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

Eficode Earns Gartner Recognition for DevOps Value Stream Management

Eficode, a Helsinki-based DevOps services and platform company, has been recognized as a Sample Vendor in the Gartner Hype Cycle for Agile and DevOps, 2020. The annual Hype Cycle report covers the key methodologies, tools, and platforms that organizations use to support and scale DevOps initiatives. The 2020 edition was authored by Gartner analysts George Spafford and Joachim Herschmann and published on July 15, 2020.

What Is the Gartner Hype Cycle?

The Gartner Hype Cycle is a graphical representation of the maturity, adoption, and business applicability of specific technologies and methodologies. It is designed to help technology leaders understand how a given tool or approach will evolve over time, enabling more informed deployment decisions aligned with business objectives. Each edition of the Hype Cycle covers dozens of technology categories across varying stages of maturity.

Important disclaimer from Gartner: Gartner does not endorse any vendor, product, or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications represent the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

DevOps Value Stream Management Platforms: Why They Matter

Eficode was specifically recognized in the category of DevOps Value Stream Management Platforms (VSMP). According to the report, VSMPs provide visibility and traceability across every stage of the software delivery lifecycle — from initial ideation through development, release, and production, and extending to capturing customer feedback.

Hassan Ennaciri, Senior Director and Analyst at Gartner, is quoted in the report explaining the strategic value of VSMPs:

“To scale agile and DevOps practices, organizations need a DevOps VSMP that provides visibility into higher-level metrics. DevOps VSMPs serve as an orchestrator by integrating with existing DevOps tools. They provide product teams with flexibility and freedom in selecting their tools by simplifying integration with existing DevOps toolchains or value stream delivery platforms (VSDPs).”

Hassan Ennaciri, Senior Director & Analyst, Gartner

This framing reflects a broader industry shift: as DevOps toolchains grow more complex and involve more teams, the need for a unifying platform that can orchestrate, measure, and visualize the entire software delivery pipeline becomes critical.

Eficode ROOT: The Platform Behind the Recognition

The recognition centers on Eficode’s ROOT DevOps Platform, which is designed to help organizations manage their software delivery processes and DevOps toolchain integrations. According to Eficode CEO Ilari Nurmi:

“We have built the Eficode ROOT DevOps Platform to help organizations manage their software delivery and DevOps toolchain. With Eficode ROOT, our customers deliver customer value faster, manage quality- and compliance-related risks, and improve the much-needed visibility into software development. We are thrilled that Gartner, the world’s leading research and advisory company, has recognized Eficode in their report. We view it as a great testament to the work we are doing.”

Ilari Nurmi, CEO, Eficode

The ROOT platform is positioned as an integration layer that connects best-of-breed DevOps tools into a coherent, measurable value stream — rather than replacing individual tools, it orchestrates them and adds cross-tool visibility.

Notable Customer Use Cases

Eficode has worked with organizations across multiple industries in their DevOps transformations. Customers referenced in the announcement include:

• Sennheiser — embedded software development
• DNA (now part of Telenor) — telecommunications
• OP — financial services

At the time of the announcement, Eficode had also recently signed an agreement with Bankdata, one of Denmark’s largest financial technology companies, to deliver the Eficode ROOT DevOps Platform. Bankdata selected Eficode for its ability to integrate best-in-class individual software development and operations tools into a continuous value stream.

Key Takeaways for DevOps Teams

• The Gartner Hype Cycle for Agile and DevOps, 2020 explicitly called out DevOps Value Stream Management Platforms as a distinct and important technology category.
• VSMPs address the orchestration and visibility gap that emerges when organizations scale DevOps across many teams and tools.
• Analyst recognition of this category signals growing enterprise demand for end-to-end software delivery observability — not just CI/CD automation.
• Organizations evaluating DevOps toolchains should consider how individual tools connect into a coherent value stream, and what visibility exists across that stream.

Source and Attribution

Gartner, Inc., Hype Cycle for Agile and DevOps, 2020, by George Spafford and Joachim Herschmann, July 15, 2020. Original press release via PR Newswire, August 4, 2020.

Financial Services Firms Are Outpacing Other Sectors on DevOps

According to a report from Redgate, businesses in the financial services sector have pulled ahead of other industries — including government — when it comes to adopting DevOps practices. The driving forces are familiar: faster feature delivery, greater developer efficiency, and the need to stay competitive in a rapidly evolving market. But the sector’s regulatory complexity and data privacy requirements make the journey more demanding than in most other verticals.

