Overview on Data Lakes

Data Lakes & Analytics | 6 Webinars | 4 Hours

Learning Path

  

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Table of Contents

    • 1. Unify Your Data Lake and Data Warehouse

      How to break information silos among lines of business in your organization by sharing data in your data lake and data warehouse.
      46:22

    • 2. How to Build Serverless Data Lake Analytics with Amazon Athena

      3. Use Amazon Athena to interactively analyze data in Amazon S3 data lakes.
      40:51

    • 3. A Deep Dive on AWS Lake Formation

      Ingest, catalog, cleanse, transform, and secure your data with AWS Lake Formation.
      32:43

    • 4. Amazon Redshift Tips and Tricks: Scaling Storage and Compute Resources

      Scale storage and compute resources on-demand and automatically, as needed with Amazon Redshift.
      27:29

    • 5. Fuzzy matching and Deduplicating Data with ML Transforms for AWS Lake Formation

      An overview of ML Transforms for AWS Lake Formation and learn how to find matching records between two different lists.
      33:30

    • 6. Machine Learning Powered Business Intelligence with Amazon QuickSight

      How to integrate ML-powered natural language summaries into your dashboards with Amazon QuickSight.
      44:27

Description

As a business grows, data accumulates. Inevitably, it gets placed in different silos, making it difficult to access and analyze. Storing data in a single place—or a “data lake"—can solve this problem. Data lakes help companies optimize operations by making it easy to read data and obtain insights. In this learning path, get an overview on AWS-powered data lakes. Learn what is needed to create one and how they can handle the scale, agility, and flexibility you need.

Learning Objectives

  • Objective One

    Learn how to analyze data between the data warehouse and data lake.

  • Objective Two

    Learn how AWS Lake Formation makes it easy to set up a secure data lake in days.

  • Objective Three

    Learn how to ingest, catalog, cleanse, transform, and secure your data.

Who Should Watch?

  • Storage administrators
  • Data Engineers
  • Data Architects
  • DBAs