Data Engineering — data engineering, Python/SQL, ETL and Big Data

Course overview

Description generated based on course syllabus and open data.

The Data Engineering program emphasizes hands‑on data engineering: from essential Python and SQL to building and orchestrating ETL/ELT pipelines, automating data pipelines, and designing scalable Big Data platforms. Focus areas include metric correctness, reproducibility, and data quality.

Data Engineering: program overview

The curriculum covers data sources, transformations, loading into warehouses, and building a data platform for analytics. It includes orchestration tools, data quality controls, and pipeline monitoring.

Who it fits / who it does not fit in Data Engineering

Best suited for data and infrastructure roles

• Analysts, BI/DS specialists, and Python/SQL developers who need stable data pipelines and ETL.
• Backend/DevOps engineers integrating services with a data platform and automating metric refresh.
• Professionals organizing data architecture and ensuring solution scalability.

Not a fit

• Those expecting theory only without coding, SQL, and orchestration tools.
• Those not planning to work with infrastructure, data warehouses, or cloud services.

Problem → outcome in Data Engineering

Challenges and expected effects

• Ad‑hoc data architecture → standardized layers (raw/staging/data warehouse).
• Manual ETL → automated scheduling, repeatable jobs, and failure recovery.
• Fragmented sources → centralized data platform with catalog and lineage.
• Inconsistent metrics → unified business logic in SQL/Python, data quality tests, and versioning.

Comparison with Data Engineering alternatives

Self‑study

• Pros: flexibility and freedom of stack choice.
• Limits: fragmentation, risk of missing critical orchestration and monitoring patterns.

Academic programs

• Pros: strong fundamentals.
• Limits: less practice in production data pipelines and tool integration.

BI/ML‑oriented path

• Pros: focus on analytics/models.
• Limits: infrastructure topics (ETL, DWH, orchestration) often remain uncovered.

Outcomes of completing the Data Engineering program

Competencies and artifacts

• Designing data architecture and storage models (warehouse/lake, batch/stream).
• Developing ETL/ELT in Python/SQL with data pipeline orchestration.
• Setting up schedulers, monitoring, alerting, and data quality checks.
• Cataloging, documentation, and version control for data and schemas.
• Foundational cloud data platform practices and scalability.