions on software and data workloads.

“Pharmaceuticals — that’s a software business. It is all about taking massive amounts of sample data and analyzing it with AI to quickly model and identify the right drug compounds for safe and effective patient treatments. That application of massive computing is happening in every industry.”

Eric Lockard, Microsoft

For DevOps and platform engineering teams, this trend has a direct architectural implication: the application estate that needs to run on VMware today will increasingly need to be complemented by cloud-native data and ML services. AVS is Microsoft and VMware’s answer to bridging that gap without a disruptive rip-and-replace migration.

Key Takeaways for DevOps and Infrastructure Teams

• AVS lets you migrate VMware workloads to Azure without modifying VMs, retraining staff, or replacing existing automation.
• The service is operated and supported by Microsoft, reducing the operational burden on internal infrastructure teams.
• Azure-native services are available over private connectivity, enabling hybrid architectures that mix VMware compute with PaaS-layer capabilities.
• VMware HCX (included with AVS) provides a supported, low-disruption migration path from on-premises vSphere environments.
• The partnership’s co-engineering model means VMware certifications and support contracts remain valid for workloads running on AVS.

For teams evaluating cloud migration strategies, Azure VMware Solution represents a lower-risk on-ramp to Azure — preserving operational continuity while opening access to the broader Azure service ecosystem.

Why Microsoft and VMware Are Joining Forces

Enterprise IT teams are under constant pressure to extend critical infrastructure to the cloud faster and with less disruption. To address this, Microsoft and VMware have collaborated to deliver a deeper iteration of Azure VMware Solution (AVS)—an offering engineered by Microsoft and endorsed by VMware that lets organisations move existing VMware-based workloads to Azure without re-architecting applications or retraining staff.

According to Eric Lockard, Corporate Vice President for Azure Dedicated at Microsoft, the partnership goes well beyond a licensing arrangement:

“Microsoft and VMware have emerged to become close partners determined to deliver a win-win for both companies and our shared customers. The very name of this solution—Azure VMware Solution—reflects just how collaborative this effort is across engineering, sales, and marketing functions in both companies.”

Eric Lockard, Corporate Vice President, Azure Dedicated, Microsoft

What Azure VMware Solution Actually Delivers

Azure VMware Solution is designed to feel like a natural extension of an existing on-premises VMware environment rather than a migration to an unfamiliar platform. Key characteristics of the solution include:

• Native VMware experience: Customers continue using familiar VMware tools—vSphere, vSAN, NSX-T, and HCX—without modification.
• Full Azure Portal integration: AVS is managed through the same Azure Portal used for all other Azure services, unifying operational visibility.
• Access to the Azure service catalogue: Once workloads run in AVS, teams can consume Azure-native services such as Azure Monitor, Azure Security Center, machine learning services, and IoT hubs alongside VMware compute.
• Flexible consumption models: Organisations can treat AVS as standard Azure compute, paying on a subscription basis and scaling capacity up or down without upfront hardware investment.

Lockard frames AVS as applicable to three distinct enterprise postures:

1. All-in on Azure: Organisations that want a single cloud while preserving their VMware compute platform.
2. Abstraction-layer approach: Teams that want to avoid perceived vendor constraints by running workloads on a consistent VMware layer regardless of the underlying cloud.
3. Multi-cloud strategy: Enterprises that intentionally spread workloads across multiple clouds, using VMware as the common operational fabric.

What Is Driving Enterprise Cloud Demand?

Lockard identifies a range of practical motivations pushing organisations toward the cloud, regardless of industry:

• OpEx over CapEx: The ability to pay by the day or month rather than investing in depreciating hardware.
• Elastic capacity (“burstiness”): Spinning up additional compute for a new project or seasonal demand spike without provisioning permanent infrastructure.
• Disaster recovery and backup: Using cloud as a failover target without maintaining a full secondary data centre.
• Hardware management offload: Freeing engineering teams from firmware updates, hardware replacement cycles, and data-centre operations.

The Deeper Trend: Every Company Is Becoming a Software Company

Beyond individual use cases, Lockard points to a more structural shift that he believes will sustain long-term cloud growth. In his view, differentiation in virtually every industry now depends on software and data analytics rather than physical assets or traditional processes alone.

“Every company is increasingly a software company. Pharmaceuticals—that’s a software business. It is all about taking massive amounts of sample data and analysing it with AI to quickly model and identify the right drug compounds for safe and effective patient treatments. That application of massive computing is happening in every industry, whether it’s optimisation of manufacturing or e-commerce.”

Eric Lockard, Corporate Vice President, Azure Dedicated, Microsoft

This framing has direct implications for how organisations should think about their VMware estates. Rather than viewing on-premises VMware infrastructure as a liability to be replaced, Lockard’s argument is that it becomes the foundation from which enterprises can layer on cloud-native capabilities—machine learning, IoT pipelines, real-time analytics—without disrupting the applications already running on it.

Key Takeaways for DevOps and Infrastructure Teams

• AVS lowers migration risk by letting teams move VMware VMs to Azure without converting them to native Azure VMs first—useful when application owners are risk-averse or timelines are tight.
• It is not a legacy crutch. The intent is that AVS workloads can progressively consume Azure-native services, enabling a gradual modernisation path rather than a hard cutover.
• The Microsoft/VMware endorsement model matters operationally: Support cases involving the underlying VMware stack are handled jointly, reducing the finger-pointing that can occur with third-party VMware cloud providers.
• Multi-cloud use cases are explicitly supported, meaning organisations already using VMware Cloud on AWS or on-premises VMware can extend to Azure without abandoning their existing operational model.

