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Before You Push: Implementing Quality Gates in Your Software Project

This post discusses best practices for automated testing in software engineering, including unit tests and integration tests for databases, APIs, and emulators. It also covers end-to-end tests using tools like Cypress, Appium, Postman, and more. Additionally, it highlights the importance of environm.. read more  

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How to Get Developers in Your Team to Contribute to Your Test Automation

A fresh blog post dives into how to get devs pulling their weight ontest automation- not as extra credit, but as part of shipping code. The playbook: tie automation work straight to thedefinition of done, clear up who owns what, and stop pretending delivery pressure is a mystery. The big idea? Most .. read more  

How to Get Developers in Your Team to Contribute to Your Test Automation
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Partitions, Sharding, and Split-for-Heat in DynamoDB

DynamoDB starts to grumble when a single partition gets hit with more than 1,000WCU. To dodge throttling, writes need to fan out across shards. Recommended move: start with10 logical shards. WatchCloudWatch metrics. DialNup or down. Letburstandadaptive capacitybuy you breathing room - untilSplit-for.. read more  

Partitions, Sharding, and Split-for-Heat in DynamoDB
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Building Mac Farm: Running 2000+ iOS Pipelines Daily

At Trendyol, they runover 2,000 iOSpipelines daily across130 Mac machines, executing50,000+ unit testsand10,000+ UI testsfor their iOS apps. The team initiated a mobile CI transformation to address the challenges of scale and performance as their team grew and AI usage increased. They built a macOS .. read more  

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In-place Pod resizing in Kubernetes: How it works and how to use it

Kubernetes 1.33 and 1.34 takein-place Pod resource updatesfrom beta to battle-ready. You can now tweak CPU and memory on the fly - no Pod restarts needed. It's on by default. What’s new: memory downsizing with guardrails, kubelet metrics that actually tell you what’s going on, and smarter retries th.. read more  

In-place Pod resizing in Kubernetes: How it works and how to use it
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KubeCon North America 2025 Recap: Federation and

HAProxy just droppedUniversal Mesh, a fresh spin on service mesh design. Forget the per-service sidecars - this model plants high-speed gateways at the network edges instead. Result? Lighter by 30–50% on resources, easier to upgrade, and way less hassle routing traffic across Kubernetes, VMs, and cl.. read more  

KubeCon North America 2025 Recap: Federation and
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Ingress NGINX Is Retiring. Here’s Your Path Forward with HAProxy

TheIngress NGINX projectis riding off into the sunset by March 2026. Time to pick a new horse. One strong contender: theHAProxy Kubernetes Ingress Controller. It matches feature-for-feature, comes with deeper observability, and reloads configs without taking your cluster offline. HAProxy’s not stopp.. read more  

Ingress NGINX Is Retiring. Here’s Your Path Forward with HAProxy
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udwall: A Tool for Making UFW and Docker Play Nice With Each Other

Hexmos droppedudwall, a declarative firewall manager that finally makesUFWandDockerplay nice. Docker’s notorious for bulldozing past UFW rules via iptables. udwall patches that hole. It syncs rules across both, auto-reconciles changes, backs up configs, and plugs cleanly intoAnsible. No more duct-ta.. read more  

udwall: A Tool for Making UFW and Docker Play Nice With Each Other
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Developers don’t care about Kubernetes clusters

Most cloud-native tools obsess over clusters. Not developers. That means poor support for things like promoting code between environments or deploying by feature - not just by repo. The author pushes for a better way: platforms that hide the Kubernetes mess and tame CI/CD. Think feature-driven deplo.. read more  

Developers don’t care about Kubernetes clusters
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The Grafana trust problem

Grafana’s been busy clearing the shelves.Grafana Agent,Agent Flow, andOnCall? All deprecated. The replacement:Grafana Alloy- a one-stop observability agent that handles logs, metrics, traces, and OTEL without flinching. Meanwhile,Mimir 3.0ships with a Kafka-powered ingestion pipeline. More scalabili.. read more  

BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).