Autonomous Database for Analytics and Data Warehousing

Shared Exadata Infrastructure Tutorials

Autonomous Database 15 Minute Quick Start

Learn about Autonomous Database on Shared Infrastructure and learn how to:
  • Deploy an Autonomous Database instance that is optimized for data warehousing workloads.

  • Use Autonomous Database tools to load data from object storage.

  • Use advanced SQL to uncover issues and possibilities.

Load and Analyze Your Data with Autonomous Database

Learn about Autonomous Database on Shared Infrastructure and learn how to create an Autonomous Database in just a few clicks.
  • Provision a new Autonomous Database instance on Shared Infrastructure

  • Run Queries on the sample data sets

  • Upload files to the Oracle Cloud Infrastructure Object Storage, create sample tables, load data into them from files on the OCI Object Storage

  • Visualize your data using Oracle Analytics Desktop

Important Tools for Everyone Using Oracle Autonomous Database

Use the suite of data tools built into Oracle Autonomous Database to help you with typical data warehouse tasks: Load, inspect, and transform data. Create a semantic business model. Identify data anomalies and outliers. Use the catalog to understand data lineage and impact analysis.

Produce Your Company's Best Picture with Converged Database Analytics on Autonomous Database

Learn how to deliver high value solutions using Oracle Cloud data platform services. Deploy an Autonomous Database instance, integrate Autonomous Database with a Data Lake, use advanced SQL to uncover issues and possibilities, predict customer churn using Machine Learning, use spatial analyses to help provide localized promotions, and offer recommendations based on graph relationships.

Manage and Monitor Autonomous Database

Load movie sales data into an Oracle Autonomous Data Warehouse from an object store, enable data integrity checks, and apply updates to the sales data.

Analyze MovieStream data in Oracle Autonomous Database using SQL Workshop

Learn some of the key analytical features of Oracle Autonomous Data Warehouse such as window functions, pattern matching, Excel-like operations using the SQL Model clause, and simple machine learning models.

Access the Data Lake Using Autonomous Database and Data Catalog

Learn about the steps to access the Data Lake using Autonomous Database and Data Catalog. You set-up an environment and create resources such as a compartment, users, a Data Catalog instance, an Autonomous Database instance, and set the necessary IAM policies. In a few steps, you create Oracle Object Storage data assets and add data connections, you harvest the data asset and view the harvested data entities, and synchronize the Data Catalog metadata. After the synchronization process completes, you can query data that lives in your Oracle database and combine that with data that is stored in your Oracle Object Storage buckets using regular SQL select statements.

Dedicated Exadata Infrastructure Tutorials

Oracle Autonomous Database Dedicated for Fleet Administrators Workshop

As fleet administrator, set up your dedicated ADB platform in the OCI and on Exadata Cloud@Customer.

Oracle Autonomous Database Dedicated for Developers and Database Users Workshop

As a developer or database user, provision a database, develop apps, monitor and tune the database.

Oracle Autonomous Database Dedicated for Security Administrators Workshop

As Autonomous Database Dedicated security administrator, set up various database security features.

Oracle Machine Learning Quick Start Tutorials

Learn how to create a project, a workspace, a notebook. Run your notebook. See how two or more users can collaborate and share notebooks with other Oracle Machine Learning users. Create, use, share, and run SQL scripts.

Introduction to Oracle Machine Learning Notebooks

Oracle Machine Learning Notebooks is an Apache Zeppelin-based collaborative web-based interface that provides a development environment to create machine learning notebooks where you can perform data exploration and visualizations, data preparation and machine learning.