Databricks Fundamentals & Apache Spark Core
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
Requirements
- Basic Scala knowledge
- Basic SQL knowledge
Description
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
Who this course is for:
- Software developers curious about big-data, data engeneering and data science
- Beginner data engineer who want to learn how to do work with databricks
- Beginner data scientist who want to learn how to do work with databricks