Looking Ahead

The partnership signals that both Microsoft and VMware see the hybrid and multi-cloud model as the dominant enterprise architecture for the foreseeable future. For DevOps and infrastructure engineers, the practical outcome is a more integrated toolchain: VMware’s operational familiarity combined with Azure’s breadth of managed services, all accessible through a single control plane.

Teams evaluating AVS should assess their current VMware licence agreements, Azure Reserved Instance pricing for dedicated hosts, and the specific Azure services they intend to consume alongside VMware compute—these factors will determine the total cost of ownership relative to alternatives such as refactoring to native Azure VMs or adopting containers.

What IoT Engineering Services Cover

IoT engineering services go well beyond device firmware. A full-service engagement typically spans four broad capability areas:

• Product Engineering: Hardware and embedded-software design, prototyping, and productisation of connected devices.
• Cloud & Platform Engineering: Building or integrating the backend infrastructure that ingests, stores, and processes device data—including IoT-specific platforms such as AWS IoT Core, Azure IoT Hub, and Google Cloud IoT.
• UI/UX Design: Creating dashboards, mobile applications, and operator interfaces that surface device data in actionable form.
• Analytics: Applying data engineering, machine learning, and visualisation to derive operational insights from sensor streams.

Key Market Drivers

Several converging forces are pushing organisations to invest in external IoT engineering capacity:

• Operational efficiency: Connected sensors and predictive-maintenance algorithms reduce unplanned downtime and lower the cost of system troubleshooting.
• Risk mitigation and data integrity: As device fleets scale, robust engineering practices around over-the-air updates, encryption, and redundancy become critical to minimising data loss and security exposure.
• Microservices adoption: Decomposing monolithic IoT backends into independently deployable services improves resilience and accelerates feature delivery.
• SMAC convergence: The intersection of Social, Mobile, Analytics, and Cloud (SMAC) technologies creates richer IoT application layers and drives demand for cross-disciplinary engineering teams.

Major Vendors in the IoT Engineering Services Space

The competitive landscape includes both large global IT services firms and specialist IoT engineering houses. Vendors frequently cited in market analyses include:

• Aricent (now Capgemini Engineering)
• Wipro
• Capgemini
• IBM
• Tata Consultancy Services (TCS)
• Happiest Minds
• Infosys
• Cognizant
• eInfochips (an Arrow company)
• RapidValue Solutions
• Tech Mahindra
• Prodapt Solutions

Large integrators compete on breadth of portfolio and global delivery capacity, while specialists differentiate on domain depth—for example, medical-device compliance expertise or industrial protocol support.

End-User Verticals

IoT engineering services are consumed across a wide range of industries. The verticals that generate the most significant revenue are:

• Industrial Manufacturing: Widely regarded as the largest segment, driven by smart factory initiatives, asset tracking, and condition-based maintenance.
• Medical / Healthcare: Remote patient monitoring, connected diagnostics, and regulatory-compliant device development.
• Transportation & Logistics: Fleet telematics, cold-chain monitoring, and last-mile optimisation.
• Energy & Utilities: Smart metering, grid monitoring, and demand-response systems.
• IT & Communications: Network infrastructure monitoring and edge-compute deployments.
• Other: Agriculture, retail, smart buildings, and public safety.

Regional Breakdown

Demand for IoT engineering services is global, but growth rates and maturity levels differ by region:

• Asia-Pacific (APAC): A high-growth region encompassing China, India, Japan, South Korea, Australia, and Southeast Asian markets. Manufacturing density and government smart-city programmes are primary demand drivers.
• North America: The United States, Canada, and Mexico represent a mature market with strong spend in industrial IoT, healthcare, and connected vehicles.
• Europe: Germany, the UK, France, Italy, Russia, and Turkey lead adoption, with industrial automation and energy-transition initiatives as key catalysts.
• South America: Brazil anchors the region; growth is emerging in agriculture and logistics IoT.
• Middle East & Africa: GCC countries are investing heavily in smart-city infrastructure; Egypt represents a growing services delivery hub.

Challenges for Market Participants

Despite strong demand signals, IoT engineering service providers and their enterprise clients face meaningful hurdles:

• Security and compliance complexity: Heterogeneous device fleets, constrained hardware, and evolving regulation (GDPR, HIPAA, IEC 62443) raise the engineering bar significantly.
• Interoperability: Fragmented connectivity standards (MQTT, AMQP, CoAP, Matter, Zigbee, LoRaWAN) require broad protocol expertise and add integration cost.
• Talent scarcity: The combination of embedded systems, cloud engineering, and data science skills in a single team is rare and expensive.
• Legacy system integration: Many industrial clients operate decades-old OT infrastructure that must be bridged to modern IP networks without production disruption.
• Proving ROI: Especially for new entrants, demonstrating measurable operational return before full deployment remains a commercial barrier.

