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Azure Databricks (PySpark / Spark SQL) Training in Hyderabad (Ameerpet)

Unleash Scalable Data Analytics with Azure Databricks
Openminds Technologies offers a career-oriented Azure Databricks training that combines PySpark and Spark SQL for real-time big data processing on Azure. This course is perfect for aspiring Data Engineers, Cloud Analysts, and Big Data Developers looking to build powerful, cloud-native data pipelines.

Course Highlights

✅ Learn Apache Spark on Azure Databricks using PySpark & Spark SQL
✅ Build scalable ETL pipelines and perform big data analytics
✅ Hands-on real-time project with structured streaming & data engineering use cases
✅ Ideal for Data Engineers, Azure Developers, and Cloud Professionals
✅ 100% Placement Assistance with mock interviews & resume support

What You Will Learn

  • Introduction to Azure Databricks & Apache Spark

  • PySpark Essentials – RDDs, DataFrames, Transformations

  • Writing & Optimizing Queries using Spark SQL

  • Delta Lake & Structured Streaming Concepts

  • Integrating Databricks with Azure Data Lake & Blob Storage

  • Building End-to-End ETL Workflows

  • Real-Time Project Implementation with Live Scenarios

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SYLLABUS

  • What is Azure Databricks?

  • Features and Benefits

  • Use Cases in Real-World Projects

  • Architecture Overview

  • Setting up Azure Databricks Workspace

Download Syllabus

  • Introduction to Apache Spark

  • Spark Architecture and Components

  • Understanding RDDs (Resilient Distributed Datasets)

  • Transformations and Actions in Spark

  • Spark Execution Model

  • Introduction to PySpark

  • Working with DataFrames and Datasets

  • Reading and Writing Data (CSV, JSON, Parquet)

  • Data Cleaning and Manipulation using PySpark

  • Handling Missing Values and Data Aggregations

  • Introduction to Spark SQL

  • Creating and Querying Tables

  • Writing SQL Queries on Spark DataFrames

  • Joins, Aggregations, and Window Functions

  • Performance Tuning with Spark SQL

  • UDFs (User Defined Functions) in PySpark

  • Partitioning and Bucketing Strategies

  • Broadcast Joins and Optimizations

  • Working with Complex Data Types (Arrays, Structs)

  • Integrating Databricks with Azure Data Lake

  • Databricks and Azure Blob Storage Integration

  • Data Ingestion and Processing Pipelines

  • Building ETL Pipelines with Databricks

  • Connecting Databricks to Power BI for Visualization

  • Introduction to Delta Lake

  • ACID Transactions in Databricks

  • Managing Slowly Changing Dimensions (SCD)

  • Upserts and Time Travel in Delta Lake

  • Best Practices for Delta Architecture