Last Updated : 09 Jan 2025

Data Transformation Using Spark Course Overview

The Data Transformation Using Spark course offers a comprehensive dive into leveraging Apache Spark for processing large datasets efficiently. It begins with an Apache Spark overview, highlighting its functionality, architecture, and integration with cloud services like Azure Synapse Analytics and Azure Databricks.

Learners will gain proficiency in Spark SQL for interacting with structured data and understanding Spark SQL's features and architecture. The course also covers PySpark, detailing its features, advantages, and architecture, which is especially relevant for Python developers working with Spark.

The curriculum delves into the Modern Data Warehouse concept, emphasizing its architecture and data flow, then explores Databricks and Apache Spark Pools, including their use cases and resource management.

Practical lessons on implementing ETL processes, reading and writing data from various sources to different destinations using notebooks, and data transformation techniques are integral parts of the course. Finally, it demonstrates how to consume data using BI tools like PowerBI, integrating and refreshing data within Azure Synapse.

This course is designed to equip learners with the skills to harness Spark's power for big data challenges, leading to insights that drive business decisions.

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Flexi Video	16,449
Official E-coursebook
Exam Voucher (optional)
Hands-On-Labs²	4,159
+ GST 18%	4,259
Total Fees (without exam & Labs)	22,359 (INR)
Total Fees (with Labs)	28,359 (INR)

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Schedule personalized sessions based upon your availability.

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Learning without limits. Create custom courses that fit your exact needs, from blended topics to brand-new content.

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Experience exceptional training with the confidence of our Happiness Guarantee, ensuring your satisfaction or a full refund.

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Immerse yourself in a focused learning environment, free from distractions, where you can sharpen your skills in popular global destinations.

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Target Audience for Data Transformation Using Spark

This course provides comprehensive training on Spark for data transformation, targeting IT professionals involved in data analytics and engineering.
Target Audience for "Data Transformation Using Spark" Course:

Data Engineers
Data Scientists
Data Analysts
BI (Business Intelligence) Developers
Software Developers with a focus on big data processing
IT Professionals working with big data ecosystems
Database Administrators looking to expand their skillset into big data
Cloud Solution Architects
System Administrators managing big data platforms
Technical Project Managers overseeing data projects
Professionals seeking to understand modern data warehouse concepts
Individuals aiming to specialize in ETL (Extract, Transform, Load) processes
DevOps Engineers involved in data pipelines and analytics workflows
AI and Machine Learning Engineers requiring data processing capabilities

Learning Objectives - What you will Learn in this Data Transformation Using Spark?

Introduction to the Course's Mentioned Learning Outcomes and Concepts Covered:

In this course, students will master data transformation techniques using Apache Spark and its ecosystem, including PySpark, Spark SQL, and Databricks, with practical applications in modern data warehouse solutions.

Learning Objectives and Outcomes:

Gain a comprehensive understanding of Apache Spark and its role in big data processing.
Learn about Spark's architecture and how it integrates with Azure Synapse Analytics and Azure Databricks.
Acquire the ability to perform data transformations and analysis using Spark SQL and DataFrames.
Understand the architecture and features of PySpark, and how to install and use it effectively for data processing.
Explore the structure and components of a modern data warehouse and how Spark fits into this architecture.
Develop skills to implement ETL (Extract, Transform, Load) processes using Azure Databricks and Apache Spark pools.
Learn how to read and ingest data from various sources like CSV, JSON, SQL pools, and CosmosDB using Spark notebooks.
Master data transformation techniques within Databricks and Apache Spark pools using both Python and SparkSQL.
Obtain the skills to write and output transformed data to multiple destinations, including Azure Data Lake, CosmosDB, and SQL pools.
Discover how to consume and visualize transformed data using BI tools like Azure Synapse Analytics and PowerBI, including data refresh practices.