Apache Spark Essential Training
1h 27mIntermediate2017-04-03
Authors
Ben Sullins
Data Geek, Tech Consultant
Course details
Apache Spark is a powerful platform that provides users with new ways to store and make use of big data. In this course, get up to speed with Spark, and discover how to leverage this popular processing engine to deliver effective and comprehensive insights into your data. Instructor Ben Sullins provides an overview of the platform, going into the different components that make up Apache Spark. He shows how to analyze data in Spark using PySpark and Spark SQL, explores running machine learning algorithms using MLib, demonstrates how to create a streaming analytics application using Spark Streaming, and more.
Learning objectives
Understanding Spark
Reviewing Spark components
Where Spark shines
Understanding data interfaces
Working with text files
Loading CSV data into DataFrames
Using Spark SQL to analyze data
Running machine learning algorithms using MLib
Querying streaming data
Connecting BI tools to Spark
Learning objectives
Understanding Spark
Reviewing Spark components
Where Spark shines
Understanding data interfaces
Working with text files
Loading CSV data into DataFrames
Using Spark SQL to analyze data
Running machine learning algorithms using MLib
Querying streaming data
Connecting BI tools to Spark
Skills covered
Apache SparkApacheData EngineeringData Science
Concepts
0. Introduction
- 01 - Welcome
- 02 - What you should know before watching this course
- 03 - Using the exercise files
1. Introducing Apache Spark
- 04 - Understanding Spark
- 05 - Origins of Spark
- 06 - Overview of Spark components
- 07 - Where Spark shines
- 08 - Overview of Databricks
- 09 - Introduction to notebooks and PySpark
2. Analyzing Data in Spark
- 10 - Understanding data interfaces
- 11 - Working with text files
- 12 - Loading CSV data into DataFrames
- 13 - Exploring data in DataFrames
- 14 - Saving your results
3. Using Spark SQL to Analyze Data
- 15 - Creating tables
- 16 - Querying data with Spark SQL
- 17 - Visualizing data in Databricks notebooks
4. Running Machine Learning Algorithms Using MLlib
- 18 - Introduction to machine learning with Spark
- 19 - Preparing data for machine learning
- 20 - Building a linear regression model
- 21 - Evaluating a linear regression model
- 22 - Visualizing a linear regression model
5. Real-Time Data Analysis with Spark Streaming
- 23 - Introduction to streaming analytics
- 24 - Streaming context setup
- 25 - Querying streaming data
6. Connecting BI Tools to Spark
- 26 - Setting up spark locally
- 27 - Connecting Jupyter notebooks to Spark
- 28 - Other connection options
Conclusion
- 29 - Next steps
Related courses
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- Apache Spark Essential Training: Big Data Engineering
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- Apache Spark Deep Learning Essential Training
- Scala Essential Training for Data Science
- PySpark Essential Training: Introduction to Building Data Pipelines
- Essentials of MLOps with Azure: 1 Introduction
- Essentials of MLOps with Azure: 4 Spark MLflow Models and Model Registry
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