What to Look for in an IoT Engineering Partner

For DevOps and platform engineering teams evaluating external IoT partners, key due-diligence criteria include:

• Demonstrated experience with your target connectivity stack and cloud platform.
• References in your specific vertical—regulatory knowledge matters in medical and energy contexts.
• Security-by-design practices: device identity management, certificate lifecycle, secure boot, and OTA update pipelines.
• CI/CD and DevOps maturity for embedded and edge workloads, not just cloud services.
• Clear ownership model for long-term firmware maintenance and hardware revision management.

Summary

The IoT engineering services market spans a broad range of technical disciplines—from hardware prototyping and embedded firmware through cloud platform integration and advanced analytics. Industrial manufacturing leads vertical adoption, Asia-Pacific and North America lead regionally, and a mix of global IT services giants and specialist boutiques compete for contracts. For engineering leaders, the most durable competitive differentiator remains depth of domain expertise paired with modern DevOps practices applied consistently from device to cloud.

Overview of the IoT Management Software Market

The Internet of Things (IoT) Management Software market encompasses platforms and tools that help organisations connect, monitor, configure, and secure large fleets of IoT devices. As enterprises scale their device deployments—from industrial sensors to smart-building infrastructure—demand for centralised management software has grown steadily. The market is shaped by factors including cloud adoption, edge computing, security requirements, and the expanding number of connected endpoints across industries.

Third-party research covering the period from 2019 to 2026 segments this market by manufacturer, deployment type, end-user application, and geography, and highlights the competitive dynamics among leading vendors.

Key Vendors in the IoT Management Software Market

The competitive landscape includes a mix of hyperscale cloud providers, enterprise software vendors, and specialist IoT platforms. The following manufacturers were identified as significant players in the global market:

• Amazon Web Services (AWS) – AWS IoT Core and related services provide device connectivity, telemetry ingestion, and rules-based automation at scale.
• Microsoft Azure – Azure IoT Hub and Azure IoT Central offer device provisioning, twin management, and integration with the broader Azure analytics ecosystem.
• Google Cloud IoT – Google Cloud IoT Core (now evolved into other Google Cloud services) provided MQTT/HTTP device connectivity and integration with Pub/Sub and BigQuery.
• Salesforce – Salesforce IoT Cloud connects device data to CRM workflows, enabling event-driven customer engagement.
• Cisco – Cisco’s IoT portfolio covers edge networking, security, and device management for industrial and enterprise environments.
• PTC ThingWorx – ThingWorx is an industrial IoT application enablement platform widely adopted in manufacturing and field-service scenarios.
• Oracle – Oracle IoT Cloud integrates device data with Oracle’s ERP, supply chain, and analytics applications.
• SAP – SAP IoT connects operational technology data with SAP’s business process suite, including SAP S/4HANA.
• Particle – Particle provides a developer-friendly hardware and software platform for prototyping and deploying connected products.
• ThingSpeak – MathWorks’ ThingSpeak is an open IoT analytics platform commonly used for data visualisation and rapid prototyping.
• Carriots – Carriots (now part of Dassault Systèmes) is a Platform-as-a-Service for IoT application development.
• Sierra Wireless – Sierra provides cellular IoT modules, gateways, and cloud connectivity management services.

Market Segmentation by Deployment Type

The research segments IoT Management Software by two primary deployment models:

• Cloud-Based – Fully managed SaaS or PaaS platforms hosted by the vendor. This model reduces infrastructure overhead and is well suited to organisations that prioritise rapid deployment and elastic scaling.
• Web-Based – Browser-accessible platforms that may run on-premises or in a private cloud, offering greater control over data residency and security posture.

Market Segmentation by Application

End-user demand is categorised across two broad groups:

• Large Enterprises – Typically require enterprise-grade security, compliance features, high availability SLAs, and deep integration with existing ERP or SCADA systems.
• Small and Medium Enterprises (SMEs) – Often prioritise ease of use, low total cost of ownership, and flexible pricing models such as pay-as-you-go or device-based subscriptions.

Geographic Coverage

The market analysis covers the following regions:

• United States
• Europe
• China
• Japan
• Southeast Asia
• India
• Central and South America

North America and Europe have historically led adoption due to mature cloud infrastructure and early enterprise IoT investment. Asia-Pacific markets—particularly China, Japan, and India—are significant growth areas driven by smart manufacturing, smart-city initiatives, and expanding mobile connectivity.

What the Market Research Covers (2019–2026 Forecast Period)

According to the source report, the analysis is designed to address the following:

• Factors driving and restraining market growth
• Historical and forecast revenue for market segments across North America, Europe, Asia-Pacific, and the rest of the world
• Country-level market sizing and future outlook
• Segment-level analysis by product type, end user, and region
• Strategic profiling of key vendors, including core competencies and competitive positioning
• Tracking of competitive developments such as mergers and acquisitions, strategic alliances, joint ventures, and new product launches

Key Questions the Research Addresses

For DevOps and platform engineering teams evaluating IoT management platforms, or for business stakeholders building a market case, the research is positioned to answer:

• What is the projected market size and growth rate through 2026?
• What macro and micro trends are shaping the market?
• What are the primary growth drivers—and the barriers that could slow adoption?
• Which vendors hold dominant positions, and what are their strengths and weaknesses?
• What opportunities and competitive threats do leading vendors face?

