Amazon Web Services: Data Services

Course details

Why use Amazon Web Services for data storage? AWS provides big data services at a small cost, offering one of the most full-featured and scalable solution sets around. Start here to explore your storage and framework options when working with data services on the Amazon cloud. Your guide, Lynn Langit, a working big data architect, helps you navigate the options when it comes to file storage, relational and NoSQL data storage, data warehousing, graph and ledger databases, and data lakes. Plus, learn how to run open-source processing tools such as Hadoop and Spark on AWS and leverage new serverless data services, including Athena serverless queries and the auto-scaling version of the Aurora relational database service, Aurora Serverless.

Topics include:
- Benefits of AWS
- File storage with S3, Glacier, EBS, and EFS
- Uploading files to AWS
- Exploring AWS Relational Database Service (RDS)
- Using the RDS Query Editor
- Creating NoSQL databases with AWS DynamoDB
- Creating data warehouses with AWS Redshift
- Creating graph databases with AWS Neptune
- Processing big data with Hadoop, Spark, and AWS EMR
- Analyzing big data with Athena queries
- Preparing and loading data for analytics with AWS Glue
- Setting up a secure data lake with AWS Lake Formation

Concepts

0. Introduction

01 - Scalable data solutions on AWS
02 - What you should know
03 - Use AWS Cloud services

1. AWS Data Services Overview

04 - Use Amazon Web Services (AWS) data services
05 - Why use AWS data storage services
06 - Explore file and HDFS storage
07 - Explore AWS data storage
08 - Understand why AWS Cloud tools matter

2. Storage and Files with S3 and More

09 - Explore the core file storage choices
10 - Explore AWS S3
11 - Explore AWS S3 using AWS CLI
12 - Explore AWS Glacier storage
13 - Explore AWS EBS and EFS
14 - Understand file import dervices for AWS and compare file services
15 - Scenario - Use cloud file storage

3. SQL Databases with RDS

16 - Explore AWS RDS for managed RDBMS
17 - Explore AWS RDS MySQL
18 - Explore AWS RDS Aurora Serverless
19 - Use RDS query editor
20 - Scenario - Move on-premises relational data to AWS

4. NoSQL Databases with DynamoDB and More

21 - Explore NoSQL options in AWS
22 - Understand AWS ElastiCache
23 - Explore AWS DynamoDB
24 - Use AWS CLI with AWS DynamoDB
25 - Explore AWS DocumentDB
26 - Scenario - Work with caching and real-time data

5. Data Warehouse and Pipelines with Redshift

27 - Explore the AWS data warehouse options and Redshift
28 - Work with AWS Redshift
29 - Connect to AWS Redshift with AWS query editor
30 - Visualize data with AWS QuickSight
31 - Scenario - Build a data warehouse in the cloud

6. Graph Databases and Ledgers with Neptune DB or QLDB

32 - Explore AWS specialty databases including graph and ledger
33 - Explore graphs with AWS Neptune
34 - Explore ledgers with AWS QLDB
35 - Explore event databases including Amazon Timestream

7. Hadoop and Spark with EMR

36 - Explore Hadoop and Spark on AWS
37 - Understand Hadoop jobs and libraries
38 - Explore AWS EMR with Hadoop and Spark
39 - Run Spark job on a Jupyter Notebook on AWS EMR

8. Data Lake with AWS Services

40 - Understand a data lake pattern with AWS Lake Formation
41 - Explore AWS Athena
42 - Explore AWS Glue Data Catalog and Crawlers
43 - Use AWS Glue for ETL
44 - Explore a data lake pattern with AWS Lake Formation
45 - Scenario - Build for the Internet of Things with Hadoop

Conclusion

46 - Keep learning AWS services