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Data Transformation Using Spark

4.63(2,365 Ratings)
Duration
4 Days
Upcoming Batch
14 December 2026 - 17 December 2026
Batch Options
8 hours & 4 hours
Language
English / Arabic

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.

Course Content

10 modules · 46 topics
01Module 1:Introduction to SPARK 5 topics
  • Apache Spark overview
  • What is Apache Spark
  • Spark pool architecture
  • Apache Spark in Azure Synapse Analytics
  • Apache Spark on Azure Databricks
02Module 2: Introduction to SPARK SQL4 topics
  • Spark SQL - Introduction
  • Features of Spark SQL
  • Spark SQL Architecture
  • Spark SQL - DataFrames
03Module 3: Introduction to SPARK PYTHON 8 topics
  • PySpark – Overview
  • Who uses PySpark?
  • Features of PySpark
  • Advantages of PySpark
  • PySpark Architecture
  • PySpark Modules & Packages
  • PySpark Installation
  • PySpark DataFrame
04Module 4: Introduction to Modern Data Warehouse 5 topics
  • Overview of Modern Data Warehouse
  • Modern Date Warehouse Architecture
  • Dataflow in Modern Data Warehouse
  • Components of Modern Data Warehouse
  • Potential Use Cases
05Module 5: Introduction to DATABRICKS / Apache Spark Pool 4 topics
  • What is Databricks used for?
  • Common Use Cases for Databricks
  • Spark Pool Overview
  • Spark Instances
06Module 6: Implementing SPARK with DATABRICKS/Apache Spark Pool2 topics
  • ETL using Azure Databricks
  • ETL using Apache Spark Pool
07Module 7: Reading the Data Using Notebook from diff sources 4 topics
  • Reading data From CSV file
  • Reading data From JSON file
  • Reading data From Dedicated SQL Pool
  • Reading data From CosmosDB
08Module 8: Data Transformation Using Databricks 6 topics
  • 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
09Module 9: Writing the Data Using Notebook to different destinations4 topics
  • Writing Data to File in Azure Data Lake
  • Writing Data to CosmosDB
  • Writing Data to Dedicated SQL Pool
  • Sending Data to ADF
10Module 10: Consuming Data Using BI Tool 4 topics
  • Azure Synapse and PowerBI
  • Integration of PowerBI in Azure Synapse
  • PowerBI Service
  • PowerBI Data Refresh

Schedule Dates

4 upcoming batches
Session Type
Physical sessions run at our training facility.
Data Transformation Using Spark
Batch DatesDurationBatch OptionsLanguageAction
14 December 2026 - 17 December 2026Next4 Days8 hours & 4 hoursEnglish / Arabic
15 March 2027 - 18 March 20274 Days8 hours & 4 hoursEnglish / Arabic
21 June 2027 - 24 June 20274 Days8 hours & 4 hoursEnglish / Arabic
27 September 2027 - 30 September 20274 Days8 hours & 4 hoursEnglish / Arabic

Can’t find a suitable date? Request a schedule that fits your team.

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FAQs

What are the prerequisites for this course?

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

How is the course structured?

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.
What will I learn from this course?

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.

Are there any hands-on projects in this course?

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.

What industries benefit from this course?

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.

How We Deliver

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Highlights

Live SessionsRecorded AccessGlobal

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Upcoming Batch
14 December 2026 - 17 December 2026