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Get to know the fundamentals to do Big Data on AWS
In this training, 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.
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What you'll learn
Perfect for the techies
- The fundamentals of the tools AWS offers to process, analyze, and visualize Big Data
- How to use Amazon EMR to process data through Hadoop, Spark, and Hive
- How DynamoDB, Redshift, and Athena can handle your data scaling as you go
- The best practice to design environments for security and cost-effectiveness
The schedule
Navigating the Big Data Landscape
- Fit AWS solutions inside a Big Data ecosystem
- Choose appropriate AWS data storage options
- Comprehend and manage costs and security for a Big Data solution
- Identify options for ingesting, transferring, and compressing data
Elastic Map Reduce
- 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
Learn the Ins and Outs of Various Big Data Services
- Identify the benefits of using Amazon Kinesis for near real-time Big Data processing
- Leverage Amazon Redshift to efficiently store and analyze 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