How do databases ensure data correctness under concurrency and failure? This article breaks down ACID properties, isolation levels, MVCC, and WAL, explaining how relational systems like PostgreSQL maintain consistency and performance.

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.

Anomaly detection in financial systems combines layered techniques—rules, statistics, and machine learning—to identify fraud, money laundering, and operational risk across high-volume transaction flows.

A data team’s success hinges on clear roles and collaboration. Explores how roles evolve, adapt to company needs, and align through a RACI matrix to deliver reliable data with minimal friction.

End-to-end breakdown of high-load API systems in Go — from architectural trade-offs and protocol selection to concurrency models, service meshes, CQRS, observability, and failure isolation in production.

Explore data modeling from basics to advanced techniques like Data Vault 2.0 and Anchor Modeling for business impact.

Compare Data Mesh vs. Data Fabric for modern data management and their impact on business scalability.

Compare Kimball vs. Inmon approaches to data warehouse design and their impact on business analytics.

A Merkle Tree is a scalable, SQL-friendly approach to verifying data integrity — widely used in systems like Git, blockchains, and distributed databases.

Design principles like SOLID, DRY, KISS, YAGNI, and GRASP aren’t rules — they’re tools for managing complexity, preserving clarity, and making software resilient to change. This deep dive explores each principle with real-world examples and refactoring patterns.

Master Kubernetes deployment strategies and troubleshooting with best practices for logging and monitoring in this guide for DevOps and ML engineers.

Dive into Kubernetes storage, security with Secrets and ConfigMaps, and advanced features like DaemonSets and Helm in this guide for DevOps engineers.

Uncover Kubernetes’ internal mechanisms—API flows, watch-loops, scheduling—and networking essentials like CNI plugins in this guide for DevOps professionals.

Explore Kubernetes’ foundational architecture and core components—control plane, worker nodes, Pods, and more—in this in-depth guide for DevOps and ML engineers.

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.