Data Engineering Blog

How Apache Airflow works for data engineers: DAGs, operators, the scheduler, and the production patterns (idempotency, backfills, sensors) that keep pipelines reliable.

Apache Spark for data engineers: the execution model (driver, executors, shuffles), DataFrames vs RDDs, production best practices, and when Spark beats SQL or a warehouse.

Change Data Capture (CDC) explained for data engineers: log-based vs query-based, Debezium and friends, the pipeline patterns that matter, and when CDC beats batch reloads.

Delta Lake vs Apache Iceberg compared: ACID, time travel, schema evolution, engine support and vendor lock-in. A practical way to pick an open table format for your lakehouse.

Dimensional modeling explained: fact and dimension tables, grain, conformed dimensions, and slowly changing dimensions (SCD). The Kimball method, applied with dbt.

Reverse ETL explained: how to sync modeled warehouse data back into SaaS tools (CRM, ads, support), when it beats a direct integration, the tools, and the main pitfalls.

Connect Metabase to DuckDB to run a fast local-first BI stack on Parquet, CSV and SQLite files. Setup steps, Docker config, gotchas and when to scale beyond it.

Fan-out joins, grain drift, NULL keys silently dropped by INNER JOIN: the 7 SQL join and GROUP BY bugs that inflate warehouse metrics — each with a detector query and a fix.

A concrete, opinionated decision framework to choose between SQL and Python for your data pipeline transformation layer — with flowchart, scoring table, and side-by-side code comparisons.

Learn Apache Kafka architecture, key concepts, and practical use cases. Includes Python examples, Docker setup, and comparisons with Pub/Sub and Kinesis.

Pass the data engineering system design interview: a 5-step framework, 3 worked pipeline examples (batch, streaming, CDC) and the patterns interviewers expect.

8 production-grade pipeline patterns explained with Python and SQL: idempotency, backfilling, dead letter queues, CDC, schema evolution. The patterns that keep ETL running at 3 AM without paging you.

Side-by-side comparison of data warehouse, data lake and lakehouse architectures: OLTP vs OLAP, medallion layers, Snowflake vs Databricks, and how to choose.

Learn dbt from scratch — models, materializations, testing, documentation, macros, incremental models, and project structure best practices.

Learn Docker essentials for data engineering — Dockerfiles, multi-stage builds, Docker Compose for local data stacks, and production best practices.

ETL still wins for compliance and on-prem; ELT dominates cloud warehouses. The hybrid setup most teams actually run (Fivetran + dbt + Snowflake), the exceptions, and how to decide for your stack.

A practical roadmap to becoming a data engineer in 2026 covering skills, tools, projects, interview prep, certifications, and salary expectations.

Complete guide to SQL window functions: ROW_NUMBER vs RANK vs DENSE_RANK, LAG/LEAD, running totals and frame clauses — with dialect notes for PostgreSQL, BigQuery and SQLite.

Master dimensional modeling with star and snowflake schemas. Learn fact tables, dimension tables, SCD types, and when to use each approach.

Learn how to use Jinja2 templating to keep Databricks Declarative Automation Bundles (formerly Databricks Asset Bundles / DABs) DRY, composable, and environment-aware with reusable fragments and conditional logic.

Production-grade Databricks projects: modular PySpark transformations, thin notebook entrypoints, unit testing, and deployment with Databricks Asset Bundles.

Learn how data contracts prevent breaking changes, reduce pipeline incidents, and improve trust across producers and consumers with practical implementation patterns.