Course Overview
The Professional Data Engineering program by PyLoom Technologies is an intensive, practical, project-based training program designed to develop job-ready Data Engineering professionals with strong skills in modern data platforms and enterprise data architecture.
The program covers the complete Data Engineering lifecycle, beginning with advanced Python and SQL and progressing through data modeling, ETL/ELT, data warehousing, data lakes, distributed data processing, cloud data engineering, data pipelines, real-time streaming, data quality, orchestration, DevOps, and data architecture.
Participants will gain hands-on experience with industry-relevant technologies including Python, SQL, Apache Spark, PySpark, AWS, Snowflake, dbt, Apache Airflow, Apache Kafka, Docker, Git, CI/CD, and data quality tools.
The program emphasizes practical implementation rather than tool-based learning alone. Participants will build multiple projects throughout the training and progressively integrate their knowledge into production-style data pipelines.
The final stage includes an Enterprise Data Engineering Capstone Project, where participants design and implement an end-to-end data platform involving data ingestion, cloud storage, distributed processing, data warehousing, transformation, orchestration, data quality, and analytics.
The program is designed to prepare participants for professional Data Engineering roles in technology companies, financial institutions, telecom organizations, software companies, and other data-driven enterprises.
What You'll Learn
Learning outcomes:
By the end of this program, participants will be able to:
Develop robust data engineering applications using advanced Python.
Write complex, optimized SQL queries for large datasets.
Design and implement ETL and ELT pipelines.
Work with structured, semi-structured, and unstructured data.
Apply data modeling and data warehousing principles.
Design star schemas, fact tables, dimension tables, and data marts.
Build scalable data processing pipelines using Apache Spark and PySpark.
Understand distributed data processing and Spark performance optimization.
Build cloud-based data platforms using AWS.
Work with AWS S3, Glue, Athena, EMR, Redshift, Lambda, and CloudWatch.
Design and manage cloud data warehouses using Snowflake.
Build transformation workflows using dbt.
Develop and manage data pipelines using Apache Airflow.
Build real-time and event-driven data pipelines using Apache Kafka.
Work with streaming data using Spark Structured Streaming.
Implement data validation, testing, quality checks, and monitoring.
Apply Git, Docker, CI/CD, and DevOps practices to Data Engineering projects.
Design scalable, reliable, secure, and cost-effective data architectures.
Understand modern Data Lake, Data Warehouse, and Lakehouse architectures.
Understand the fundamentals of MLOps and ML data pipelines.
Apply Data Engineering techniques to large-scale business and telecom datasets.
Troubleshoot and optimize production-style data pipelines.
Develop an enterprise-grade Data Engineering project from end to end.
Build a professional portfolio demonstrating practical Data Engineering skills.
Prepare for technical interviews and professional Data Engineering roles.
Training Summary
A comprehensive, industry-oriented Data Engineering program designed to develop professionals capable of building, deploying, and managing scalable data platforms using Python, SQL, PySpark, AWS, Snowflake, dbt, Airflow, Kafka, Docker, CI/CD, and modern data architecture practices.
Delivery Mode
Live, Inperson & Hybrid
Class Schedule
2 Hrs Per Day
Certificate
Professional Certificate of Completion issued by PyLoom Technologies upon successful completion of the training and assessment.
