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Learn to master Databricks and Apache Spark through this comprehensive 13 hour 58 minute course that takes you step-by-step from basic concepts to advanced implementations. Begin by creating a Databricks workspace and understanding the data science process, then dive deep into Spark SQL including data definition language, table creation, views, joins, set operators, functions, and window functions. Progress to PySpark programming where you'll work with RDDs, DataFrames, user-defined functions (UDFs), pandas UDFs, and database connectivity for parallel queries. Explore the evolution from Koalas to Pandas on Spark for enhanced data manipulation capabilities. Expand your skillset by learning R integration with Spark through Arrow, SparkR, and sparklyr, then master Scala programming with RDDs, DataFrames, and Datasets. Gain practical experience through hands-on demonstrations, notebook exercises, and real-world examples that prepare you to leverage the full power of distributed computing for big data analytics and machine learning workflows.