Practical Takeaways for DevOps and Platform Teams

Understanding the competitive landscape of IoT management software matters directly to teams responsible for selecting and operating IoT infrastructure. A few practical considerations:

• Vendor lock-in risk: Hyperscale platforms (AWS, Azure, Google Cloud) offer deep feature sets but tight ecosystem coupling. Evaluate portability of device SDKs and data formats before committing at scale.
• Edge vs. cloud trade-offs: Platforms from Cisco and Sierra Wireless emphasise edge processing, which reduces latency and bandwidth costs for high-frequency telemetry use cases.
• Protocol support: Ensure candidate platforms support the communication protocols your devices use—MQTT, AMQP, CoAP, HTTP, or proprietary cellular protocols are all common.
• Security and compliance: Regulated industries (healthcare, utilities, automotive) require platforms with strong device identity management, certificate lifecycle handling, and audit logging.
• Integration depth: Platforms from SAP, Oracle, and Salesforce add the most value when your IoT data needs to flow directly into business process systems, rather than staying in operational silos.

Source: Market overview originally reported by Galus Australis, citing research published by Reports and Markets (Algoro Research Consultants Pvt. Ltd.).

AWS Wavelength Goes GA on Verizon 5G

Amazon Web Services announced the general availability of AWS Wavelength on Verizon’s 5G network, giving developers the ability to build ultra-low latency applications for mobile devices and end-users in Boston and the San Francisco Bay Area. The launch marked a significant step in bringing cloud-native development patterns directly to the edge of carrier 5G infrastructure.

What Is AWS Wavelength?

AWS Wavelength embeds AWS compute and storage services inside telecommunications providers’ data centers at the edge of their 5G networks. These deployments are called Wavelength Zones. Application traffic originating from a mobile device travels only as far as the nearest cell tower and then on to a Wavelength Zone hosted in a metro aggregation site — eliminating the multiple network hops through regional aggregation points and the public internet that ordinarily add latency.

From a developer perspective, Wavelength Zones look and behave like any other AWS infrastructure. Teams use the same AWS APIs, SDKs, CLI tools, and management consoles they already know, and workloads running in a Wavelength Zone can connect seamlessly back to services and data in a parent AWS Region.

Why Edge Latency Matters for Modern Applications

Even with AWS operating 24 Regions and 77 Availability Zones worldwide (as of the announcement period), many emerging application categories require latencies that a regional cloud footprint alone cannot deliver over a mobile network. Use cases that benefit from Wavelength-class edge compute include:

• Machine learning inference — running real-time model scoring close to the data source rather than round-tripping to a remote Region.
• Internet of Things (IoT) — processing sensor and telemetry data at the network edge to reduce response time and backhaul cost.
• Smart manufacturing — enabling low-latency feedback loops on factory floors connected over private 5G or carrier mmWave.
• Autonomous driving and ADAS — offloading compute-intensive perception tasks to nearby edge nodes.
• Live event and video streaming — reducing buffering and improving quality of experience for large concurrent audiences.
• Cloud gaming — lowering input-to-display round-trip time to make streaming games feel responsive.

How the Traffic Path Changes

In a conventional cloud architecture, a request from a mobile device traverses the radio access network (RAN), travels through multiple carrier aggregation hops, exits onto the public internet, and finally reaches an AWS Region data center. Each hop adds latency.

With Wavelength, the path is shortened to: device → cell tower → Wavelength Zone (metro aggregation site). Traffic never leaves the carrier network to reach the application’s latency-sensitive compute layer. Only traffic that genuinely requires broader cloud services — persistent storage, managed databases, AI training, etc. — flows back to the parent Region.

Consistent Developer Experience Across Carriers

One of AWS’s stated design goals for Wavelength is a uniform developer experience across multiple 5G operators worldwide. Because Wavelength Zones expose the same AWS primitives regardless of which carrier hosts the underlying infrastructure, a development team can write once and deploy across different telecom partners without reworking deployment pipelines or operations tooling — even when those carriers have different internal deployment semantics.

This portability is particularly valuable for ISVs and SaaS vendors who need to serve customers on different carrier networks in different geographies without maintaining separate application stacks per operator.

Getting Started with AWS Wavelength

Developers already familiar with Amazon EC2, Amazon EBS, and Amazon VPC can extend an existing VPC into a Wavelength Zone by creating a subnet inside the zone, launching EC2 instances with carrier IP addresses, and routing edge traffic through those instances. AWS documentation and the AWS Management Console guide the configuration steps.

Applications that require ultra-low latency for their user-facing or device-facing layer — but rely on a broader set of AWS services for persistence, analytics, or orchestration — are the best candidates for a Wavelength deployment pattern.

Video Overview

Infostretch, a leader in digital engineering services, has announced that it has achieved the status of Advanced Consulting Partner in the Amazon Web Services (AWS) Partner Network (APN). To achieve this certification, Digital Engineering Services (DES) firm Infostretch had to undergo a meticulous approval process to demonstrate the company’s knowledge, experience and customer success with AWS. The AWS partnership comes as Infostretch continues to expand and strengthen its global strategic partner network, having recently announced a partnership with continuous testing leader Tricentis.

To help enterprise delivery teams accelerate their digital initiatives on the AWS cloud, Infostretch has developed sixteen AWS solution packages. These “accelerators” and “enablers” draw upon the company’s competencies in cloud engineering, data analytics, DevOps and IoT to allow its customers to benefit from the capabilities of the AWS at an accelerated pace. These include data engineering and IoT accelerators as well as enablers for the data pipeline, DevOps and Microservices.

