There are five key services under Analytics and AI:
1. Data integration: This is a native cloud service that helps users with common Extract, Load, Transform (ETL) tasks. Powered by Spark ETL, this service is beneficial for storing, managing, and sharing development artifacts, while also providing schema evolution protection.
2. Data Flow: Data Flow is a fully managed OCI service that enables users to run Apache Spark applications at various scales with virtually zero administration. Using Apache Spark, a user can conduct large-scale data transformations and analyses and subsequently run Machine Learning and AI algorithms on resulting data. It is advantageous for building large scale AI services due to AI's dependency on the data that supports it. These functions separate Apache Spark and make it a highly beneficial tool. Data Flow analyzes data from OCI Object Storage and Autonomous Data Warehouse, from various third-party relational database systems. It provides services such as Big Data Processing (ETL + SQL + Streaming) and Machine Learning (MLib + SparkR).
3. Data Catalog: This service provides a single collaborative environment to manage technical, business, and operational metadata. Users can collect, organize, find, access, understand, enrich, and activate this metadata. It is a multi-step process of self-service data discovery and provides a governance solution. First, users need to harvest technical metadata from a wide range of supported data sources, which they can access using private or public APIs. Then, users will build a hierarchy of categories, subcategories, and terms with detailed text descriptions. Users can enrich this harvested metadata with annotations and link to data entities and attributes. Once these steps have been completed, a user can search, browse, or explore the data catalog. Furthermore, user can automate this entire process using schedules.
4. Data Science: Oracle Cloud Infrastructure offers a comprehensive and efficient solution for data science teams and data scientists with its Data Science service. This fully managed and serverless platform enables this training and management of machine learning models within the Oracle Cloud Infrastructure environment. It seamlessly integrates with other OCI services, including serverless functions, Data Flow (OCI's Manage Apache Spark service), Autonomous Data Warehouse, and Object Storage. One of the key features of this platform is the concept of projects, which serve as collaborative workspaces for organizing and documenting data science assets such as notebook sessions and models. Notebook Sessions provide interactive coding environments for building and training models. These sessions come equipped with a wide range of pre-installed open source and Oracle-developed machine learning and data science packages. The project-driven user interface facilitates easy teamwork on end-to-end model workflows and supports the latest open-source tools for Python. Models, in the context of the OCI platform, represent a mathematical representation of users' data and business processes. The model catalog serves as a centralized location for storing, tracking, sharing, and managing models throughout their lifecycle. Finally, OCI offers the Accelerated Data Science SDK, a Python library integrated into the OCI Data Science service. This SDK provides a user-friendly interface with objects and methods that cover all the essential steps involved in the lifecycle of machine learning models, from data acquisition to model evaluation and interpretation.
5. Golden Gate: This service is a fully managed, native cloud service that moves data in real-time, at scale. OCI GoldenGate processes data as it moves from one or more data management systems to target databases.