The UDA channel is a place for information sharing regarding Teradata’s Unified Data Architecture including the Analytical Ecosystem and other UDA influences.  This channel provides information specific to the integration and co-existence of multiple systems, in particular when a mix of Aster, Teradata, and Hadoop are present. It is also meant to support information around the UDA enabling technologies so products like Viewpoint, Data Mover, Connectors, QueryGrid, etc.

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Getting Started with Loom

Loom provides core capabilities needed for enterprises to successfully deploy a Hadoop data lake.  Loom makes the first phase of the analytic workflow more efficient, enabling analysts to quickly find, understand, and prepare data in a Hadoop data lake, which allows them to spend more time developing analytics.  Ultimately, this means more business insights are developed faster, which is the ultimate driver for ROI of the data lake.

Try Teradata Loom with CDH

Teradata Loom® is an integrated big data solution for effective data and metadata management on Hadoop, enabling rapid analyst productivity by making it easy to find, access and understand data. Loom is the complete solution for getting the most from your data lake. Loom provides access to Hadoop data and metadata through an open REST API. Built on the API, the Loom Workbench is a simple browser-based UI for working with data and metadata in Hadoop.

Try Teradata Loom with HDP

Teradata Loom® is an integrated big data solution for effective data and metadata management on Hadoop, enabling rapid analyst productivity by making it easy to find, access and understand data. Loom is the complete solution for getting the most from your data lake. Loom provides access to Hadoop data and metadata through an open REST API. Built on the API, the Loom Workbench is a simple browser-based UI for working with data and metadata in Hadoop.

Try Teradata Loom

Teradata Loom® is an integrated big data solution for effective data and metadata management on Hadoop, enabling rapid analyst productivity by making it easy to find, access and understand data. Loom is the complete solution for getting the most from your data lake. Loom provides access to Hadoop data and metadata through an open REST API. Built on the API, the Loom Workbench is a simple browser-based UI for working with data and metadata in Hadoop.

Teradata Loom

Teradata Loom is an integrated big data solution for effective data and metadata management on Hadoop, enabling rapid analyst productivity by making it easy to find, access and understand data. With deep integration capabilities, Teradata Loom captures all kinds of metadata about data and processes across Hadoop and provides a single, robust platform for data management, maintaining data lineage and data preparation through metadata management.

Unified Data Architecture Monitoring & Management

The Unified Data Architecture enables Teradata, Aster and Hadoop to deliver unparalleled value.

Unity Ecosystem Manager – Advanced Monitoring and Alerting for your UDA

It’s 2 AM, and the Load Job required for the executives’ Monday morning briefing report failed… How are you notified? Ecosystem Manager provides advanced features that can alert and trigger automated corrective actions for such system events.

Backup and Restore using Teradata Data Stream Architecture (DSA)

Teradata Data Stream Architecture (DSA) is a new product offering for Teradata Backup and Restore, providing significant performance and usability improvements over existing backup and restore functionality.

What's New in 15.00 for Unity Ecosystem Manager

Unity Ecosystem Manager is part of the Teradata Unity portfolio of products supporting multi-system Teradata Environments.  While part of the Unity portfolio, it can also be leveraged as an event-driven system for efficient monitoring, alerting  and control of an entire analytic environment (Applications, Servers, Jobs, Tables) for a single Teradata, Aster or Hadoop system. 

Hadoop Security 101

Processes for integrating security controls were underdeveloped in early versions of Hadoop because data protection needs in its ecosystem were not well-understood.

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