Data fabric
Consistently deliver reliable data to users and applications at a fraction of the time and cost.
Data integration is the #1 area data leaders want to automate
Manual data integration projects consume too much time for data stewards and data engineers. That time can be used more efficiently.
A data fabric architecture saves resources and provides data faster to business users—every time they need it.
Establish a core with our automated data catalog
Ataccama ONE Data Catalog captures and stores rich metadata about your data sources. This includes data profiles, data quality, classification, lineage, access policies, data models, and more.
- Up-to-date metadata
All of this metadata stays up-to-date automatically, so that
you can reuse it and deliver data integration projects faster.
How an AI-augmented catalog powers your data fabric
Flexibly provide valid, cleansed, and deduplicated data to downstream applications, users, or source systems. Deploy anywhere you want and grow at your own pace.
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Find & understand
Ready for any deployment style, data domain, and data volumes. Enjoy the freedom to choose between cloud or on-premise environments. Easily connect with data lakes and big data platforms.
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Provide
Two-way synchronization enhances data availability. It works with relational and NoSQL databases, data lakes, data warehouses, cloud storage, metastores, streams, and files. It also integrates with major BI tools or analytical platforms.
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Reuse
Easily add new data sources to your MDM ecosystem and save time and resources with pre-generated data load interfaces, APIs, and user interfaces.
Deploy data integration pipelines faster with active metadata
Metadata is not the end goal. Activate it to build and run your data fabric architecture.
- Generate
Ataccama ONE generates data integration pipelines based on existing rules, patterns, data, and AI models.
- Process
Process data directly on big data platforms: data lakes, Hadoop and Spark clusters, and cloud data warehouses— without moving it anywhere.
- Orchestrate
Combine and orchestrate data transformation pipelines across all environments within the whole data fabric architecture.
Never miss a problem with your data integration pipeline
Ataccama ONE uses AI to detect anomalies when new data is processed. Then decide on the next appropriate action, and even stop the processing.
Ensure timely data delivery to any consumer
See what you can do with data fabric
Ataccama
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FAQ
Ataccama ONE powers a data fabric by using an AI-augmented catalog to capture and activate rich metadata across all your data sources. The platform generates data integration pipelines based on existing rules, patterns, and AI models, processes data directly on big data platforms without moving it, and orchestrates transformations across all environments within the architecture.
Active metadata is the foundation of a data fabric architecture. Ataccama ONE captures and maintains up-to-date metadata including data profiles, quality scores, classification, lineage, access policies, and data models. This metadata is then activated to generate integration pipelines, detect anomalies, and deliver data faster to business users every time they need it.
Ataccama generates data integration pipelines automatically based on existing rules, patterns, data, and AI models. The platform processes data directly on big data platforms, data lakes, Hadoop and Spark clusters, and cloud data warehouses without moving it, and uses AI to detect anomalies when new data is processed.
Ataccama supports three delivery styles: virtual data sets created on demand without writing data anywhere, analytical delivery that pushes integrated data into any analytical data store, and operational delivery that provides data in real time through web services, streaming, and messaging APIs. This flexibility ensures every consumer gets data in the format and speed they need.
A data fabric built on Ataccama supports data migration between on-premises and cloud sources, reliable analytics for tools like Power BI, Tableau, Qlik, and Looker, third-party data acquisition and sharing, data virtualization for on-demand views, interactive data preparation, master data management, and rapid ETL pipeline prototyping for enterprise deployments.
Data integration is the top area data leaders want to automate because manual projects consume too much time for data stewards and engineers. A data fabric architecture saves resources by reusing metadata and pre-generated integration patterns, delivering data faster to business users while reducing the manual effort and specialized expertise traditionally required for each new integration project.