Data Lake Sources, Visualizations, & ETL Operations

Amazon Web Services    |    Expert
  • 13 videos | 1h 27m 53s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 16 users Rating 4.4 of 16 users (16)
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

  • 2m 3s
  • 9m 8s
    In this video, you will learn how to configure a Redshift cluster to store data. FREE ACCESS
  • Locked
    3.  Create Tables and Load Data From S3
    7m 38s
    In this video, you will learn how to load data into a Redshift cluster from S3 buckets. FREE ACCESS
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    4.  Establish a JDBC Connection to Redshift
    7m 31s
    In this video, you will configure a JDBC connection on Glue to connect to the Redshift cluster. FREE ACCESS
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    5.  Crawl Redshift Using a JDBC Connection
    5m 45s
    In this video, learn how to crawl data on a Redshift cluster using a Glue crawler. FREE ACCESS
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    6.  Crawl DynamoDB
    7m 53s
    In this video, you will learn how to crawl data stored in a DynamoDB table. FREE ACCESS
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    7.  Configure QuickSight to Visualize Data
    4m 33s
    During this video, you will learn how to configure the Amazon QuickSight business intelligence tool to visualize data. FREE ACCESS
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    8.  Visualize Data in QuickSight
    8m 47s
    During this video, you will learn how to build charts and dashboards in Amazon QuickSight. FREE ACCESS
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    9.  Configure a Job to Perform Extract, Transform, Load
    6m 21s
    In this video, you will define a job in Glue to perform ETL operations. FREE ACCESS
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    10.  Execute an ETL Operation in Glue
    6m 43s
    Find out how to run ETL scripts using AWS Glue. FREE ACCESS
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    11.  Perform ETL to Back Up Redshift Data in S3 Buckets
    8m 31s
    In this video, learn how to perform ETL operations in Glue to backup data originally stored in Redshift. FREE ACCESS
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    12.  Perform ETL to Back Up DynamoDB Data in S3 Buckets
    7m 48s
    Learn how to perform ETL operations in Glue to backup data originally stored in DynamoDB. FREE ACCESS
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    13.  Exercise: Multiple Sources, Visualizations, and ETL
    5m 12s
    After completing this video, you will be able to recall how to use AWS services for visualizations and ETL. FREE ACCESS

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