Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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Google Cloud BigQuery
BigQuery is a serverless, multicloud data warehouse that makes working with all types of data effortless, allowing you to focus on extracting valuable business insights quickly. As a central component of Google’s data cloud, it streamlines data integration, enables cost-effective and secure scaling of analytics, and offers built-in business intelligence for sharing detailed data insights. With a simple SQL interface, it also supports training and deploying machine learning models, helping to foster data-driven decision-making across your organization. Its robust performance ensures that businesses can handle increasing data volumes with minimal effort, scaling to meet the needs of growing enterprises.
Gemini within BigQuery brings AI-powered tools that enhance collaboration and productivity, such as code recommendations, visual data preparation, and intelligent suggestions aimed at improving efficiency and lowering costs. The platform offers an all-in-one environment with SQL, a notebook, and a natural language-based canvas interface, catering to data professionals of all skill levels. This cohesive workspace simplifies the entire analytics journey, enabling teams to work faster and more efficiently.
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Rocket Data Virtualization
Conventional techniques for integrating mainframe data, such as ETL, data warehouses, and connector development, are increasingly inadequate in terms of speed, accuracy, and efficiency in today’s business landscape. As the amount of data generated and stored on mainframes continues to surge, these outdated methods fall further behind. Data virtualization emerges as the solution to bridge this growing divide, automating the accessibility of mainframe data for developers and applications alike. This approach allows organizations to discover and map their data just once, after which it can be easily virtualized and reused across various platforms. Ultimately, this capability enables your data to align with your business goals and aspirations. By leveraging data virtualization on z/OS, organizations can simplify the complexities associated with mainframe resources. Moreover, data virtualization facilitates the integration of data from numerous disparate sources into a cohesive logical repository, significantly enhancing the ability to connect mainframe information with distributed applications. This method also allows for the enrichment of mainframe data by incorporating insights from location, social media, and other external datasets, promoting a more comprehensive understanding of business dynamics.
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Oracle Data Service Integrator
Oracle Data Service Integrator empowers organizations to swiftly create and oversee federated data services, allowing for unified access to diverse datasets. This tool is entirely built on standards, is declarative in nature, and promotes the reusability of data services. It stands out as the sole data federation solution that facilitates the development of bidirectional (both read and write) data services across various data sources. Moreover, it introduces an innovative feature that removes the need for coding by enabling users to graphically design both straightforward and intricate modifications to different data sources. Users can easily install, verify, uninstall, upgrade, and initiate their experience with Data Service Integrator. Initially branded as Liquid Data and AquaLogic Data Services Platform (ALDSP), Oracle Data Service Integrator still retains some references to these earlier names within its product structure, installation paths, and components. This continuity ensures that users familiar with the legacy names can still navigate the system effectively.
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