Systems | Development | Analytics | API | Testing

A few weeks ago during a sprint, our QA team flagged a frustrating issue: a Playwright test that passed locally, failed in CI, then passed again all without any code change. It was slowing us down and shaking confidence across the team. Digging deeper, we found what many engineers face: Flaky tests caused by bad timing, unstable selectors, and missed auto-wait features. In fast-moving CI/CD pipelines, these issues went unnoticed until they broke builds.

Enterprise data pipelines are no longer mere support systems—they are strategic assets central to analytics, compliance, and operational intelligence. This article offers a comprehensive overview of how enterprise ETL pipelines work, the technologies involved, common challenges, and best practices for implementation at scale in 2025.

As businesses race to become data-driven, the ability to quickly build and iterate on data workflows is more critical than ever. Traditional ETL and ELT processes, while powerful, often require extensive coding, long development cycles, and high maintenance overhead. Enter low-code data pipelines: a modern, visual-first paradigm enabling faster development, broader accessibility, and better scalability.

Choosing the right test management tool is never simple, as it involves much more than creating test cases or organizing your tests into executions. We’re talking about a tool that will support the entire QA process, from connecting to your requirements to defect processing to reporting and everything in between. The two tools usually evaluated for test management within Jira are Xray and Zephyr.

True embedded analytics isn't about dashboards; it's about giving your users the confidence to act. It's about turning your application into the one place where data becomes a story, and differentiating it from those that lack a cohesive narrative. This guide explores how smoothly or painfully different BI platforms let you connect, embed, and share analytics within your product.

Organizations have mastered collecting and storing vast amounts of data in cloud data warehouses like Snowflake. This central repository has become the single source of truth for analytical insights, business intelligence, and reporting. However, the true potential of this data remains trapped if it's confined to the warehouse, creating a disconnect between rich analytical insights and real-time operational systems.

BigQuery has three new categories of advanced aggregation functions: group by extensions, user-defined and approximate aggregate functions.

BigQuery ML TimesFM model is now in preview, and forecasts datasets through the use of generative AI.