Amazon Web Services: Data Analytics

Course details

Many modern organizations have a wealth of data that they can draw from to inform their decisions. But all of this information can't truly benefit a business unless the professionals working with that data can efficiently extract meaningful insights from it. Amazon Web Services (AWS) offers data scientists an array of tools and services that they can leverage to analyze data. In this course, learn about best practices, patterns, and tools for designing and implementing data analytics using AWS. Explore key analytics concepts, common methods of approaching analytics challenges, and how to work with services such as Athena, RDS, and QuickSight. Plus, discover how to visualize text-based data in a more visually intuitive way, use partner solutions for analytics from the AWS Marketplace, and more.

Learning objectives
Explain the difference between files and databases.
Identify examples of batching, micro-batching, and streaming.
Prepare helpful data visualizations with QuickSight.
Recognize the different types of analytics available in AWS.
Demonstrate how to set up AWS CLI.
Describe common analytics architecture patterns.

Concepts

0. Introduction

01 - Welcome
02 - Exercise files
03 - About using cloud services

1. Analytics on AWS

04 - AWS analytics design concepts
05 - Files vs. databases
06 - Business vs. predictive analytics
07 - Batching vs. streaming
08 - Which analytics type to use
09 - Data hygiene and ETL
10 - Visualization and QuickSight
11 - QuickSight demo

2. Analytic Services

12 - Setup for AWS analytics
13 - Query Athena using SQL query on S3
14 - Query DynamoDB for NoSQL
15 - Set up Kinesis for input streams
16 - Query Kinesis Analytics
17 - Query CloudSearch and Elasticsearch
18 - Query AWS IoT
19 - Set up EMR, RDS, and Redshift
20 - Query RDS with ANSI SQL
21 - Query Redshift for RDBMS
22 - Query Redshift Spectrum
23 - Query EMR with Apache Spark

3. AWS Code Tools for Analytics

24 - Set up AWS CLI for analytics
25 - Query Athena using the AWS CLI
26 - Query DynamoDB using the AWS CLI
27 - Code tools for analytics
28 - Use the AWS SDK for querying DynamoDB
29 - Using AWS Cloud9

4. Advanced Analytics

30 - Query AWS public datasets
31 - Use AWS Glue for ETL
32 - Understanding ETL options
33 - Use AWS QuickSight for visualizations
34 - Use the AWS Marketplace for visualization tools
35 - Summary of tools
36 - Common analytics architecture patterns

Conclusion

37 - Next steps

Amazon Web Services: Data Analytics

Authors

Lynn Langit