Data Transformation Using Spark

Overview Schedule Dates Course Content Faqs

Course Overview

The “Data Transformation Using Spark” course from Microsoft provides a comprehensive introduction to using Apache Spark for data transformation tasks. This course focuses on teaching participants how to efficiently process and transform large datasets using Spark’s powerful distributed computing capabilities.

Participants will learn how to leverage Spark’s core components, such as Spark SQL, DataFrames, and Datasets, to perform various data transformation operations. The course emphasizes practical, hands-on exercises to ensure that learners can apply these concepts in real-world scenarios. Key topics include the use of Spark’s built-in functions for data manipulation, optimization techniques for improving performance, and best practices for handling large-scale data transformations.

By the end of the course, participants will have a solid understanding of how to use Spark to streamline and enhance data transformation processes, making them better equipped to handle complex data workflows and contribute to data-driven decision-making in their organizations.

Learning Options

Online Instructor Led

Learn from the comforts of your home/office with our expert trainers via online platforms.

Classroom Training

Face-to-face physical classroom training with max interactions at our 5* training venues.

Onsite Training

Learn in your own environment from our expert trainers for maximum impact.

Overseas Training

Choose your desired country for learning with our expert trainers.

Schedule Dates

Data Transformation Using Spark

17 February 2025 - 20 February 2025

Data Transformation Using Spark

19 May 2025 - 22 May 2025

Data Transformation Using Spark

19 August 2025 - 22 August 2025

Data Transformation Using Spark

24 November 2025 - 27 November 2025

Course Content

Apache Spark overview
What is Apache Spark
Spark pool architecture
Apache Spark in Azure Synapse Analytics
Apache Spark on Azure Databricks

Spark SQL - Introduction
Features of Spark SQL
Spark SQL Architecture
Spark SQL - DataFrames

PySpark – Overview
Who uses PySpark?
Features of PySpark
Advantages of PySpark
PySpark Architecture
PySpark Modules & Packages
PySpark Installation
PySpark DataFrame

Overview of Modern Data Warehouse
Modern Date Warehouse Architecture
Dataflow in Modern Data Warehouse
Components of Modern Data Warehouse
Potential Use Cases

What is Databricks used for?
Common Use Cases for Databricks
Spark Pool Overview
Spark Instances

ETL using Azure Databricks
ETL using Apache Spark Pool

Reading data From CSV file
Reading data From JSON file
Reading data From Dedicated SQL Pool
Reading data From CosmosDB

Creating and using the Notebook in Databricks
Creating and using the Notebook in Apache Spark Pool
Using Python in Databrciks Notebook
Using SparkSQL in Databricks Notebook
Using Python in Apache Spark Pool Notebook
Using SparkSQL in Apache Spark Pool Notebook

Writing Data to File in Azure Data Lake
Writing Data to CosmosDB
Writing Data to Dedicated SQL Pool
Sending Data to ADF

Azure Synapse and PowerBI
Integration of PowerBI in Azure Synapse
PowerBI Service
PowerBI Data Refresh

FAQs

Basic knowledge of data processing and familiarity with Python programming are recommended. Prior experience with Spark or data engineering concepts will also be helpful.

The course is organized into several modules, including:

Introduction to Apache Spark: Overview of Spark’s functionality, architecture, and integration with cloud services.
Spark SQL: Working with structured data using Spark SQL.
PySpark: Understanding PySpark’s features and advantages.
Modern Data Warehouse: Architecture and data flow concepts.
Databricks and Spark Pools: Use cases and resource management.
ETL Processes: Implementing ETL processes and data transformation techniques.
BI Tool Integration: Consuming and integrating data using tools like PowerBI.

You will learn how to use Apache Spark and PySpark for big data processing, understand Spark SQL, manage data pipelines, implement ETL processes, and integrate data with BI tools for actionable insights.

Yes, the course includes practical hands-on labs and projects where you will apply the concepts learned to real-world scenarios, such as implementing ETL processes and working with data in notebooks.

Industries such as finance, healthcare, retail, and technology benefit from advanced data processing and transformation capabilities. This course helps organizations manage large datasets, optimize data workflows, and derive actionable insights.