Data Engineering Professional Certification
Build reliable pipelines, warehouses and lakehouses end to end
Key facts
- Level: Professional
- Field: Data Engineering & Analytics
- Estimated study time: about 35 hours
- Credential price: $149
- Exam: 70 questions · 100 minutes · pass mark 75%
About the Data Engineering Professional certification
The Data Engineering Professional certification validates that you can design and operate production data platforms against real business constraints. You will reason about ETL vs ELT, batch vs streaming, and the modern data stack — and turn those choices into concrete pipeline, storage and modeling decisions. The exam covers data-pipeline design and orchestration (DAGs, Airflow concepts, idempotency, backfills), ingestion patterns (CDC, APIs, files, connectors), distributed processing with MapReduce and Apache Spark (RDDs vs DataFrames, transformations vs actions, shuffles, partitioning, lazy evaluation), dimensional data warehousing (Kimball facts and dimensions, SCD types, OLAP vs OLTP), data lakes and the lakehouse (Parquet/ORC, Delta/Iceberg, schema evolution), file formats and compression, data quality, testing and observability, partitioning and bucketing, cost and performance optimization, and data governance, lineage and DataOps. It is tool-agnostic: the principles transfer across Spark, Snowflake, BigQuery, Databricks, Airflow and dbt. Candidates should already hold the SQL & Data Modeling associate credential or equivalent hands-on experience.
What you will learn
The official Data Engineering Professional study course covers:
- Pipelines & Orchestration — ETL vs ELT, batch vs streaming, and orchestrating idempotent, backfillable DAGs.
- Data Ingestion — Files, APIs, connectors and Change Data Capture — getting data in reliably.
- Distributed Processing & Spark — MapReduce, RDDs vs DataFrames, lazy evaluation, shuffles and partitioning.
- Dimensional Data Warehousing — Kimball facts and dimensions, star schemas, SCD types and OLTP vs OLAP.
- Data Lakes & the Lakehouse — Object storage, Parquet/ORC, Delta/Iceberg, schema evolution and the medallion architecture.
- Quality, Governance & DataOps — Data quality testing, observability, lineage, governance and cost/performance tuning.
Prerequisites
Frequently asked questions
- Is the Data Engineering Professional certificate verifiable?
- Yes. Every issued Hootix Academy certificate carries a unique credential code that anyone can verify online.
- How is the Data Engineering Professional exam structured?
- It is a 100-minute proctored multiple-choice exam of 70 questions; you need 75% to pass.
- Do I need to buy the course to take the exam?
- You can purchase the certification exam on its own, or bundle it with the full study course at a reduced price.
- How long does the Data Engineering Professional course take?
- About 35 hours of self-paced study.