AWS-BDA

AWS-BDA - Big Data on AWS

In this course, you will learn about cloud-based Big Data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis, and the rest of the AWS Big Data platform. We will show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We will also teach you how to create Big Data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon QuickSight, Amazon Athena, and Amazon Kinesis, and leverage best practices to design Big Data environments for security and cost-effectiveness.

Durée: 3 Days

COURSE PROGRAM

In this course, you will learn about cloud-based Big Data solutions such as Amazon EMR, Amazon Redshift, Amazon Kinesis, and the rest of the AWS Big Data platform. We will show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We will also teach you how to create Big Data environments, work with Amazon DynamoDB, Amazon Redshift, Amazon QuickSight, Amazon Athena, and Amazon Kinesis, and leverage best practices to design Big Data environments for security and cost-effectiveness.

In this course, you will learn to:

  • Fit AWS solutions inside a Big Data ecosystem
  • Leverage Apache Hadoop in the context of Amazon EMR
  • Identify the components of an Amazon EMR cluster, then launch and configure an Amazon EMR cluster
  • Use common programming frameworks available for Amazon EMR, including Hive, Pig, and streaming
  • Improve the ease of use of Amazon EMR by using Hadoop User Experience (Hue)
  • Use in-memory analytics with Apache Spark on Amazon EMR
  • Choose appropriate AWS data storage options
  • Identify the benefits of using Amazon Kinesis for near real-time Big Data processing
  • Leverage Amazon Redshift to efficiently store and analyze data
  • Comprehend and manage costs and security for a Big Data solution
  • Identify options for ingesting, transferring, and compressing data
  • Leverage Amazon Athena for ad-hoc query analytics
  • Use AWS Glue to automate extract, transform, and load (ETL) workloads
  • Use visualization software to depict data and queries using Amazon QuickSight

This course is intended for:

  • Solutions architects
  • SysOps administrators
  • Data scientists
  • Data analysts
  • Familiarity with big data technologies, including Apache Hadoop and HDFS
  • Knowledge of big data technologies such as Pig, Hive, and MapReduce is helpful but not required
  • Working knowledge of core AWS services and public cloud implementation
  • Students should complete the AWS Essentials course or have equivalent experience
  • Basic understanding of data warehousing, relational database systems, and database design

1. Overview of Big Data

2. Data Ingestion, Transfer, and Compression

3. AWS Data Storage Options

4. Using DynamoDB with Amazon EMR

5. Using Kinesis for Near Real-Time Big Data Processing

6. Introduction to Apache Hadoop and Amazon EMR

7. Using Amazon Elastic MapReduce

8. The Hadoop Ecosystem

9. Using Hive for Advertising Analytics

10. Using Streaming for Life Sciences Analytics

11. Using Hue with Amazon EMR

12. Running Pig Scripts with Hue on Amazon EMR

13. Spark on Amazon EMR

14. Running Spark and Spark SQL Interactively on Amazon EMR

15. Using Spark and Spark SQL for In-Memory Analytics

16. Managing Amazon EMR Costs

17. Securing your Amazon EMR Deployments

18. Data Warehouses and Columnar Datastores

19. Introduction to Amazon Redshift

20. Optimizing Your Amazon Redshift Environment

21. The Big Data Ecosystem on AWS

22. Visualizing and Orchestrating Big Data

23. Using Tibco Spotfire to Visualize Big Data

Next Sessions

Actuellement aucunne session n'a été planifiée pour ce cour.