Analytics Engineering
52 articles tagged with "Analytics Engineering"
Scaling with Databricks and Snowflake: Strategies
Compare horizontal vs vertical scaling for cloud data platforms, explore autoscaling policies, cost trade-offs, and hybrid best practices for performance and savings.
Polyglot Persistence: Database Per Service Pattern
How polyglot persistence and the database-per-service pattern let microservices pick optimal databases, scale independently, and manage consistency trade-offs.
Open Source ETL Tools: Comparison Guide 2026
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.
How to Optimize Query Concurrency in Snowflake
Reduce Snowflake query slowdowns by tuning MAX_CONCURRENCY_LEVEL, using auto-scaling, clustering keys, materialized views, and monitoring.
Error Handling in dbt: Best Practices
Practical dbt error-handling guide: diagnose compilation, model, and database errors; use tests, safe casts, macros, logs, and CI/CD to prevent failures.
Case Study: Optimizing Analytics with dbt and Snowflake
How dbt and Snowflake modernize analytics: three-layer pipelines, faster queries, lower costs, and AI-enabled features with real-world results.
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.
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.
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.