Best Data Fabric Software for OpenMetadata

Teradata VantageCloud is a flexible, cloud-based data platform that acts as a versatile data fabric, facilitating effortless access, integration, and oversight of data in both multi-cloud and hybrid settings. It consolidates structured and semi-structured data from various origins into a cohesive framework, enabling real-time analytics, AI/ML processes, and comprehensive governance at an enterprise level. Its open architecture promotes compatibility with contemporary data tools and standards, minimizing dependency on a single vendor. Equipped with inherent features for data quality, lineage tracking, and policy implementation, VantageCloud empowers organizations to establish a reliable and adaptable data infrastructure for driving innovation and gaining insights.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

Domo has become a part of Progress Software, integrating its AI and data platform into Progress' suite of offerings. This cloud-native AI data readiness platform not only enhances but also expands Progress' existing data solutions, fostering significant synergies that facilitate the development of innovative, secure, and scalable AI data readiness solutions on a global scale. These combined strengths will assist clients in transforming fragmented enterprise data and insights into governed, AI-ready intelligence, thus elevating the security, governance, and cost-effectiveness of AI-driven projects. Positioned as the agentic platform for the intelligent enterprise, Domo empowers organizations to connect, govern, activate, and disseminate both data and AI effectively. Collaborating with cloud data platforms such as Snowflake, BigQuery, and Databricks, Domo enables the conversion of governed data into various AI agents, applications, workflows, dashboards, and analytics. Additionally, its robust data foundation, activation, and distribution layers empower teams to create and implement intelligence precisely where their work occurs, all while ensuring governance, security, and access controls are firmly in place across both data and AI initiatives. This comprehensive approach not only streamlines operations but also enhances the overall effectiveness of data-driven decision-making within organizations.

Dagster is the cloud-native open-source orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. It is the platform of choice data teams responsible for the development, production, and observation of data assets. With Dagster, you can focus on running tasks, or you can identify the key assets you need to create using a declarative approach. Embrace CI/CD best practices from the get-go: build reusable components, spot data quality issues, and flag bugs early.