SafeNet

Quantum & AI

Data Engineering Roadmap

Follow this roadmap to develop expertise in Data Engineering

Programming Fundamentals

Master programming languages like Python, Java, or Scala, essential for building data pipelines and working with big data frameworks.

Database Systems

Develop expertise in relational and NoSQL databases, SQL, data modeling, and database optimization techniques.

Data Processing

Learn batch and stream processing frameworks to handle large volumes of data efficiently and in real-time.

Data Infrastructure

Gain skills in cloud platforms, containerization, and infrastructure as code to build scalable data systems.

Data Pipelines & Orchestration

Design robust ETL/ELT workflows and use orchestration tools to automate, monitor, and manage data pipeline execution.

Orchestration Tools

Apache Airflow: Workflow management platform
Luigi: Python package for building pipelines
Dagster: Data orchestrator for ML, analytics, and ETL
Prefect: Modern workflow management
AWS Step Functions: Serverless workflow service

ETL/ELT Tools

Apache NiFi: Data ingestion and transformation
Informatica: Enterprise data integration
Talend: Data integration platform
Fivetran: Automated data integration
Stitch: Cloud ETL service

Data Quality & Governance

Great Expectations: Data validation
Apache Atlas: Data governance and metadata management
Collibra: Enterprise data governance
Alation: Data catalog
dbt Tests: Testing in transformation workflows

Pipeline Design Patterns

Idempotent Pipelines: Safe for multiple executions
End-to-End Testing: Testing entire pipelines
Backfilling: Reprocessing historical data
Incremental Processing: Handling only new/changed data
Error Handling: Managing pipeline failures