Insights and deep dives into data engineering, MLOps, and analytics — exploring practical architectures, system design principles, and the real-world challenges data teams face every day.
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Apache Spark architecture explained through real-world mechanics: job stages, partitions, shuffle behavior, memory usage, structured streaming, deployment models, and performance tuning strategies in production.
Slowly Changing Dimensions (SCD) are essential for maintaining historical accuracy in data systems where context evolves over time. This in-depth guide explores all SCD types, their engineering trade-offs, and practical strategies for designing dimensional data that preserves meaning — not just metrics.
Attribution across channels and devices isn’t just about tracking—it’s about understanding synergy across traffic sources like push notifications, social media, webinars, and affiliate programs. Combining data-driven attribution with MMM and incrementality testing enables smarter budget decisions under modern privacy constraints.
Modern networks are more than packets and ports—they’re programmable systems where architecture defines resilience. From OSI and TCP/IP models to segmentation, observability, and zero-trust enforcement, this article dissects how secure, scalable, and verifiable networks are built and defended.
Discover how a modern data platform unifies data, boosts business intelligence, and drives decisions with real-world fintech and ecommerce examples.
A comparison of AWS, Google Cloud, and Azure for data platforms — from storage and processing to analytics, governance, and MLOps. How each shapes architecture, operations, and long-term flexibility.
Modern data engineering isn’t about building pipelines — it’s about building trust, reliability, and cost-aware systems. This article reframes the role and explains what experienced engineers actually do.
Parquet, ORC, Arrow, Delta, Iceberg, and Hudi — not just file formats, but architectural levers. Storage layout, compression, and schema semantics define how data moves, scales, and fails across distributed systems.
Learn how to manage the machine learning lifecycle with MLOps. Follow a fintech team’s journey to build, deploy, and monitor a fraud detection model, ensuring scalability and GDPR compliance.
Explore the 4 types of analytics—descriptive, diagnostic, predictive, prescriptive—and learn how they drive business decisions with real-world examples.