Data Engineering Training
Learn how to design, construct, and manage scalable data pipelines. Master advanced SQL, Python data automation, ETL/ELT architecture, cloud data warehouses, and dimensional modeling for enterprise analytics.
Program Snapshot
- Learning Level: Intermediate to Production-Ready
- Delivery Modes: Classroom (Hyderabad) & Live Online
- Practical Emphasis: End-to-End Automated Cloud Pipeline
- Target Roles: Data Engineer, ETL Developer, DW Specialist
The Backbone of Modern Data Platforms
Learn the foundational engineering skills required to move, transform, and store high-volume data reliably.
Advanced SQL & Dimensional Modeling
Master window functions, CTEs, partition keys, clustering strategies, star schemas, snowflake schemas, and slowly changing dimensions (SCD).
Python Data Automation
Write robust scripts to ingest JSON APIs, parse raw files, execute schema validations, handle retries, and automate batch ingestion tasks.
Cloud Data Warehouses
Work with modern cloud columnar stores (Google Cloud BigQuery / Snowflake concepts) for high-performance analytical query processing.
Data Engineering Curriculum
A rigorous, practical curriculum covering ingestion, transformation, storage, and orchestration.
Advanced SQL for Data Engineering
Deep dive into complex joins, analytical window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD), common table expressions (CTEs), indexing, and execution query plan analysis.
Python for Data Processing & ETL
Using Python for data extraction: REST APIs, file handling (CSV, JSON, Parquet), Pandas DataFrame transformations, error logging, and data cleansing routines.
Data Warehousing & Dimensional Modeling
Kimball methodology: Fact tables vs. Dimension tables, Star schema design, Snowflake schema design, Granularity definitions, and Slowly Changing Dimensions (SCD Type 1 & Type 2).
Cloud Data Platforms & Partitioning
Cloud data storage architectures, Google Cloud BigQuery architecture, table partitioning by date/time, clustering keys for query cost optimization, and external tables.
Pipeline Orchestration Concepts & Data Quality
Directed Acyclic Graphs (DAGs), task dependencies, idempotent pipeline design, automated data freshness checks, and schema validation gates.
Applied Practical Scenario: End-to-End Cloud ETL
Author a multi-stage data ingestion pipeline: fetch mock transactional streams, validate anomalies, apply business transformations, and populate partitioned analytical tables.
What You Build in the Data Engineering Labs
You will construct functional, automated data flows that you can explain with technical depth during enterprise interviews.
- Automated Python API Ingestion Scripts with Exception Handling
- Star Schema SQL Architecture with Surrogate Keys
- Partitioned & Clustered Cloud Data Warehouse Tables
- Data Quality Validation Playbook & Pipeline Test Suite
Request Data Engineering Program Details
Speak with a data mentor regarding prerequisites and upcoming batch schedules.