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Learn how to process big-data using Databricks & Apache Spark 2.4 and 3.0.0 - DataFrame API and Spark SQL
What you'll learn:- Databricks
- Apache Spark Architecture
- Apache Spark DataFrame API
- Apache Spark SQL
- Selecting, and manipulating columns of a DataFrame
- Filtering, dropping, sorting rows of a DataFrame
- Joining, reading, writing and partitioning DataFrames
- Aggregating DataFrames rows
- Working with User Defined Functions
- Use the DataFrameWriter API
Welcome to this course on Databricks and Apache Spark 2.4 and 3.0.0
Apache Spark is a Big Data Processing Framework that runs at scale.
In this course, we will learn how to write Spark Applications using Scala and SQL.Databricks is a company founded by the creator of Apache Spark.
Databricks offers a managed and optimized version of Apache Spark that runs in the cloud.The main focus of this course is to teach you how to use the DataFrame API & SQL to accomplish tasks such as:
Write and run Apache Spark code using Databricks
Read and Write Data from the Databricks File System - DBFS
Explain how Apache Spark runs on a cluster with multiple Nodes
Use the DataFrame API and SQL to perform data manipulation tasks such as
Selecting, renaming and manipulating columns
Filtering, dropping and aggregating rows
Joining DataFrames
Create UDFs and use them with DataFrame API or Spark SQL
Writing DataFrames to external storage systems
List and explain the element of Apache Spark execution hierarchy such as
Jobs
Stages
Tasks
Overview