A Generative Culture at the Core

Kendra Little, Redgate DevOps Advocate and author of the report, points to cultural posture as the key differentiator:

“At the heart of what makes the financial services sector so interesting is its willingness to adopt a generative culture, which focuses on breaking free of siloes and promoting a proactive, collaborative atmosphere. It’s what makes DevOps adoption work so well. This is a far cry from the culture and structure we saw from respondents in other sectors, which were often marred by little to no cooperation, and an inability to adapt to change.”

— Kendra Little, Redgate DevOps Advocate

In DevOps literature, a “generative culture” — a term rooted in Ron Westrum’s organisational typology — describes an environment where information flows freely, failure is treated as an opportunity to learn, and cross-functional collaboration is the norm rather than the exception. Financial services firms, at least the leading ones, appear to be embodying this model more consistently than their peers in other industries.

Unique Pressures in Financial Services

The financial services sector faces a particularly demanding set of constraints that make DevOps adoption both more urgent and more complex:

• Customer expectations: End-users increasingly expect seamless digital experiences, new product features delivered quickly, and near-zero downtime.
• Regulatory compliance: Strict frameworks — including data protection regulations and financial industry standards — must be maintained throughout any modernisation effort.
• Data privacy: Handling sensitive financial data requires robust access controls and auditability, which must be preserved even as deployment pipelines are automated.

This combination of speed-driven demand and compliance-driven caution is exactly why database DevOps — not just application DevOps — becomes critical in this sector. Changes to database schemas and stored procedures carry significant risk and must be automated with care.

Key Findings From the Redgate Report

The report surfaces several metrics that illustrate how financial services firms compare to respondents across other sectors:

• 77% of financial services respondents have adopted DevOps across all or some projects, or have a proof-of-concept in place — compared to 69% across other sectors.
• 34% of financial services respondents cite increased speed of delivery as the biggest driver for automating database change delivery, versus 26% across other sectors.
• 66% of respondents believe the transition from traditional database development to a fully automated deployment process can be achieved in a year or less. That figure rises to 69% in the US and compares to 61% across other sectors.
• Financial services businesses are ahead of every other sector surveyed in adopting automation across the database development lifecycle.
• 54% of financial services businesses now deploy database changes daily, weekly, or on demand — compared to 49% across other sectors.

Leaders Are Surging Ahead — Laggards Risk Falling Behind

Little emphasises that the sector’s leading firms are not operating under easier conditions than their slower-moving peers — they face the same obstacles. The difference lies in how proactively those obstacles are addressed:

“What’s fascinating about the deeper sector analysis is the way businesses in financial services are innovating faster and further than any other sector. The data shows they face the same obstacles and challenges, but most are still surging ahead. Those who are lagging behind may need to revisit their digital transformation plans in order to remain competitive.”

— Kendra Little, Redgate DevOps Advocate

Common Hurdles That Still Remain

Despite overall sector leadership, the report acknowledges that significant challenges persist. Based on common patterns in financial services DevOps adoption, these typically include:

• Legacy systems: Many financial institutions run decades-old core banking systems that are difficult to integrate into modern CI/CD pipelines.
• Database automation gaps: Application deployment automation often matures faster than database change management, creating a bottleneck in the pipeline.
• Compliance and audit requirements: Automated pipelines must produce audit trails and maintain controls that satisfy regulators — adding complexity to pipeline design.
• Organisational silos: While leading firms have broken down silos, many institutions still operate with separate development, DBA, security, and operations teams that do not collaborate effectively.
• Talent and skills gaps: DevOps skills — particularly around database DevOps, security automation (DevSecOps), and platform engineering — remain in short supply.

What Financial Services Teams Should Take Away

For DevOps practitioners and engineering leaders working in or with financial services organisations, this data reinforces several practical priorities:

1. Treat database changes as first-class citizens in your pipeline. Application CI/CD without database automation leaves a critical gap. Tools that version-control schema changes and automate deployments alongside application code are essential.
2. Invest in cultural change alongside tooling. Technology alone does not produce DevOps outcomes. Cross-functional team structures, blameless post-mortems, and shared ownership of the delivery pipeline are non-negotiable.
3. Build compliance into the pipeline, not around it. Automated policy checks, audit logging, and security scanning should be embedded in the CI/CD workflow rather than applied as manual gates after the fact.
4. Benchmark your deployment frequency. The shift from quarterly or monthly releases to daily, weekly, or on-demand deployments is achievable within a year according to most survey respondents — but it requires deliberate planning and executive sponsorship.
5. Reassess your digital transformation roadmap regularly. Firms that are not measuring their DevOps maturity against sector benchmarks risk losing ground to competitors who are.