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Infostretch has also developed solutions for specific industries to use with AWS. This includes Healthcare Data Analytics Platform (HDAP), a new data accelerator built on AWS for companies in the healthcare ecosystem to harness their healthcare data assets more effectively. By integrating and collaborating with AWS, Infostretch has built a unique set of AWS experiences for its customers, designed to reduce resource requirements, cost, time and project risk.

Headquartered in Silicon Valley, Infostretch has over 70 AWS Certified Experts among its ranks and has already delivered many AWS cloud enablement projects for its customers including Westhill. The company’s ‘Go, Be, Evolve’ digital approach aims to help clients at any stage in their digital maturity journey from strategy, planning, migration, and execution to production scaling and automation.

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“We were creating a first-of-its-kind, extremely complex platform ecosystem, leading with speed and security of data,” commented Larry Parker, Chief Product Officer & Founder of Westhill. “Infostretch had the proven experience and scale to deliver on our vision. They designed a robust architecture, fully mapped to AWS, which is already paying dividends for our business. Our customers highlight efficiencies in our solution every week, and that’s a testament to the technology.”

“With Advanced Consulting Partner status, Infostretch is now among the elite of cloud enablement partners globally,” said Rutesh Shah, Infostretch Chief Executive Officer and Co-Founder. “But we’re not stopping there. To extract the most value out of AWS and set up digital initiatives for long-term success requires deep expertise, which we’ve developed over the years. We’ve crystallized this expertise into accelerators and enablers that will fundamentally improve the AWS experience for our clients – and deliver better results for them at speed.”

The Cloud Market Keeps Growing — and Startups Are Leading the Charge

Even as the COVID-19 pandemic disrupted large portions of the global economy in 2020, the cloud computing market showed remarkable resilience. Gartner forecast the public cloud market would grow 6.4% in 2020 to reach $242.7 billion, with Software as a Service (SaaS) accounting for roughly $104.7 billion of that total. A separate 2020 InformationWeek/Interop survey found that IT leaders estimated 42% of their cloud-related budgets were directed at SaaS initiatives.

Flexera’s 2020 State of the Cloud Report reinforced how pervasive cloud adoption had become: 96% of surveyed organisations were using public cloud services, and 76% had a private cloud environment in place. Cloud computing was no longer an emerging option — it had become the default operating model for enterprises and small businesses alike.

Why Cloud-Native Startups Matter

As cloud infrastructure matured, it created fertile ground for a new generation of companies built entirely around cloud primitives. These cloud-native startups did not need to migrate legacy systems or maintain on-premises hardware — they could design their products from the ground up for elasticity, distributed architecture, and pay-as-you-go economics.

This structural advantage allowed them to challenge established vendors in categories such as backup and recovery, enterprise networking, security, and relational databases — often delivering the same capabilities at lower cost and with greater agility. Investors took notice: many of the most prominent cloud-native startups raised hundreds of millions of dollars in venture funding.

Selection Criteria

InformationWeek compiled its list of ten notable cloud startups using the following criteria:

• Founded in 2015 or later — ensuring the companies were genuinely cloud-era businesses rather than incumbents that had adopted cloud features.
• Cloud-related product or service — each company had to offer something directly tied to cloud infrastructure, platforms, or applications.
• Significant funding — the candidate pool was drawn from the highest-funded cloud startups tracked by Crunchbase at the time of publication.
• Ranked by total funding received — companies are listed in descending order of capital raised, not by subjective merit.

Key Technology Themes Among These Startups

While each company on the list has its own focus area, several common themes emerge across the group:

Cloud Meets IoT and Edge Computing

Several startups combine cloud-native architectures with Internet of Things (IoT) and edge computing capabilities. Rather than processing all data in a central cloud region, these platforms push computation closer to where data is generated — reducing latency and bandwidth costs while still leveraging cloud-scale management and analytics.

Reinventing Traditional IT with Cloud Economics

Other companies on the list take familiar IT disciplines — backup and disaster recovery, enterprise networking, identity and access management, and relational databases — and rebuild them as cloud-native services. The result is typically a consumption-based pricing model, automated operations, and integrations with major cloud providers such as AWS, Microsoft Azure, and Google Cloud.

Robotics and Automation

Some startups extend the cloud’s reach into physical operations, using cloud-connected robotics and automation platforms to bring software-defined control to warehouses, manufacturing floors, and logistics networks.

What This Means for DevOps and Platform Teams

For DevOps engineers and platform architects, the growth of cloud-native startups has practical implications:

• More specialised tooling: As startups target narrow problem spaces, teams gain access to purpose-built tools rather than trying to adapt general-purpose platforms.
• Vendor consolidation risk: Many well-funded startups are acquisition targets. Evaluate long-term vendor viability before standardising on a startup’s platform.
• Integration complexity: Cloud-native tools often assume a multi-cloud or hybrid environment. Review integration support for your specific stack before committing.
• Pricing model shifts: Consumption-based pricing can reduce upfront costs but introduces budget unpredictability. Implement cloud cost monitoring from day one.

Where to Learn More

The original InformationWeek slideshow, authored by Cynthia Harvey, provides individual profiles of each company including their founding story, core product, and funding history. For the most current funding data, consult Crunchbase directly, as startup valuations and funding rounds change frequently. Flexera publishes an annual State of the Cloud Report that tracks enterprise cloud adoption trends and is worth reviewing each year as a benchmark for your own organisation’s cloud maturity.

