Complete guide to building a data quality framework. Learn constraint validation in Scala, YAML checks (DPAT), Spark/Airflow/CI/CD integration, and automated failure reporting. Includes real code and architecture to catch issues early.
Build reusable Scala utilities for data engineering on GCP, covering CDC, Delta maintenance, schema evolution, GCS helpers, affected-partition strategies, and error handling. Includes patterns for safe ops and repeatable pipelines.
Production-tested strategy to cut pipeline failures from 12% to 2% using YAML-based data quality checks (DPAT), CI/CD gates, and strict data contracts. Includes before/after metrics and architecture for self-healing pipelines in prod.
Building a demand forecasting system with Random Forest, ANN, and hybrid time-series models on Hadoop and Spark. Covers data ingestion, feature engineering, model training, validation, and rollout for large SKU catalogs and seasonality.
Deep-dive into Hadoop's state in 2019: Cloudera+Hortonworks merger, MapR struggles, and what Hadoop 3.0 delivers (GPU scheduling, Docker, Hive ACID). Learn why AWS/GCP/Azure dominate but Hadoop is evolving into hybrid cloud with Spark.
Deep-dive performance comparison of Hive, HBase, and Spark on 9.6M NYC taxi records. Learn why Hive ACID falls short, when HBase is 141x faster for lookups, and what Spark actually optimizes for. Includes benchmarks and usage guidance.