How to Learn Data Engineering

A structured path through Data Engineering — from first principles to confident mastery. Check off each milestone as you go.

Data Engineering Learning Roadmap

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Estimated: 27 weeks

Programming and SQL Foundations

3-4 weeks

Master Python programming fundamentals and advanced SQL including window functions, CTEs, joins, subqueries, and query optimization. These are the two essential languages of data engineering.

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Relational Databases and Data Modeling

2-3 weeks

Learn relational database concepts: normalization, indexing, transactions, ACID properties. Study dimensional modeling (star and snowflake schemas) and Data Vault methodology.

ETL/ELT and Data Pipelines

2-3 weeks

Build data pipelines that extract from APIs, databases, and files; transform data through cleaning, validation, and enrichment; and load into target systems. Learn both ETL and ELT patterns.

Data Warehousing and Cloud Platforms

3-4 weeks

Gain hands-on experience with a cloud data warehouse (Snowflake, BigQuery, or Redshift) and a cloud platform (AWS, GCP, or Azure). Learn cloud storage, compute, IAM, and cost management.

Workflow Orchestration

2-3 weeks

Learn Apache Airflow or a comparable orchestrator (Dagster, Prefect). Build DAGs with task dependencies, scheduling, retries, alerting, and monitoring for production pipelines.

Big Data and Distributed Processing

3-4 weeks

Study distributed computing fundamentals, then learn Apache Spark (PySpark) for large-scale batch and streaming processing. Understand partitioning, shuffling, and optimization strategies.

Streaming and Real-Time Data

2-3 weeks

Learn Apache Kafka for event streaming, including producers, consumers, topics, and partitions. Study stream processing with Flink or Spark Structured Streaming. Understand exactly-once semantics and windowing.

Data Quality, Governance, and Observability

2-3 weeks

Implement data quality testing with dbt tests and Great Expectations. Study data governance, lineage tracking, cataloging, and observability practices to ensure reliable, trustworthy data in production.

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