Original reporting by Cynthia Harvey for InformationWeek. Data points sourced from Gartner, Flexera, and a 2020 InformationWeek/Interop survey.

Overview

Amazon has extended AWS Glue with support for streaming ETL jobs, allowing teams to build continuous data ingestion pipelines that process and prepare streaming data on the fly. The feature makes processed data available for analysis within seconds of ingestion, without requiring customers to manage any underlying compute infrastructure.

What Is AWS Glue?

AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service designed to help teams prepare and load data for analytics workloads. Core capabilities include:

• Glue Data Catalog: A central metadata repository that provides a unified view of data across sources, usable by services such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
• Automatic code generation: AWS Glue generates Scala or Python ETL code that developers can inspect and customize.
• Serverless execution: No compute resources to provision, patch, or scale — AWS manages the underlying infrastructure.

What Streaming ETL Adds

Before this feature, AWS Glue jobs were primarily designed for batch workloads. The streaming ETL extension allows Glue jobs — built on Apache Spark — to run continuously and consume data from:

• Amazon Kinesis Data Streams
• Apache Kafka, including the fully managed Amazon Managed Streaming for Apache Kafka (Amazon MSK)

As Danilo Poccia described in the AWS blog post announcing the feature:

Managing continuous ingestion pipelines and processing data on-the-fly is quite complex, because it’s an always-on system that needs to be managed, patched, scaled, and generally taken care of. Today, we are making this easier and more cost-effective to implement by extending AWS Glue jobs, based on Apache Spark, to run continuously and consume data from streaming platforms such as Amazon Kinesis Data Streams and Apache Kafka (including the fully-managed Amazon MSK).

Danilo Poccia, Chief Evangelist (EMEA), Amazon Web Services

Key Capabilities

Spark Structured Streaming

Streaming ETL jobs in AWS Glue have access to the full capabilities of Spark Structured Streaming. This enables teams to apply a wide range of transformations to in-flight data, including:

• Aggregating records (e.g., computing rolling counts or sums)
• Partitioning data by time window or key
• Reformatting or normalizing fields
• Joining streaming data with static or slowly-changing reference datasets to enrich or cleanse records before writing them to a target

Supported Data Targets

Processed streaming data can be written to:

• Amazon S3 — for data lake storage
• Amazon Redshift — for data warehouse analytics
• Other supported data stores reachable through Glue connections

Automatic Code Generation

AWS Glue automatically generates Scala or Python boilerplate for streaming ETL jobs. Engineers can then customize the generated code using familiar tools and development workflows, lowering the barrier to entry for teams new to Spark Structured Streaming.

Unified Data Catalog

Data ingested via streaming ETL jobs is registered in the AWS Glue Data Catalog, making it immediately queryable and reportable through services such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum — without requiring additional catalog configuration.

Common Use Cases

Streaming ETL in AWS Glue is well-suited to scenarios where data arrives continuously and needs to be immediately usable for analytics or downstream systems:

• IoT event streams: Ingest and normalize device telemetry in real time before storing it in a data lake.
• Clickstream analytics: Process user interaction events from web or mobile applications, enriching records with session or user data before loading them into Redshift.
• Network and security logs: Continuously parse and transform log data for near-real-time monitoring dashboards or alerting pipelines.

Serverless Operations and Pricing

Because AWS Glue is serverless, streaming ETL jobs require no cluster provisioning or capacity planning. AWS automatically scales the underlying Spark infrastructure as data volumes change. Poccia summarized the operational benefit:

Managing a serverless ETL pipeline with Glue makes it easier and more cost-effective to set up and manage streaming ingestion processes, reducing implementation efforts so you can focus on the business outcomes of analytics.

Danilo Poccia, Chief Evangelist (EMEA), Amazon Web Services

The streaming ETL feature is available in the same AWS regions as AWS Glue. For current pricing, refer to the AWS Glue pricing page on the AWS website, as rates may have changed since this announcement.

Further Reading

For a deeper walkthrough of the feature, including example job configurations, refer to the original AWS blog post by Danilo Poccia and the AWS Glue developer documentation on streaming ETL.

The ten areas below are not ranked in order of importance. Each represents a distinct discipline with its own tooling, community, and career path. Consider which aligns best with your existing skills and interests, then invest deliberately in that direction.

1. Artificial Intelligence (AI)

Artificial intelligence refers to the ability of machines to perform tasks that would otherwise require human-level reasoning — recognising speech, interpreting images, making decisions, or generating text. AI is no longer confined to academic research. It powers recommendation engines, fraud detection systems, autonomous vehicles, and a growing range of enterprise applications. For IT professionals, practical entry points include understanding AI APIs, working with pre-trained models, and learning how AI systems are deployed and monitored in production.

2. Blockchain

A blockchain is a distributed ledger that records transactions across a network of computers in a way that makes records difficult to alter retroactively. Originally associated with cryptocurrency, blockchain technology is now being applied to supply-chain management, digital identity verification, smart contracts, and healthcare data sharing. IT professionals who understand consensus mechanisms, cryptographic hashing, and distributed systems architecture are well positioned to contribute to blockchain projects beyond the financial sector.

