This hands-on self-paced training course targets Analysts and Data Scientists getting started using Databricks to analyze big data with Apache Spark™ SQL. The course ends with a capstone project demonstrating Exploratory Data Analysis with Spark SQL on Databricks.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.

In this course data engineers access data where it lives and then apply data extraction best practices, including schemas, corrupt record handling, and parallelized code. By the end of this course, you will extract data from multiple sources, use schema inference and apply user-defined schemas, and navigate Azure Databricks and Apache Spark™ documents to source solutions.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.

In this course data engineers apply data transformation and writing best practices such as user-defined functions, join optimizations, and parallel database writes. By the end of this course, you will transform complex data with custom functions, load it into a target database, and navigate Databricks and Spark documents to source solutions.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.

In this course data engineers optimize and automate Extract, Transform, Load (ETL) workloads using stream processing, job recovery strategies, and automation strategies like REST API integration. By the end of this course you will schedule highly optimized and robust ETL jobs, debugging problems along the way.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.

This hands-on self-paced training course targets Data Engineers, Data Scientists and Data Analysts who want to use Managed Delta Lake for ETL processing on data lakes. The course ends with a capstone project building a complete data pipeline using Managed Delta Lake.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.

This hands-on self-paced training course targets Data Engineers who want to process big data using Apache Spark™ Structured Streaming. The course ends with a capstone project building a complete data streaming pipeline using structured streaming.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.

In this course data scientists and data engineers learn the best practices for managing experiments, projects, and models using MLflow. By the end of this course, you will have built a pipeline to log and deploy machine learning models using the environment they were trained with.

NOTE: This course is specific to the Databricks Unified Analytics Platform (based on Apache Spark™). While you might find it helpful for learning how to use Apache Spark in other environments, it does not teach you how to use Apache Spark in those environments.