Data Engineering
38 articles in "Data Engineering"
10 Benefits of Domain-Oriented Data Architecture
Decentralized domain-oriented data architecture improves data quality, speed, scalability, governance, security, and sharing by treating data as products.
How Partitioning Impacts Query Performance
Table partitioning reduces data scanned, speeds queries, lowers cloud costs, and improves resource use - learn keys, sizes, and pruning best practices.
Kubernetes Best Practices for Data Teams
Kubernetes best practices for data teams: cluster setup, Spark/Airflow integration, resource requests, autoscaling, security, monitoring, GitOps, and cost.
How to Debug Airflow DAG Failures
Step-by-step checklist to diagnose and fix Airflow DAG failures: verify DAG import, inspect task logs, test with dag.test(), validate connections, and tune resources.
AWS vs Azure for Data Engineers: Tool Comparison
Compare AWS and Azure data engineering tools — storage, ETL, streaming, ML, and pricing — to choose the platform that fits your team's skills and infrastructure.
Data Engineering Bootcamp Checklist: What to Look For
Assess curriculum, hands-on projects, mentorship, cloud tools, and costs to pick a bootcamp that truly prepares you for data engineering roles.
Data Engineering Portfolio: 5 Projects That Get Hired
Five end-to-end data engineering projects—streaming, ETL, warehouse, lakehouse, and observability—to showcase production-ready skills.
How to Learn SQL for Data Engineering: A Roadmap
Three-phase SQL roadmap for data engineers: master querying and DDL/DML, data warehousing and modeling, then optimization, testing, security and hands-on projects.
Databricks vs Snowflake: Which Platform to Learn First
Compare Databricks and Snowflake to choose which to learn first—Databricks for ML and engineering; Snowflake for SQL analytics and BI.
Databricks vs Snowflake: Cost Efficiency in Embedded Analytics
Compare pricing and scaling for Databricks and Snowflake in embedded analytics—compute, storage, and which workloads they suit best.
7 Essential Skills Every Data Engineer Needs
A concise guide to seven core data engineering skills: SQL, Python, data modeling, ETL/ELT, cloud platforms, governance, observability, and communication.
How to Transition into Data Engineering in 2026
A pragmatic roadmap to transition into data engineering: key skills, tools, cloud stack, and a 6–12 month plan to build production-ready pipelines.