3. Augmented Reality (AR) and Virtual Reality (VR)

Augmented reality overlays digital content — graphics, sound, contextual data — onto a live view of the physical world, typically through a smartphone camera or dedicated headset. Virtual reality replaces the physical environment entirely with a simulated one, immersing the user through a headset and, in some implementations, motion controllers or haptic feedback.

Both technologies have moved well past gaming into enterprise use cases: AR is used for remote maintenance assistance and warehouse picking; VR is used for training simulations, architectural walkthroughs, and surgical planning. Developers with experience in Unity, Unreal Engine, WebXR, or platform-specific SDKs (ARKit, ARCore) are increasingly sought after.

4. Big Data

Big data describes datasets so large, fast-moving, or structurally complex that conventional relational databases and processing tools cannot handle them effectively. The classic framing uses three dimensions: volume (scale of data), velocity (speed of ingestion and processing), and variety (structured, semi-structured, and unstructured formats). Technologies such as Apache Hadoop, Apache Spark, Kafka, and managed cloud data warehouses (BigQuery, Redshift, Snowflake) form the backbone of modern big data pipelines. For IT professionals, skills in distributed storage, stream processing, and data engineering are highly transferable across industries.

5. Cloud Computing

Cloud computing delivers on-demand access to computing resources — servers, storage, databases, networking, analytics, and more — over the internet, typically on a pay-as-you-use basis. The three major public cloud providers (AWS, Microsoft Azure, and Google Cloud) collectively host infrastructure for the majority of the world’s internet-facing applications. IT professionals who can architect, deploy, and operate cloud-native workloads — including containers, serverless functions, managed databases, and identity and access management — are among the most consistently in-demand specialists in the industry.

6. Modern Front-End Frameworks: React and Angular

The modern web UI ecosystem is dominated by component-based JavaScript frameworks. React, maintained by Meta (formerly Facebook) and a broad open-source community, is a library for building composable user interfaces. It is widely used for single-page applications and, through React Native, for mobile applications. Angular, maintained by Google, is a full-featured framework that includes routing, form handling, and dependency injection out of the box.

Both ecosystems have large job markets and active communities. Choosing one to learn deeply — rather than skimming both — is generally more productive for career development. Understanding TypeScript is a practical prerequisite for Angular and is increasingly common in React projects as well.

7. DevOps

DevOps is a set of practices, cultural philosophies, and tooling that unifies software development and IT operations. The goal is to shorten the software delivery lifecycle while maintaining high reliability — achieved through automation of build, test, deployment, and infrastructure provisioning. Key practices include continuous integration and continuous delivery (CI/CD), infrastructure as code (IaC), observability, and blameless post-mortems.

Widely used DevOps tools include Git, Jenkins, GitHub Actions, Docker, Kubernetes, Terraform, Ansible, Prometheus, and Grafana. DevOps is not a job title so much as a competency that complements almost every other technical role — developers, sysadmins, QA engineers, and security engineers all benefit from DevOps fluency.

8. Internet of Things (IoT)

The Internet of Things refers to the network of physical devices — sensors, actuators, appliances, industrial equipment, wearables — that are embedded with computing capability and internet connectivity. These devices collect and exchange data, enabling remote monitoring, automated control, and data-driven optimisation across sectors including manufacturing, agriculture, healthcare, and smart buildings.

IoT work spans hardware selection, embedded firmware, communication protocols (MQTT, CoAP, LoRaWAN), edge computing, cloud ingestion pipelines, and security hardening. IT professionals with networking backgrounds or experience in Linux systems administration often find IoT a natural extension of their existing skills.

9. Intelligent Applications and Conversational AI

Intelligent applications embed AI capabilities directly into the user experience — chatbots that handle customer queries, virtual assistants that schedule meetings, recommendation engines that surface relevant content, and diagnostic tools that flag anomalies. These applications rely on natural language processing (NLP), intent recognition, and increasingly on large language models (LLMs).

For IT professionals, building intelligent applications today rarely means training models from scratch. It more often means integrating AI APIs, designing conversation flows, managing context and session state, evaluating model outputs for quality and safety, and deploying the resulting system reliably. Familiarity with platforms such as OpenAI’s API, Google Dialogflow, or AWS Lex is increasingly useful.

10. Machine Learning

Machine learning (ML) is the discipline of building systems that learn from data to make predictions or decisions without being explicitly programmed with rules for every scenario. It underpins AI applications ranging from image classification and speech recognition to fraud detection and predictive maintenance.

Practitioners typically need a grounding in linear algebra, probability, and statistics, alongside practical skills in Python and ML frameworks such as scikit-learn, TensorFlow, or PyTorch. Equally important — and often overlooked — is the ability to deploy, monitor, and retrain models in production, a discipline sometimes called MLOps. IT professionals with strong data engineering or software development backgrounds often find MLOps a productive specialisation without requiring deep research-level mathematics.

How to Choose Where to Start

Ten technology areas is a lot. Trying to learn all of them simultaneously is a reliable path to superficial knowledge in all of them. A more effective approach:

• Anchor to your current role. If you work in infrastructure, Cloud Computing and DevOps are natural adjacent skills. If you write web applications, React or Angular combined with a cloud platform is a high-value combination.
• Follow the job market in your geography. Demand varies by region and industry. Check local job postings to understand which skills employers in your area are actively hiring for.
• Pick depth over breadth. One technology mastered to a deployable, production-relevant level is worth more to an employer than cursory familiarity with five.
• Build something real. Certifications and courses matter, but a working project that demonstrates applied knowledge is consistently the strongest signal you can send to a hiring team.

