Core foundations
SQL, Python, data modeling, file formats, data quality, batch processing, and problem solving.
Course curriculum
Modern data engineering skills for cloud, AI, and analytics teams.
The curriculum covers foundations, platform tools, cloud workflows, project practice, and career preparation so learners can connect concepts to real work.
Skill stack
SQL Python Spark Cloud
Topics covered
A balanced path from fundamentals to advanced platform practice.
Big data tools
Spark, Hadoop, Hive, Sqoop, Kafka, NoSQL, and scalable data pipeline patterns.
Cloud platforms
AWS, Microsoft Azure, GCP, Snowflake, Redshift, and Databricks workflows.
Team tools
Jira, ServiceNow, Confluence, GitHub, Bitbucket, release basics, and documentation practice.
AI readiness
How clean pipelines, trusted data, monitoring, and analytics support AI use cases.
Career practice
Project explanation, resume preparation, mock interviews, and job search guidance.
Pipeline thinking
Ingest, transform, validate, store, and serve trusted data.
Cloud scale
Practice services and patterns used by modern data teams.
Interview value