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100 XP
Azure Databricks offers a highly scalable platform for data analytics and processing using Apache Spark.
Spark is a flexible platform that supports many different programming languages and APIs. By setting up a Databricks workspace and deploying Spark clusters, users can easily ingest data from various sources like Azure Data Lake or Cosmos DB into Spark DataFrames. Within the interactive Databricks notebooks, users can perform complex data transformations using Spark’s DataFrame API, which includes operations like filtering, grouping, and aggregation. Most data processing and analytics tasks can be accomplished using the Dataframe API, which is what we'll focus on in this module.
In this module, you'll learn how to:
- Describe key elements of the Apache Spark architecture.
- Create and configure a Spark cluster.
- Describe use cases for Spark.
- Use Spark to process and analyze data stored in files.
- Use Spark to visualize data.