Data Engineering
83 articles in "Data Engineering"
Unified Storage with Apache Iceberg: Future Trends
Iceberg unifies streaming and historical data with metadata-driven ACID tables, time travel, and AI-ready file formats.
Hive Query Optimization Questions Explained
Practical Hive optimization: partitioning, bucketing, compression, Tez, vectorized execution and CBO to speed queries and cut storage and compute costs.
dbt Core vs dbt Cloud: Key Differences
dbt Cloud reduces ops overhead while dbt Core gives full control—compare hosting, scheduling, security, onboarding, and real costs.
Databricks Logging: Setup and Tips
Configure Python or Log4j logging in Databricks, centralize JSON logs to Unity Catalog or cloud storage, set retention and integrate monitoring.
Structured Streaming for Live Video on Databricks
Build low-latency live video pipelines with a unified lakehouse streaming approach, efficient state stores, and medallion data layers.
Metadata-Driven Data Quality: How It Works
Use metadata, lineage, and AI to automate validation, catch errors early, and scale data quality across pipelines.
Databricks vs. Airflow for Event-Driven Workflows
Compare Databricks and Airflow for event-driven workflows—native triggers, Spark scaling, integration trade-offs, and cost differences.
Databricks Projects for Data Engineer Portfolios
Build end-to-end Databricks portfolio projects that integrate Snowflake and Airflow to showcase ML, ELT, and orchestration skills.
Databricks for Anomaly Detection in Data Pipelines
Build real-time anomaly detection pipelines in Databricks using Delta Live Tables, Unity Catalog, Isolation Forest models, and SQL alerts.
Horizontal vs. Vertical Scalability in Analytics
Compare horizontal (scale-out) and vertical (scale-up) analytics strategies — benefits, costs, latency, fault tolerance, hybrid patterns, and when to switch.
Checklist for Building a Cloud Data Engineer Portfolio
Two to three production-ready cloud data projects beat dozens of tutorials for landing data engineering interviews.
Ultimate Guide to Stream Processing Frameworks
Compare Flink, Spark Structured Streaming, Kafka Streams, and Kinesis—learn latency, state management, time semantics, and how to choose the right framework.