Data Engineering
Build reliable data pipelines and platforms with SQL, Spark, Kafka, Airflow, cloud data services and Snowflake.
What your team will learn
SQL & Advanced Databases
Master complex queries, window functions, indexing, and database optimization techniques.
Python for Data Engineering
Write robust Python scripts for data extraction, transformation, and automation.
Linux & Shell Scripting
Navigate Linux environments and automate server tasks using Bash scripting.
Data Warehousing
Design dimensional models, star schemas, and understand OLAP vs OLTP systems.
ETL Development
Build Extract, Transform, Load pipelines to move data reliably between systems.
Big Data Ecosystem
Understand Hadoop architecture, HDFS, and distributed computing principles.
Hadoop & Spark
Process massive datasets in-memory using Apache Spark and PySpark.
Kafka Streaming
Build real-time event streaming pipelines using Apache Kafka.
Program curriculum
17 modules. Modules and depth can be customised for your team.
01Programming Fundamentals with Python
- Python Basics & Data Structures
- OOP Concepts
- File Handling & Exception Handling
- Pandas & NumPy for Data Manipulation
02Linux & Shell Scripting
- Linux File System & Commands
- User & Permission Management
- Bash Scripting Basics
- Cron Jobs & Automation
03SQL & Advanced Databases
- RDBMS Concepts
- Complex Joins & Subqueries
- Window Functions & CTEs
- Query Optimization & Indexing
04Data Warehousing Concepts
- OLTP vs OLAP
- Dimensional Modeling (Star/Snowflake)
- Slowly Changing Dimensions (SCD)
- Fact & Dimension Tables
05ETL & Data Integration
- ETL vs ELT Architecture
- Data Extraction Techniques
- Data Transformation & Cleansing
- Loading Strategies
06Big Data Fundamentals
- Introduction to Big Data (5 Vs)
- Hadoop Ecosystem Overview
- HDFS Architecture
- MapReduce Concepts
07Apache Spark
- Spark Architecture & RDDs
- Spark SQL & DataFrames
- PySpark Programming
- Spark Performance Tuning
08Kafka & Real-Time Streaming
- Kafka Architecture (Brokers, Topics, Partitions)
- Producers & Consumers
- Kafka Streams
- Real-Time Data Processing
09Apache Airflow
- Airflow Architecture
- Creating DAGs
- Operators & Tasks
- Scheduling & Monitoring Workflows
10Cloud Data Engineering AWS
- AWS IAM & Security
- Amazon S3 Data Lake
- AWS Glue (ETL)
- Amazon Redshift & Athena
11Azure Data Engineering
- Azure Storage Solutions
- Azure Data Factory (ADF)
- Azure Databricks
- Azure Synapse Analytics
12Snowflake Data Warehouse
- Snowflake Architecture
- Virtual Warehouses
- Data Loading (Snowpipe)
- Time Travel & Zero-Copy Cloning
13Data Lake & Lakehouse Architecture
- Data Lake Concepts
- Delta Lake Architecture
- Apache Hudi & Iceberg
- Managing ACID Transactions on Data Lakes
14DevOps for Data Engineers
- Git & Version Control
- Docker Containerization
- Kubernetes Basics
- CI/CD Pipelines for Data
15Data Governance & Security
- Data Quality Management
- Data Lineage & Cataloging
- Role-Based Access Control (RBAC)
- Compliance (GDPR/CCPA)
16AI & Modern Data Engineering
- Using LLMs for Code Generation
- AI-Assisted Query Optimization
- Automated Data Profiling
- Future Trends in Data Engineering
17Real-Time Industry Projects
- Requirement Gathering
- Architecture Design
- Implementation & Testing
- Deployment & Monitoring
Project work
Participants apply what they learn by building practical, real-world projects such as:
End-to-End Data Pipeline Project
A comprehensive batch processing pipeline extracting data from APIs, transforming it using PySpark, and loading it into a cloud data warehouse.
Banking Analytics Pipeline
Processing millions of transaction records to identify patterns, calculate daily balances, and flag suspicious activities.
E-Commerce Data Platform
Building a dimensional model for an e-commerce company to analyze sales, inventory, and customer behavior.
IoT Streaming Platform
Real-time processing of sensor data from IoT devices to monitor equipment health and trigger alerts.
Cloud Data Warehouse Project
Migrating legacy on-premise data to Snowflake, utilizing Snowpipe for continuous data ingestion.
Who should attend
Frequently asked questions
Is Coding Mandatory?
Yes, Data Engineering requires coding. You will need to write scripts in Python and complex queries in SQL. However, we teach both from scratch.
Will Cloud Technologies Be Covered?
Absolutely. Modern Data Engineering relies heavily on the cloud. We cover AWS (S3, Redshift, Glue), Azure (Data Factory, Databricks), and Snowflake extensively.
Are Real-Time Projects Included?
Yes, the curriculum includes 5 major end-to-end projects that simulate real-world corporate data pipelines.
Is Spark Covered with PySpark?
Yes, Apache Spark is a core component, and we focus on PySpark as it is the industry standard for big data processing.
Can this program be customised for our team?
Yes. We tailor the modules, depth, examples and hands-on projects to your team's current skills, technology stack and business goals.
Where is the training delivered?
On-site at your office or campus, live online for distributed teams, or at our training centre in Nagole, Hyderabad, whichever suits your team best.
How long does the program take?
Duration depends on the modules you choose and your team's availability. Our training proposal includes a detailed schedule with hours, timeline and session plan.
Do participants receive a certificate?
Yes. Participants who complete the program receive a PRR Technologies training completion certificate.
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