What Is Data Engineering? A Plain-English Guide
Data engineering is the discipline of collecting, moving, transforming and storing data so analysts, applications and AI can use it reliably.
Data EngineeringETL vs ELT: What Is the Difference?
Two patterns for moving and transforming data, differing in where the transformation happens.
Data EngineeringData Lake vs Data Warehouse vs Lakehouse
Three architectures for storing analytical data.
Data EngineeringWhat Is Apache Spark and When Should You Use It?
Apache Spark is an open-source engine for processing large datasets in parallel across a cluster.
Build Data Engineering capability in your team
Data Engineering Foundations
Pipelines, warehouses, lakes and the modern data stack explained hands-on, from first principles to a working end-to-end pipeline.
Data EngineeringApache Spark & PySpark for Data Engineers
Distributed processing with DataFrames, Spark SQL, partitioning, joins and performance tuning on realistic datasets.
Databricks & Lakehouse Engineering
Delta Lake, medallion architecture, workflows, Unity Catalog concepts and production-grade lakehouse patterns.
Data EngineeringWorkflow Orchestration with Apache Airflow
DAG design, scheduling, retries, sensors, testing and running reliable pipelines in production.
Data EngineeringReal-Time Data with Kafka & Streaming
Event streaming fundamentals, producers/consumers, topics, schema handling and stream processing patterns.
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