This article is based on research published by Redgate and originally reported by Help Net Security in 2020. Specific survey methodology and sample size details were not available in the source material reviewed.

The Advertising Dependency Problem

Google entered 2020 with an enviable revenue base, but also a significant structural vulnerability: an overwhelming dependence on advertising. In Q1 2020, Google generated $33.8 billion in advertising revenue, representing approximately 82% of its $41.2 billion in total quarterly revenue. That share had already declined slightly from 84.5% in Q1 2019, but advertising still dominated the business.

CFO Ruth Porat acknowledged during the Q1 2020 earnings call that advertising results are highly correlated with macroeconomic conditions. She described “an abrupt decline” in March advertising revenue after strong January and February results—a direct reflection of the economic disruption caused by the COVID-19 pandemic. For a company whose innovation engine depends on stable, predictable cash flows, that volatility represented a serious strategic risk.

Cloud as the Resilient Counterweight

While advertising stumbled, Google Cloud proved notably resilient. Cloud revenue for Q1 2020 reached $2.8 billion—up 52% year-over-year and up approximately 6% from Q4 2019. A strong March performance drove much of that result, directly contrasting the collapse seen in the advertising segment during the same month.

This divergence illustrated a broader industry pattern: enterprise cloud spending tends to be more contractual and less immediately reactive to short-term economic shocks than digital advertising spend. For Google, which had been investing heavily to close the gap with AWS and Microsoft Azure, the pandemic created both urgency and opportunity to accelerate its cloud strategy.

Google’s Three-Pronged Cloud Growth Strategy

1. Continued Cloud Hiring Amid Broader Slowdowns

Even as Google outlined plans to slow overall headcount growth in the second half of 2020, Porat reiterated the company’s commitment to continued cloud hiring. This mirrored an approach taken by Microsoft, which reportedly exempted its Azure cloud division from a broader hiring freeze instituted in April 2020. The message was clear: cloud talent acquisition was non-negotiable, even as other parts of the business tightened spending.

2. Accelerating G Suite Adoption

Google moved quickly to leverage the remote-work surge by making Google Meet—part of its G Suite cloud productivity suite—available for free with no time limit. This was part of a broader push to grow G Suite’s enterprise footprint during the pandemic, as organizations of all types adapted to distributed work.

The results were significant:

• G Suite enterprise subscribers surpassed 6 million paying customers in March 2020, up from 5 million in February 2019, according to reporting by CNBC.
• Google Classroom doubled its active user base over the course of March 2020, reaching 100 million active users.
• Google Meet saw 25 times more daily users in April 2020 compared to the start of the year.

These growth figures underscored the role pandemic-driven adoption played in validating Google’s productivity cloud investments.

3. Aggressive Google Cloud Platform Acquisitions

Rather than pulling back on M&A activity, Google reportedly continued pursuing strategic acquisitions to strengthen Google Cloud Platform (GCP). In late April 2020, Axios reported that Google was in talks to acquire D2iQ—a company focused on Kubernetes-based cloud infrastructure and enterprise container management—for as much as $775 million. While the deal’s outcome was not confirmed in the original reporting, the disclosed negotiations signaled that Google’s acquisition appetite for cloud-native technology remained strong even during a period of broader financial caution.

GCP competes directly with Amazon Web Services and Microsoft Azure by offering cloud compute, storage, machine learning, and data analytics services on a subscription basis. Acquiring specialized cloud software companies is a proven mechanism for rapidly adding differentiated capabilities to such platforms.

The Bigger Picture: Diversification as a Survival Strategy

Google’s “Other Bets” segment—which houses long-term, capital-intensive ventures like Waymo (autonomous vehicles), Wing (drone delivery), and Verily (life sciences)—recorded a net loss of over $1.1 billion in Q1 2020 alone. These businesses are not expected to generate near-term returns; they depend on Alphabet’s core business generating sufficient surplus capital to fund speculative, long-horizon R&D.

That model works when advertising revenue is stable and growing. It becomes fragile when advertising is exposed to macroeconomic shocks—exactly the scenario that materialized in early 2020. A more diversified revenue base, anchored in recurring cloud subscriptions rather than cyclical ad spend, would provide Google with greater financial stability to sustain this innovation-through-subsidiaries strategy across economic cycles.

Key Takeaways for DevOps and Cloud Practitioners

• Enterprise cloud spending proved more recession-resistant than digital advertising during the early COVID-19 economic shock—a data point relevant to budget justification conversations in engineering organizations.
• Google’s investment in Kubernetes ecosystem acquisitions (such as the reported D2iQ talks) signals continued platform-level commitment to container orchestration as a core GCP differentiator.
• G Suite’s rapid growth during remote-work adoption demonstrated the compounding value of bundled productivity-plus-infrastructure cloud offerings for enterprises.
• Selective hiring protection for cloud teams—even during company-wide slowdowns—reflects how organizations prioritize strategic growth areas when economic pressure forces trade-offs.

