Fast queries, scalable pipelines and data you can trust.
Data systems fail quietly: a query fast at a thousand rows crawls at a million, a pipeline that worked in a demo drops records under load. Keeping data systems fast and correct as volume grows is its own discipline — one I practise across production pipelines and analytics workloads.
Articles in this hub
6 articles
Production-Grade Playwright Web Scraping on Kubernetes with Bright Data (2026)
The hard part is not writing the scraper. It is making it run reliably in production. A step-by-step guide using Playwright, Bright Data Browser API, and Kubernetes Jobs and CronJobs.
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LinkedIn Scraping vs. Buying a Dataset in 2026: 3 Days of Code I Could Have Skipped
A data engineer's review of Bright Data's Dataset Marketplace: why ready-made LinkedIn and e-commerce datasets can save weeks of scraping.
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Real-Time RAG in Python: Feed Your LLM Live Google Results (2026)
What if your RAG pipeline could pull fresh web context right before generating an answer? A step-by-step guide to building a live search retrieval layer with Bright Data's SERP API and Python.
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Kafka vs Pub/Sub in 2026: When Managed Messaging Saves a Fintech Launch
A 2026 fintech messaging case study: how a Kafka backlog, broker disk pressure, and rebalance storm broke checkout, and why Google Cloud Pub/Sub moved the payment flow out of the danger zone.
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Python Web Scraping Without Proxy Management: Bright Data Web Unlocker API (2026)
The hard part is not parsing HTML. It is getting a usable response from modern protected websites in the first place. A step-by-step guide using Bright Data Web Unlocker API with Python and BeautifulSoup.
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SQL Query Optimization in 2026: 7 Simple Techniques for Faster Database Performance
Seven practical SQL query optimization techniques for faster database performance, with PostgreSQL-focused examples for joins, IN lists, EXISTS, date ranges, aggregates, and deduplication.
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