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.