CodeIsGo.com

A 20-line PowerShell snippet scans every subscription, flags ADF factories that haven’t executed a pipeline in 30 days, and hands you a clean-up hit-list in seconds. By Dieter Gobeyn.

This blog post provides overview of:

• Zero-run ADF factories still accrue IR, logging, and governance costs.
• “Unused” = no pipeline execution in last 30 days (configurable).
• One self-contained PowerShell script; read-only, no extra modules.
• Iterates all subscriptions, outputs table with subscription, RG, name, tags.
• Safe for Reader roles; can be scheduled in Azure Automation.
• Extend script to pipeline-level or different time windows as needed.
• Clean-up decisions remain manual—pair output with tagging/owner process.

Stale ADF pipelines bloat your tenant: they burn IR capacity, inflate log ingestion, confuse engineers, and widen the blast-radius of a security breach. Gobeyn’s post supplies a single, self-contained PowerShell script that enumerates every subscription, queries the ADF activity log for runs in the last 30 days, and returns a table of “zero-run” factories together with resource-group, subscription, and tags. Run it, eyeball the list, delete or disable—no external tools, no cost, no excuses. Perfect for FinOps squads, SREs, and data-platform owners who need a quick hygiene win before the next funding review. Nice one!

Deploying a Hugo static site to AWS S3 using the AWS CLI provides a robust, scalable solution for hosting your website. This guide covers the complete deployment process, from initial setup to advanced automation and cache management strategies. By About Rost Glukhov.

The article details deploying Hugo static sites to AWS S3 using the AWS CLI, offering a comprehensive guide from initial setup to advanced optimization. Key steps include generating static files with Hugo’s build command, configuring AWS CLI with proper credentials and IAM permissions, and setting up an S3 bucket for static website hosting.

Some key points mentioned:

• Use hugo --gc --minify to generate optimized static files.
• Configure AWS CLI with IAM permissions for S3 and CloudFront.
• Create and set up an S3 bucket for static website hosting.
• Apply bucket policies for public access or CloudFront integration.
• Deploy using aws s3 sync with --delete and --cache-control.
• Implement advanced cache control strategies for different file types.
• Set up CloudFront for CDN, SSL/TLS, and custom domains.
• Automate deployments with CI/CD pipelines (e.g., GitHub Actions).
• Monitor with CloudWatch and S3 logging.
• Troubleshoot common issues like cache invalidation and permissions.

Security considerations like restricting S3 access and using CloudFront as a CDN are highlighted. The guide explains using aws s3 sync with parameters like --delete and --cache-control to manage files and caching. Advanced strategies for cache management, such as setting different TTLs for HTML and assets, and selective CloudFront invalidation to reduce costs, are covered. Automation via CI/CD pipelines, including GitHub Actions, is demonstrated, along with monitoring through CloudWatch and troubleshooting common issues. The overall focus is on scalable, secure, and cost-effective deployment practices for static sites. Good read!

Observo AI revolutionizes privacy protection by dynamically identifying and securing sensitive data in telemetry across all organizational layers. By observo.ai.

Observo AI offers an AI-driven solution to detect and safeguard sensitive information within dynamic telemetry data. It addresses the growing challenge of hidden PII in logs, metrics, traces, and events, crucial for organizations grappling with stricter regulations and escalating breach disclosure timelines.

The article takes on journey exploring following:

• Invisible risk of sensitive data hidden in telemetry
• Why field-dependent tools can’t keep up
• How AI-Native data pipelines detect and secure PII
• What Observo AI delivers
• Real-world example: Hospital system secures PII and simplifies compliance

Observo AI offers a transformative approach to securing sensitive data in modern telemetry, addressing critical gaps in traditional tools. By leveraging AI-native pipelines, it provides real-time detection, protection, and cost-effective retention, significantly reducing compliance risks and operational burdens. This solution represents a substantial advancement in data security, appealing to organizations managing complex, dynamic data environments. Good read!

Black Friday’s chaos is a perfect example of how message queues can transform a fragile system into a resilient one, smoothing out traffic spikes and preventing system crashes. By Jakub Slys.

This blog post explains the core function of message queues in distributed systems, illustrating how they decouple producers and consumers to handle uneven workloads. It highlights the problem of system overload during peak demand (like Black Friday) and how queues act as a buffer, absorbing excess requests and preventing failures. The article targets developers and DevOps engineers interested in understanding how to build more robust and scalable applications. Essentially, queues are a critical tool for managing asynchronous communication and improving system stability.

Key Points:

• Decoupling: Message queues separate producers and consumers, allowing them to operate independently.
• Buffering: They absorb traffic spikes, preventing system overload.
• Asynchronous Communication: They enable non-blocking operations, improving responsiveness.
• Scalability: Consumer groups allow scaling out processing capacity.
• Fault Tolerance: Queues ensure messages are not lost even if consumers are temporarily unavailable.
• Idempotency: Producers and consumers need to handle potential message duplicates.
• Event-Driven Architecture: Queues are a foundational element of this architectural style.

Ultimately, the article argues that understanding message queues is essential for building modern, scalable, and fault-tolerant distributed systems, moving beyond simply handling immediate requests to embracing a more reactive and resilient approach to software design. Nice one!

This article compares Java and C++ performance, debunking myths and revealing Java’s strengths in memory management, execution speed, and optimizations. Find out why Java might be the unsung hero of high-frequency trading and server applications. By Johnny’s Software Lab LLC.

