Analytics Platform Capabilities Explorer


Oracle Analytics connects to many data sources, both Oracle and non-Oracle, including other cloud providers (e.g., Azure and Google), social feeds, IoT sources, data lakes, and more. Connected data sources can include cloud, on-premises, or self-service data sets. Use self-service to blend third-party or personal sources for a complete business view.

Native connectors

Get started quickly with 40 out-of-the-box native connectors that provide connections to dozens of applications including Oracle Autonomous Database, Enterprise Performance Management, Fusion Applications (HCM, ERP, CX, and more), Oracle Database Analytic Views, and non-Oracle sources, such as Google Big Query, Salesforce, Amazon Redshift, Azure Synapse Analytics, Snowflake, and more. Connect to any Java Database Connectivity-based (JDBC) data source.

Figure 1: Native connectors to popular data sources

Integration with data lakes

The data required to make informed decisions comes from many data sources and includes a wide variety of different data types, such as structured, semi-structured and unstructured. Oracle Analytics provides the flexibility to connect data lakes (Oracle Cloud, Azure, AWS, and Google Cloud) through OCI services, such as OCI Data Catalog and Oracle Autonomous Database, to ensure all relevant data is available to users. Data does not need to be moved or replicated to support business analytics. Whenever possible, functions are shipped to and processed by the data source servers.

Figure 2: Integrating analytics with the data lakehouse

This image positions Oracle Analytics as part of an extensive Oracle Cloud Infrastructure (OCI) ecosystem of data lake services, including AI services, data integration services, and more. Integrate any of the OCI services seamlessly with OAC for business users to use with their data sets.

On the left are the data sources including any database, any application, any cloud and any event/sensors. The source information flows to the right into the central box.

The central box depicts the Oracle data lakehouse and the services that comprise it. However, in context of Oracle Analytics, the data lakehouse can be comprised of services from any cloud vendor. The services shown in the diagram are the data warehouse, data definition and discovery, data movement, and data processing engines. Information from the central box then flows to the right-hand box. The right-hand box shows the data consumers, including Oracle Analytics, machine learning, and data science or any other application.

Direct query and data caching

The Oracle Analytics platform provides both direct query and caching options. Direct query enables data to be ingested into the analytics layer directly from the data source itself at query time. Choose a custom balance between direct query and caching depending on the analytics use case. Analytics queries are automatically optimized for each data source for best performance. Oracle Analytics does not require any third party or proprietary data store to be preloaded with data before users’ analytics activities can begin.

Direct or live connections

Direct query is an essential need as identified by Gartner in their Critical Capabilities for Analytics and Business Intelligence Platforms report. This ensures the most accurate representation of the data in the visualization layer but can potentially place a lot of analytics compute load on the data source systems.

Data set caching

Frequently accessed query results can optionally be cached by Oracle Analytics (both OAC and OAS) to boost performance and reduce the analytics workloads on the source systems. Caching analytics data sets also helps to reduce data source processing loads.

Learn more about OAC caching

An in-memory engine is part of Oracle Analytics Cloud, and it boosts the performance of slow, or legacy data sources. Boosting slow systems means frequently run query data is cached and optimized for analytics, which then provides high performance consistently to users. Once data is cached, modern analytics capabilities, such as auto-insights and machine learning, can easily be run on that cached data. This extends legacy data management systems with otherwise missing modern capabilities.

Figure 3: Oracle Analytics Cloud in-memory engine

This image shows Oracle Analytics, which contains an in-memory engine that boosts the performance of slow, or legacy data sources. On the left, are built-in capabilities describing the in-memory engine.

On the left are built-in capabilities describing the in-memory engine.

  1. Self-tuning
  2. Self-compressing
  3. Self-caching
  4. Self-optimized
  5. Fully managed Oracle Analytics UI

In the center, the diagram shows the in-memory engine, which feeds data upward to Oracle Analytics to provide a highly performant experience to users as they interact with their data. On the right, are capabilities built-in the in-memory engine.

  1. 1. UI cache: Faster response times for similar queries
  2. 2. Automating caching: Self-caching technology
  3. 3. Optimized and compressed: In-memory optimized column store
Learn more about OAC’s in-memory capabilities
Learn more about OAS performance tuning

Data sets and local files

Upload local or personal data sets, such as spreadsheets and comma-separated value (CSV) files. Analyze these data sets alone or combine with any connector-based data source or governed enterprise data model.

Figure 4: CSV upload data preview