Best Data Engineering Course in Bengaluru

Program Highlights

• End-to-End Data Engineering
• Snowflake Cloud Data Warehouse
• PySpark Big Data Processing
• dbt Transformations
• Apache Airflow Workflow Orchestration
• ETL & ELT Pipelines
• Real-Time Data Processing
• Cloud & DevOps Basics
• Performance Optimization
• Capstone Project & Interview Preparation

Modules Covered

• Introduction to Data Engineering
• SQL for Data Engineering
• Python for Data Engineering
• Apache Spark & PySpark
• Advanced PySpark
• Snowflake Fundamentals
• Data Loading & Ingestion in Snowflake
• dbt (Data Build Tool)
• Apache Airflow
• PySpark + Snowflake Integration
• Advanced Snowflake Concepts
• Real-Time Data Engineering
• Cloud & DevOps Basics
• Data Modeling & Warehousing
• Performance Optimization
• Capstone Project

Tools & Technologies

• Python
• SQL
• PySpark
• Snowflake
• dbt
• Apache Airflow
• Kafka Basics
• Git & GitHub
• AWS / Azure
• VS Code
• Jupyter Notebook

Module 1: Introduction to Data Engineering

• What is Data Engineering?
• Role of Data Engineer
• Data Engineering Lifecycle
• OLTP vs OLAP
• Data Warehouse Concepts
• Data Lake vs Data Warehouse
• ETL vs ELT
• Batch vs Streaming

Module 2: SQL for Data Engineering

• Advanced SQL
• Joins & Subqueries
• CTEs
• Window Functions
• Analytical Functions
• Stored Procedures
• Views & Materialized Views
• Query Optimization
• Performance Tuning

Module 3: Python Basics for Data Engineering

• Python Fundamentals
• Data Types & Loops
• Functions
• File Handling
• Exception Handling
• Modules & Packages
• Working with APIs
• JSON Handling

Module 4: PySpark Fundamentals

• Introduction to Apache Spark
• Spark Architecture
• RDD vs DataFrame vs Dataset
• SparkSession
• Transformations & Actions
• Lazy Evaluation
• Spark SQL

Module 5: Advanced PySpark

• Data Cleaning
• Handling NULLs
• Window Functions
• UDFs
• Joins in PySpark
• Partitioning
• Caching & Persistence
• Performance Optimization
• Spark Streaming Basics

Module 6: Snowflake Fundamentals

• Snowflake Architecture
• Virtual Warehouses
• Databases, Schemas, Tables
• Micro-partitions
• Clustering
• Pruning
• Time Travel
• Zero Copy Cloning
• Secure Data Sharing

Module 7: Data Loading & Ingestion in Snowflake

• Internal & External Stages
• File Formats
• COPY INTO
• Snowpipe
• Incremental Loading
• Error Handling
• Loading Semi-Structured Data
• JSON & Parquet Processing

Module 8: dbt (Data Build Tool)

• Introduction to dbt
• dbt Architecture
• dbt Models
• Materializations
• Incremental Models
• Seeds & Snapshots
• dbt Tests
• dbt Macros
• Jinja Templates
• dbt Documentation
• dbt with Snowflake
• ELT using dbt

Hands-on

• Build dbt models
• Create reusable transformations
• Generate documentation
• Create testing pipelines

Module 9: PySpark + Snowflake Integration

• Snowflake Connector for Spark
• Reading Snowflake Data in PySpark
• Writing DataFrames to Snowflake
• ETL Workflows
• Data Migration Pipelines

Module 10: Advanced Snowflake for Data Engineering

• Streams
• Tasks
• Dynamic Tables
• Materialized Views
• CDC Pipelines
• Query Profile Analysis
• Warehouse Scaling
• Cost Optimization
• Security & Access Control

Module 11: Real-Time Data Engineering

• Streaming Concepts
• Kafka Basics
• Spark Streaming
• Near Real-Time Analytics
• Snowpipe Streaming
• CDC Concepts

Module 12: Cloud & DevOps Basics

• Azure/AWS Fundamentals
• Data Storage Services
• CI/CD Basics
• Git & GitHub
• Scheduling ETL Jobs
• Monitoring Pipelines

Module 14: Data Modeling

• Star Schema
• Snowflake Schema
• Fact & Dimension Tables
• Slowly Changing Dimensions (SCD)
• Data Mart Concepts

Module 14: Performance Optimization

• Spark Optimization
• Snowflake Query Tuning
• Clustering Strategies
• Partitioning Techniques
• File Size Optimization
• Caching Mechanisms

Interview Preparation

• SQL Interview Questions
• Snowflake Scenario Questions
• PySpark Coding Questions
• ETL Scenarios
• Real-Time Use Cases
• Resume Building
• Mock Interviews

Course Duration Options

Career Opportunities

• Data Engineer
• Snowflake Developer
• PySpark Developer
• Big Data Engineer
• Cloud Data Engineer
• ETL Developer
• Analytics Engineer