57 articles in "Data Engineering"

Compare Soda's SQL/YAML real-time monitoring and Great Expectations' Python validations to pick the best data quality tool for your team's workflow.

Automated data validations for ingestion and transformations using Great Expectations and dbt-expectations to catch errors early and keep analytics trustworthy.

How data teams use audits, root-cause analysis, PDCA, feedback loops, agile methods and modern tools to improve data quality, reliability and delivery.

Plan RBAC, enforce MFA, apply network and session policies, and monitor grants to secure Snowflake during and after migrations.

Project-driven training and mentorship rapidly convert career-changers into high-earning data engineers.

Mentorship helps data professionals learn tools faster, build soft skills, expand networks, and accelerate promotions with practical, real-world guidance.

Compare green and traditional data pipelines: energy use, cost savings, scalability, and techniques like lazy evaluation, sparse models, and carbon-aware scheduling.

A practical checklist for selecting stream processing tools based on scalability, latency, cost, and support.

Use Databricks Lakehouse to combine real-time and historical market data, build streaming Delta pipelines, and train scalable predictive models.

Compare horizontal vs vertical scaling for cloud data platforms, explore autoscaling policies, cost trade-offs, and hybrid best practices for performance and savings.

How polyglot persistence and the database-per-service pattern let microservices pick optimal databases, scale independently, and manage consistency trade-offs.

Compare six open-source ETL tools—Airbyte, Airflow, NiFi, Pentaho, Meltano, and Talend (retired)—to find the best fit for scale, real-time needs, and team skills.