Data Engineering Cheat Sheet

1. Foundations of Data Engineering

Data engineering rests on a small set of foundational patterns for moving and storing data. The two dominant integration approaches are ETL (Extract, Transform, Load) and ELT (Extr...

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2. Data Modeling and Dimensional Design

Data modeling is the process of designing how data is structured in a database or warehouse to support efficient storage and querying. It involves defining entities, attributes, re...

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3. Distributed Compute with Spark and Flink

Apache Spark is a distributed computing framework for large-scale data processing with APIs in Python (PySpark), Scala, Java, and R. It supports batch processing, stream processing...

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4. Streaming Systems and Apache Kafka

Apache Kafka is a distributed event streaming platform used for building real-time data pipelines. It operates on a publish-subscribe model in which producers write messages to top...

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5. Storage Formats and Query Performance

Efficient analytics depend on how data is physically stored and read. Columnar storage organizes data by column rather than by row, which is highly efficient for analytics because...

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6. Orchestration, Transformation, and Engineering Workflows

Workflow orchestration is what turns individual scripts into reliable, scheduled, and observable production systems. Apache Airflow is an open-source platform for authoring, schedu...

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7. Data Quality, Governance, and Observability

Data quality is the foundation of trustworthy analytics, encompassing accuracy, completeness, consistency, timeliness, and validity. Poor data quality leads to incorrect analytics,...

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8. Modern Data Architecture

Several architectural patterns have emerged to address the scale, diversity, and real-time demands of modern data. The medallion architecture, popularized by Databricks, organizes...

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