Data Transformation Using Spark Course Overview

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.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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Course Prerequisites

To successfully undertake the "Data Transformation Using Spark" course, students should possess the following minimum prerequisites:


  • Basic understanding of data processing and data warehouse concepts.
  • Familiarity with SQL and relational databases.
  • Fundamental knowledge of programming, preferably in Python or Scala, as these are commonly used with Spark.
  • An introductory level of knowledge in big data concepts and distributed computing.
  • Comfort with using command-line interfaces and development environments.
  • Access to a computer with an internet connection to work on cloud-based platforms like Azure Synapse Analytics and Azure Databricks.

Please note that while the course will cover introductory aspects of Apache Spark and its ecosystem, having these prerequisites will enable students to grasp the concepts more effectively and apply them in practical scenarios.


Target Audience for Data Transformation Using Spark

  1. This course provides comprehensive training on Spark for data transformation, targeting IT professionals involved in data analytics and engineering.


  2. 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.