The common thread across all ten areas is that they are not static destinations — each evolves rapidly. The most valuable skill is the ability to learn continuously, apply new knowledge practically, and communicate clearly across the development and operations boundary.

What Is Fog Computing?

There is an IT architectural concept that combines roots stretching back to the 1800s with some of the most pressing infrastructure challenges of the modern era—and most engineers have never heard of it. It is called Fog Computing, and it has attracted founding consortium members including Dell Technologies, Cisco, Intel, and Microsoft.

In the simplest terms: Fog Computing is an IT systems architectural model that distributes computing, storage, and networking throughout the device-to-cloud landscape based on economic and physical constraints. Rather than routing all data from connected devices to centralised cloud endpoints, fog computing inserts a hierarchy of compute resources between the device and the cloud—closer to where data is actually generated.

Why Cloud-Only IoT Architectures Hit a Wall

Researchers at Cisco are widely credited with identifying the core problem: there is not enough public network capacity for tens of billions of devices to continuously stream data to a handful of large cloud providers. Physics and economics both get in the way.

Consider what a cloud-only IoT model requires at scale:

• Bandwidth: Projections for connected devices now exceed 50 billion. Sending raw sensor data from all of them to centralised cloud endpoints would require network capacity that simply does not exist today—and public networks are already under growing pressure from streaming media and entertainment.
• Latency: Many IoT applications—industrial monitoring, autonomous systems, healthcare devices—require near-real-time analysis. A round-trip to a distant cloud data centre introduces latency that can make responses too slow to be useful or safe.
• Reliability: Cloud-only models create a single dependency chain. If network connectivity is interrupted, edge devices lose their ability to act on data.
• Security: Transmitting sensitive sensor data across public networks to remote endpoints increases the attack surface significantly.

The conclusion is straightforward: IoT applications will need to compete for public network capacity on economic terms, and the economics increasingly favour computing resources located closer to where devices are installed.

A Concept With Deep Historical Roots

Remote monitoring and control of machines and environmental factors is not a new idea. Wikipedia’s article on Telemetry references an 1847 example in which French engineers built a network of weather and snow-depth sensors on Mont Blanc that transmitted real-time information to Paris.

Translated into modern fog terminology, that 19th-century system maps almost directly onto contemporary IoT architecture:

• The weather and snow-depth sensors → IoT endpoint devices
• A ruggedised local computer on the mountain → an edge gateway
• A private network connection → a low-latency, reliable link to a fog node
• The data centre in Paris → a fog node or cloud back-end for long-term analysis

The same pattern emerges across many related disciplines: machine-to-machine (M2M) communication, mesh computing, mobile edge computing, and dew computing. Each has its own standards bodies, communities, and best practices. Fog computing aims to serve as a unifying architectural umbrella for all of them.

How Fog Computing Works in Practice

Fog computing introduces a hierarchy of compute, storage, and networking resources between cloud data centres and end devices. This hierarchy typically looks like this:

1. Endpoint devices – Sensors, actuators, meters, cameras, and other IoT hardware that generate data.
2. Edge gateways – Local compute nodes (often ruggedised hardware) that aggregate data from nearby devices, apply filtering, run lightweight analytics, and pass only relevant data upstream.
3. Fog nodes – Mid-tier servers or clusters, potentially located at the network edge or in regional data centres, that perform more complex analytics, store intermediate data, and coordinate with both gateways and the cloud.
4. Cloud back-end – Centralised infrastructure reserved for long-running analytics, machine learning model training, archival storage, and management of the wider system.

By processing data at the appropriate tier, fog architectures reduce the volume of data that must traverse public networks, cut round-trip latency for time-sensitive decisions, and improve resilience when connectivity is intermittent.

Why Fog Computing Matters for DevOps and Platform Engineering

For DevOps and platform engineers, fog computing is not just an academic concept—it has direct operational implications:

• Pipeline design: Data pipelines must be designed to route data to the right processing tier, not just to a central cloud endpoint.
• Deployment targets: CI/CD pipelines may need to deliver software updates to edge gateways and fog nodes, not just cloud services.
• Observability: Monitoring and logging must span all tiers. An alert generated at an edge gateway that never reaches the cloud is invisible to centralised monitoring tools.
• Security posture: Each tier must be hardened independently. Edge devices running in uncontrolled physical environments require a different security model than cloud-hosted services.
• Interoperability: With many existing standards bodies and protocols in play—from industrial automation to consumer IoT—platform engineers must manage integration complexity across heterogeneous ecosystems.

The Road Ahead

The growth of IoT is real, rapid, and—under cloud-only architectural assumptions—unsustainable. Fog computing offers a principled, layered approach to distributing computation where it is physically and economically rational to do so.

The current landscape of related but fragmented disciplines (M2M, mesh computing, mobile edge computing, and others) each contribute valuable thinking, but lack the critical mass to rapidly scale IoT deployments to the level envisioned. A unified fog framework, implemented with open and interoperable standards, is a necessary foundation for any infrastructure team serious about building systems that can handle tens of billions of connected devices.

If you are designing IoT infrastructure today, the question is not whether fog computing principles apply to your architecture—it is how deliberately you are applying them.