Unlock the power of Amazon EMR to build robust batch data analytics solutions tailored for data platform engineers and architects, leveraging tools like Apache Spark and Hadoop for optimized performance and cost efficiency.
EnrollDesign and implement a batch data analytics solution
Optimize data storage and processing techniques
Secure, monitor, and troubleshoot analytics workloads on Amazon EMR
Apply cost and performance management best practices
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.
Data analytics use cases
Using the data pipeline for analytics
Using Amazon EMR in analytics solutions
Amazon EMR cluster architecture
Interactive Demo 1: Launching an Amazon EMR cluster
Cost management strategies
Storage optimization with Amazon EMR
Data ingestion techniques
Apache Spark on Amazon EMR use cases
Why Apache Spark on Amazon EMR
Spark concepts
Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell
Transformation, processing, and analytics
Using notebooks with Amazon EMR
Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR
Using Amazon EMR with Hive to process batch data
Transformation, processing, and analytics
Practice Lab 2: Batch data processing using Amazon EMR with Hive
Introduction to Apache HBase on Amazon EMR
Serverless data processing, transformation, and analytics
Using AWS Glue with Amazon EMR workloads
Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions
Securing EMR clusters
Interactive Demo 3: Client-side encryption with EMRFS
Monitoring and troubleshooting Amazon EMR clusters
Demo: Reviewing Apache Spark cluster history
Batch data analytics use cases
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Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier
Aaron Steele
Casey Pense
Chris Tsantiris
Javier Martin
Justin Gilley
Kathy Le
Kelson Smith
Oussama Azzam
Pascal Rodmacq
Randall Granier