Big Data Analytics on AWS Specialization

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$499

Adnan Hassan

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Course Details

This specialized course introduces you to cloud-based architecture and design patterns to build highly scalable big data solutions using Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis, Amazon Glue, Amazon Athena, Data Lake, Elastic Search and the rest of the AWS big data platform.

Through this course, you learn how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue.

You gain a deeper understanding of 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.

This course will be 60% workshop driven with hands-on labs for various big data platform services.

Week	Content
Week0	Pre-requisites
Module1 Introduction to AWS Analytics	Overview of Big Data Big data ingestion, processing and analytics Big data streaming and Amazon Kinesis Amazon EMR and Hadoop Amazon EMR and Spark AWS Glue Amazon Redshift AWS Lake Formation Amazon QuickSight
Module2 Deep dive into EMR Day1-Material Day2-Lab	Using Amazon Elastic MapReduce Storing and Querying Data on DynamoDB Hadoop Programming Frameworks Processing Server Logs with Hive on Amazon EMR Streamlining Your Amazon EMR Experience with Hue Running Pig Scripts in Hue on Amazon EMR Spark on Amazon EMR Processing New York Taxi dataset using Spark on Amazon EMR
Module3 Deep dive - AWS Glue/Athena/Lake Formation Day1-Material Day2-Lab	Using AWS Glue to automate ETL workloads Amazon Athena and Big Data Visualizing and Orchestrating Big Data Visualizing - QuickSight Managing Amazon EMR Costs Securing Big Data solutions Big Data Design Patterns Amazon Lake Formation
Module4 Date Warehousing Deep Dive Day1-Material Day2-Lab	Evaluate the relationship between Amazon Redshift and other Big Data systems Evaluate use cases for data warehousing workloads and review real-world implementation of AWS data and analytic services as part of a data warehousing solution Understand which security features are appropriate for Amazon Redshift, such as encryption, IAM permissions, and database permissions Design the data warehouse to make effective use of compression, data distribution, and sort methods Review Redshift spectrum and materialized views Load and unload data and perform data maintenance tasks Write queries and evaluate query plans to optimize query performance Configure the database to allocate resources such as memory to query queues and define criteria to route certain types of queries to your configured query queues for improved processing Audit, monitor, and receive event notifications about activities in the data warehouse by using features and services such as Amazon Redshift database audit logging, Amazon CloudTrail, Amazon CloudWatch, and Amazon Simple Notification Service (Amazon SNS) Prepare for operational tasks such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters
Module5 Streaming Day1 - Workshop	Amazon Kinesis Analytics deep dive to understand how to query streaming data or build entire streaming applications using SQL. In this course, we discuss how the service collects, processes and analyzes streaming data in real-time. We also discuss how to use and monitor Amazon Kinesis Analytics and explore use cases. Review Amazon Kinesis firehose loading into S3 pattern Amazon Kinesis stream review
Module 6 Elastic Search Day1 - Workshop	Deep dive into Amazon Elastic search Components of Elastic search Review architecture and integration patterns.
Capstone week	Make it real project