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Build a Data Lake with Autonomous AI Lakehouse

About This Workshop

Youtube Video

About This Workshop
Data is everywhere stored both locally and on the cloud in different format such as CSV, Parquet, JSON, ORC, Avro, and the new table format that will be available soon such as Iceberg and Delta Share. Although the metadata can stored in different places, Autonomous AI Lakehouse can work with it whether it is stored locally (using PL/SQL,API, and the UI) or in central places in data catalogs (such as OCI DCAT and AWS Glue). AI Lakehouse provides high performance when querying external data at scale. Finally, AI Lakehouse provides multi-language support to process Data Lakes such as SQL, Python, R, and REST.
The labs in this workshop walk you through all the steps that you need to build and access the Data Lake (Oracle Object Storage buckets) using Autonomous AI Lakehouse. First, you set-up the workshop environment and create the necessary resources such as a compartment, an Autonomous AI Lakehouse instance, and Oracle Object Storage resources. Next, evolve object storage files into meaningful business entities by defining the metadata for external data in AI Lakehouse using several methods such as programmatically using PL/SQL scripts, using the Database Actions user interface, or by synchronizing with Data Catalogs (to derive the metadata) such as OCI Data Catalog and AWS Glue. Next, you can gain insights from Data Lakes by querying data that lives in your Oracle AI Lakehouse and combine that with data that is stored in your Oracle Object Storage buckets using regular SQL select statements that includes joined tables. You also move data to Object Storage buckets by exporting your datasets. Finally, you query your data using the SQL Worksheet.

Workshop Info

2 hours, 30 minutes
  • Introduction
  • Set up the workshop environment
  • Load local data
  • Enhance data for analytics using the Table AI Assist tool
  • Install and use the Autonomous AI Database Excel Add-in
  • Load and analyze JSON data
  • Link to data in public object storage buckets
  • Link to data in private object storage buckets
  • Load and analyze data from REST endpoints
  • Create External Tables using the Delta Sharing protocol
  • Query data from multi-cloud data lakes
  • Create and share data products from the AI Lakehouse
  • Integrate AI Lakehouse with existing data catalogs
  • Improve query performance
  • Learn about AI Lakehouse's support for Multiple Languages 
  • Clean up resources used in this workshop (optional)

The user must have the necessary permissions to: 

  • Provision and use an Oracle Autonomous AI Lakehouse instance
  • Manage Oracle Object Storage buckets
  • Familiarity with Oracle Database and SQL is desirable, but not required
  • Some understanding of cloud and database terms is helpful
  • Familiarity with Oracle Cloud Infrastructure (OCI) is helpful

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