Data Lake Sources, Visualizations, & ETL Operations

Amazon Web Services 2019
  • 13 Videos | 1h 33m 23s
  • Includes Assessment
  • Earns a Badge
Likes 27 Likes 27
This course discusses the transition of data warehousing to cloud-based solutions using the AWS (Amazon Web Services) cloud platform. You will explore Amazon Redshift, a fully managed petabyte-scale data warehouse service which forms part of the larger AWS cloud-computing platform. The 12-video course demonstrates how to create and configure an Amazon Redshift cluster; to load data into it from an S3 (simple storage service) bucket; and configure a Glue crawler for stored data. This course examines how to visualize the data stored in the data lake and how to perform ETL (extract, transform, load) operations on the data using Glue scripts. You will work with the DynamoDB, a NoSQL database service that supports key-value and document data structures. You will learn how to use AWS QuickSight, a high-performance business intelligence service which integrates seamlessly with Glue tables by using the Amazon Athena Query Service. Finally, you will configure jobs to run extract, transform, and load operations on data stored in our data lake.

WHAT YOU WILL LEARN

  • configure a Redshift cluster to store data
    load data into a Redshift cluster from S3 buckets
    configure a JDBC connection on Glue to the Redshift cluster
    crawl data on a Redshift cluster using a Glue crawler
    crawl data stored in a DynamoDB table
    configure the Amazon QuickSight business intelligence tool to visualize data
  • build charts and dashboards in QuickSight
    define a job in Glue to perform ETL operations
    run ETL scripts using Glue
    perform ETL operations in Glue to backup data originally stored in Redshift
    perform ETL operations in Glue to backup data originally stored in DynamoDB
    recall how to use AWS services for visualizations and ETL

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 3s
    UP NEXT
  • Playable
    2. 
    Set Up a Redshift Cluster
    9m 8s
  • Locked
    3. 
    Create Tables and Load Data From S3
    7m 38s
  • Locked
    4. 
    Establish a JDBC Connection to Redshift
    7m 31s
  • Locked
    5. 
    Crawl Redshift Using a JDBC Connection
    5m 45s
  • Locked
    6. 
    Crawl DynamoDB
    7m 53s
  • Locked
    7. 
    Configure QuickSight to Visualize Data
    4m 33s
  • Locked
    8. 
    Visualize Data in QuickSight
    8m 47s
  • Locked
    9. 
    Configure a Job to Perform Extract, Transform, Load
    6m 21s
  • Locked
    10. 
    Execute an ETL Operation in Glue
    6m 43s
  • Locked
    11. 
    Perform ETL to Back Up Redshift Data in S3 Buckets
    8m 31s
  • Locked
    12. 
    Perform ETL to Back Up DynamoDB Data in S3 Buckets
    7m 48s
  • Locked
    13. 
    Exercise: Multiple Sources, Visualizations, and ETL
    5m 12s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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