Data Engineering
14 articles tagged with "Data Engineering"
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.
AI Engineering Career Path: Complete Guide for 2026
Roadmap to become an AI engineer in 2026: key skills, tools, specializations, salary ranges, and portfolio guidance for building production-ready AI systems.
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.