Original reporting by Business Insider Intelligence, based on Google/Alphabet Q1 2020 earnings disclosures and industry analysis. Financial figures cited reflect Q1 2020 results as reported at the time.

Why Azure’s Computing Power Is Accessible Right Now

Organisations can start realising significant business benefits from Microsoft Azure without waiting until they have fully migrated their data to the cloud. Azure offers a range of high-performance computing capabilities that are simply not cost-viable to replicate in a traditional on-premises environment — chief among them being enterprise-scale artificial intelligence.

A clear example is Azure Databricks, an Apache Spark-based analytics platform capable of running complex machine learning workloads at speeds that would be cost-prohibitive using on-premises infrastructure. Services like this can be provisioned on demand and shut down when no longer needed, meaning organisations pay only for what they consume — eliminating the cost of idle servers.

The Cost Trade-Off: Cloud Compute vs. Data Transfer

It is worth noting that while you do not need to have the bulk of your organisational data permanently in the cloud to begin using Azure’s compute services, there are real costs associated with repeatedly transferring large datasets — potentially terabytes at a time — into Azure for processing. If high-performance computation is to become a regular workload, it makes economic sense to evaluate permanently migrating corporate data to the cloud rather than repeatedly incurring egress and ingress transfer costs.

Driving Innovation with Pre-Built Azure Services

Decision-makers today have a spectrum of options when it comes to building innovative solutions. They can develop capabilities entirely from scratch, or they can accelerate delivery by leveraging Azure’s growing library of pre-built services. Azure Cognitive Services is a particularly compelling example: it exposes pre-trained machine learning models for tasks such as:

• Facial recognition
• Text sentiment analysis
• Speech-to-text transcription
• Image classification and object detection

The significance here is accessibility. A developer with no background in deep neural networks can integrate a production-grade facial recognition capability into a standard business application by consuming an Azure Cognitive Services API. Organisations do not need to recruit large teams of AI researchers or data scientists to begin delivering value — they can build on models that Microsoft has already trained and validated.

A Real-World Example: Automating Insurance Call Centre Workflows

A practical illustration of this approach comes from the insurance sector. An insurance client needed a way to automatically prioritise call centre emails from customers whose tone indicated frustration or urgency. Altron Karabina worked with the client and Microsoft to deploy a solution using Azure Cognitive Services combined with Robotic Process Automation (RPA). The workflow operates as follows:

1. Incoming emails are analysed automatically as they arrive.
2. The sentiment analysis model estimates the probability that the customer is highly dissatisfied, based on the language and tone of the email content.
3. High-priority emails are flagged and routed accordingly, allowing agents to respond to the most urgent cases first.

The same client also needed to classify insurance claims that included photographic attachments. Using Azure Cognitive Services Image Recognition, the system could identify images of damaged vehicles attached to claims and automatically route those claims through the appropriate vehicle claims workflow — reducing manual triage effort significantly.

Starting Small: Experimentation Without Full Migration

Organisations that are not yet ready to commit to a full cloud migration can still begin experimenting with Azure’s high-performance capabilities at a modest scale. Azure Databricks, for example, can be used at an individual or team level to explore data analysis tasks — the platform scales from small exploratory workloads right through to complex enterprise pipelines.

This flexibility is one of cloud computing’s defining strengths: the same underlying infrastructure that handles a single developer’s exploratory analysis can scale to support an organisation’s most demanding production workloads. Starting small provides a low-risk way to build internal competency, validate use cases, and build the business case for broader cloud adoption.

Key Azure Services to Know

The Bottom Line for DevOps and Engineering Teams

The practical takeaway for engineering and DevOps teams is this: Azure lowers the barrier to delivering AI-powered features significantly. You no longer need to build and maintain custom ML infrastructure to integrate capabilities such as sentiment analysis or image recognition into your applications. Pre-built, API-accessible services reduce both time-to-delivery and the specialist skill requirements for your team.

The pay-per-use model also changes the economics of experimentation. Running a proof-of-concept on Azure Databricks costs a fraction of what provisioning equivalent on-premises compute hardware would require. Teams can validate ideas quickly, fail cheaply, and scale successful projects with confidence.

As AI workloads become an increasingly standard part of enterprise application development, the organisations that invest time now in understanding and experimenting with these services will be far better positioned to compete.