DevOps
Best practices in continuous integration, continuous deployment, infrastructure as code, container orchestration, and site reliability engineering.
8 articles
Building Scalable Data Pipelines with Apache Kafka and Flink
Learn how to design and implement production-grade data pipelines that handle millions of events per second. We cover architecture patterns, fault tolerance strategies, and performance tuning techniques.
Lessons Learned Migrating to Microservices at Scale
After two years of migrating our monolithic application to a microservices architecture, here are the patterns that worked, the mistakes we made, and the tools that saved us along the way.
Building a Production-Ready CI/CD Pipeline with GitHub Actions
A step-by-step tutorial for building a comprehensive CI/CD pipeline using GitHub Actions, covering automated testing, security scanning, staging deployments, and production releases.
Cloud Migration Strategies: Lessons from 50 Enterprise Migrations
Drawing on our experience helping fifty enterprise customers migrate to the cloud, we explore the most effective migration strategies, common pitfalls, and the organizational changes required for success.
Kubernetes Autoscaling: A Deep Dive into HPA, VPA, and KEDA
Master Kubernetes autoscaling with this comprehensive guide covering Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and event-driven autoscaling with KEDA. Includes real-world configurations and performance benchmarks.
Securing Microservices with Istio Service Mesh: A Practical Guide
Learn how to implement zero-trust security for your microservices architecture using Istio service mesh. This tutorial covers mutual TLS, authorization policies, traffic encryption, and observability.
API Connect Now Supports GraphQL: Unified API Management
API Connect expands beyond REST with full GraphQL support, including schema management, query optimization, rate limiting, and analytics for GraphQL APIs alongside your existing REST endpoints.
The MLOps Maturity Model: Where Does Your Organization Stand?
Machine learning operations is evolving rapidly. This article presents a five-level maturity model for MLOps practices and provides actionable guidance for advancing through each level.