• TERADATA ASTER DATABASE

    Deliver Powerful Big Data Analytics

    Accelerate time to insights with minimal resource outlays.


    Independent analyst firm cites Teradata as Market Leader in Analytic Database Platforms
  • Meeting your business needs

    Experience 10x-1000x better performance than traditional systems and scalability to hundreds of terabytes and petabytes of data. This powerful suite of analytic functions delivers industry leading visualizations for SQL, MapReduce and Graph applications and helps you gain valuable insights with the combined analytics power of MapReduce, R and SQL.

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    Accelerate time to insights with minimal resource outlays for big data analytics on multistructured data sources and types. Since it does not require new investments in hard-to-find programming skills and system administration capabilities, it is a cost-effective way to deliver new analytic capabilities for a wide group of users with varying technical skills at breakthrough performance and scalability.

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    Streamline management and centralized administration - even when your system scales to tens and hundreds of servers. The Aster Management Console makes it easy to configure, manage and monitor data, applications and infrastructure. The graphical interface includes summary dashboards, graphical views of query and process execution – plus single-click scaling and point-and-click access to workload management policies.

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    Optimize performance by executing loads, queries, exports, backups, recovery, installs and upgrades in parallel for faster insight discovery. Unique Applications-Within™ architecture runs analytic application logic inside the system via patented SQL-MapReduce®, SQL-Graph, R and SQL engines to deliver continuous availability, easy management, and linear scalability to meet your growing enterprise needs.




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    Now you can snap together multiple analytic engines (SQL-MR, SQL-GR™, R and SQL) and file stores based on your specific needs. And when it’s time to add more analytics engines, all current workload management, query optimization, execution and resource planning functions are automatically applied without sacrificing performance. The SNAP Framework™ is standards-based, extensible and integrates with existing IT infrastructures – and it’s a Teradata exclusive.

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    Adapt to changing priorities in real time with advanced workload management that allows for granular rule and policy-based prioritization. Dynamic mixed workload management lets you maintain scalable performance even with large numbers of concurrent users and workloads. Fault-tolerant design and simultaneous load and export during queries, online backup and recovery, online restoration, and online scaling help you avoid scheduled downtime.

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    Optimize, simplify, and orchestrate processing across and beyond the Teradata Unified Data Architecture (UDA) with Teradata QueryGrid™. Run the right analytic on the right platform and take advantage of specialized processing engines while operating as a cohesive analytic environment.

    Teradata QueryGrid: Aster-Hadoop
    Provides users with self-service access to Hadoop data at query run-time using standard SQL and their favorite query tool. Data from Hadoop can be brought into the Aster Database, where the advanced Aster functions be applied for sophisticated Big Data analytics.

    Teradata QueryGrid: Aster-Teradata
    Helps companies realize the full value of the Teradata UDA by leveraging production data from any Teradata database system to accelerate discovery projects. This bi-directional link can “push” processing into the Teradata database and return only the data required for discovery projects on Aster.

    Learn More: Teradata QueryGrid

    The Teradata Aster Solution

    Aster Management Console makes it easy to monitor and manage both data and analytic applications. Administrators gain an authoritative view of the system and mechanisms for invoking actions. Developers and other users gain insight into their activity, such as details on currently executing statements and statement histories. Features include:

    •  Single System View – Even as the database scales to several hundred servers, administrators see the system as a single entity, and all internal details of the cluster are transparently managed.
    •  Size-Independent Manageability – Management effort does not depend on the number of servers in the cluster and does not increase with cluster size.
    •  Single-Click Online Scalability – Add and remove servers from the cluster with one click, thanks to Aster Database’s Online Precision Scaling™ feature. 

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  • "Aster with the discovery platform actually allows us to have those con-nections both to Teradata and the data warehouse as well as our connection into the Hadoop distrib-ution so that we can have the insights on the data that sit in both locations without necess-arily having to completely rep-licate the data as other solutions might require."

    Rob Smith

    Exec. Director, IT – Verizon Wireless

    Customer Story

    Verizon Wireless Know Their 100 Million Customers

     

    How does the biggest wireless carrier in the United States keep their customers happy? Provide the best network with the best customer experience and listen. How does Verizon Wireless listen? With a Unified Data Architecture. “The traditional past of batch-and-blast is really something that's not effective. We're finding that with the insights that we're getting from these customer interactions that we can better target our communications, our marketing efforts and our sales, based on what the customer's individual needs are as opposed to what we as a business think they might need.”

     

    Read more about Verizon Wireless
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  • IDC

    Exclusive Research from IDC

    IDC has created your one-stop learning center for data discovery.

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    SnapFramework

    Teradata’s SNAP Framework Combines All Data, All Analytics

    The key to achieving success with Big Data is the ability to combine multiple data types, together with multiple analytical techniques, all at the same time while applying them all to the same problem.

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