Big Data & ETL Testing
Big Data / ETL Testing Training Program
An eight-week intensive that compresses a year of industry experience into a single, rigorous cohort.
Designed for aspiring data quality engineers, this program covers the full ETL testing lifecycle — from source-to-target validation, to data reconciliation, to performance testing of Hadoop and cloud data pipelines. Graduates leave with a professional portfolio of test plans, automation scripts, and case studies, prepared to walk into entry- and mid-level QA roles.
What makes this programme worth your time.
Industry-Grade Curriculum
Built from real engagement playbooks used at Fortune-500 banks, insurers, and telecoms — not textbook exercises.
Hands-On Lab Access
Every student provisions a private sandbox with Hadoop, Hive, Spark, and Azure Data Factory for unrestricted practice.
Portfolio on Graduation
You leave with three documented case studies — test strategy, automation suite, and a reconciliation dashboard.
Career Services
Resume coaching, mock interviews, and referral introductions to our Peel-region employer network.
A careful progression, in four modules.
Module I · Foundations of Data Quality
- —Principles of ETL architecture
- —Data warehousing concepts (Kimball / Inmon)
- —Writing a test strategy document
Module II · SQL & Source-to-Target
- —Advanced SQL for reconciliation
- —Schema and constraint validation
- —Row-count, checksum, and transformation checks
Module III · Big Data Stack
- —Hadoop & HDFS fundamentals
- —Hive / Spark query patterns
- —Testing streaming pipelines (Kafka)
Module IV · Automation & Delivery
- —Python & Pytest for data tests
- —CI/CD with GitLab pipelines
- —Defect management & sign-off protocols
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