The main observations in teh article:

• Java’s garbage collection and compaction improve memory locality and reduce fragmentation.
• Java’s high-tier JIT-compiled code can match C++’s performance, but warm-up time and mixed code execution can slow down Java.
• Java’s latency is less predictable than C++ due to GC pauses and mixed code execution, but it can achieve low latency with proper tuning.
• Java’s runtime profiling and deoptimization checks enable aggressive, speculative optimizations.
• Java can emit more efficient instructions based on the runtime environment.
• The choice between Java and C++ depends on the specific use case and requirements

You will read about valuable insights into Java’s performance capabilities, challenging the notion that C++ is always superior. It highlights Java’s strengths in memory management and optimizations, making a strong case for its use in specific scenarios like high-frequency trading and long-running server applications. The article represents a significant step towards demystifying Java’s performance and encouraging informed language choices based on specific use cases.

The author concludes that Java and C++ have their strengths and weaknesses. While C++ excels in predictable latency and resource efficiency, Java shines in long-running server applications and high-frequency trading systems. The choice between the two depends on the specific use case and requirements. Good read!

Dive into the core protocols powering WebRTC, exploring how SDP, ICE, and DTLS work together to enable secure, real-time communication in production environments. Author works with the engineering team of a global live-streaming platform, and recently, we built a planet-scale WebRTC Selective Forwarding Unit (SFU) on AWS using Kubernetes to keep end-to-end latency below 150 ms (P95 = 140 ms) even across continents. By Oleksii Bondar.

The main learning article provides:

• SDP facilitates session negotiation between peers.
• ICE handles NAT traversal using STUN and TURN servers.
• DTLS ensures secure media stream encryption.
• Protocols are crucial for production-grade WebRTC applications.
• Implementation requires attention to scalability and performance.
• Security best practices are essential for real-time communication.

This article offers a valuable technical overview of the core protocols underpinning WebRTC, providing essential insights for developers and engineers. By elucidating the workings of SDP, ICE, and DTLS, it equips readers with the knowledge to build robust, scalable, and secure real-time communication systems. The detailed exploration of practical implementation challenges makes it a significant resource for advancing WebRTC applications in production environments. Good read!

Automating stateful apps with Kubernetes Operators simplifies management of complex workloads by handling critical tasks like scaling, backup and failover automatically. This reduces downtime and human errors that are common in manual deployment processes. By Keval Bhogayata.

Kubernetes controllers are mostly applicable when it comes to managing simple apps. But when dealing with complex or stateful apps, they have limitations. These native controllers cannot automate application-specific operations and workflows, making it difficult to manage tasks like database provisioning, upgrades, and failover reliably.

In this blog post you will also find information on:

• Custom Resource Definitions (CRDs)
• Controllers
• Reconciliation Loop
• Reconciler Function

While Operators automate many tasks, teams still need robust monitoring and alerting solutions to ensure apps are working as expected. Observability tools provide the visibility needed into app performance and stability that bridges automation with assurance. By integrating observability with Operators, Kubernetes can truly become a resilient platform. Excellent read!

Flink’s complexity stems from supporting a variety of use cases and having a rich set of features, but can be simplified with proper tooling. By Yaroslav Tkachenk.

The article covers these topics:

• Flink’s complexity comes primarily from supporting many different use cases (analytics, integration, ETL) and having a large feature set that enables these capabilities.
• The complexity is manageable with proper tools like the Flink Kubernetes Operator which simplifies deployment and management.
• Focusing on specific workflows can minimize Flink’s complexity by not using all its features at once.
• While Flink requires more effort upfront to learn and deploy compared to proprietary solutions, its richness of capabilities far surpasses any one-size fits all tool.
• The claims that Flink is “complex” are overblown considering other tools also have complexity, especially when supporting a wide range of use cases.

Flink is not going anywhere due to its rich feature set and support for a wide range of data streaming use cases, but requires proper tools like the Kubernetes Operator and focused workflows to minimize complexity compared to proprietary solutions. While the claims that Flink is "complex" are overblown considering other tools also have their own complexities, its richness far surpasses any one-size fits all tool for data processing. Nice one!

Automating post-deployment testing with GitHub Actions and Playwright provides rapid feedback, automatic rollbacks, and increased production confidence. By Türkçe Yazılar.

Tou will read about a robust post-deployment testing strategy using GitHub Actions and Playwright to ensure system health in production. It automates tests against the live environment after deployment, catching environment-specific issues, third-party service failures, performance problems, and configuration drift. The setup includes immediate Slack notifications for success or failure, and automatic rollback capabilities with Vercel integration.

The main points presented are:

• Why post-deployment tests matter
• Setting up GitHub actions
• What about the tests, what and how?
• The real value: Fast feedback and automatic recovery

This article provides a highly practical and valuable approach to post-deployment testing. The combination of GitHub Actions, Playwright, and Vercel’s API offers a compelling solution for automating critical checks and ensuring production stability. It represents incremental progress on existing CI/CD practices but significantly enhances confidence by incorporating real-world test execution. Its simplicity and effectiveness make it highly applicable across various development teams. Good read!

Provisioned mode for SQS ESM enables dedicated resources (event pollers) that scale up to 1000 per minute to handle traffic spi kes and low latency processing, maintaining up to 20K concurrency. This improves performance and responsiveness of event-driven apps, making it a viable option for high throughput scenarios. By Micah Walter.

The article points out these improvements with new integration:

• 3x faster scaling (1K per minute)
• Up to 20K concurrency (16x higher capacity)
• Configurable minimum/maximum pollers
• Low latency processing during traffic spikes

Provisioned mode represents a significant advancement in the capabilities of SQS ESM for Lambda functions. The dedicated event pollers provide 3x faster scaling and up to 16x higher capacity, enabling developers to build highly scalable, responsive event-driven applications with precise control over resource usage. The configurable minimum/maximum pollers allow optimization of costs during low traffic periods while maintaining consistent performance. With these improvements, provisioned mode opens new possibilities for high throughput Lambda workloads that require predictable, sub-second processing times even during traffic spikes. Good read!