In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.

starstarstarstarstar_outline

* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

In this course, you will learn to:

Compare the features and benefits of data warehouses, data lakes, and modern data architectures
Design and implement a batch data analytics solution
Identify and apply appropriate techniques, including compression, to optimize data storage
Select and deploy appropriate options to ingest, transform, and store data
Choose the appropriate instance and node types, clusters, auto-scaling, and network topology for a particular business use case
Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
Secure data at rest and in transit
Monitor analytics workloads to identify and remediate problems
Apply cost management best practices

Course Info

Not Your Location? Change

Cost: $ 675

Length: 1.0 day (8 hours)

Level:

Next Available Classes:

Schedule select
07
Feb
Tuesday
9:00 AM ET -
5:00 PM ET
Almost Full
5 seat(s) left
Schedule select
07
Mar
Tuesday
9:00 AM ET -
5:00 PM ET
Almost Full
5 seat(s) left
Schedule select
04
Apr
Tuesday
9:00 AM ET -
5:00 PM ET
Almost Full
5 seat(s) left
Schedule select
02
May
Tuesday
9:00 AM ET -
5:00 PM ET
Almost Full
5 seat(s) left
Schedule select
31
May
Wednesday
9:00 AM ET -
5:00 PM ET
Almost Full
5 seat(s) left
Loading...