Teradata platform helps big-data analysts ask the right questions

Next-generation analytics is not about getting the right answer so much as asking the right questions.  But what if agencies could get the data to help them discover the questions they need to ask?

Organizations need more than just a handful of analytic tools, according to officials with Teradata, a provider of analytics software. They need an entire discovery platform that weaves analytical tools together to help business users “uncover things they didn’t know they needed to know.”

To address this, the company recently unveiled the Teradata Aster Discovery platform that snaps multiple analytical engines into a common framework. The common interface lets business analysts investigate all types of data sources – structured, multi-structured or unstructured – without having to change formats of the analytical engines or rely on data scientists.  Yet the platform is sophisticated enough to meet the analytical needs of data scientists, company officials said.

Many companies offer stand-alone technologies that independently run text, statistical or graph analytics, but Teradata has integrated them into a single platform, said Scott Gnau, president, Teradata Labs. 

The firm’s  Aster Discovery Platform includes Teradata Aster SQL-GR, a graph engine, and the Teradata SNAP Framework (Teradata Aster Seamless Network Analytic Processing Framework). Key features of the platform include:

  • The Teradata SNAP Framework lets users put together multiple analytic engines and file stores based on their data discovery needs. The tightly integrated components let users delve deeply into data by leveraging multiple analytical capabilities – like graph, MapReduce, text, statistical, time series and SQL-based analytics. Business analysts need to submit a single query in a SQL database or through a business intelligence tool. Then, a new query executer tool engages a combination of analytic engines from new and existing files stores.
  • The graph analytic engine lets users customize their Aster analytics with Teradata Aster SQL, Teradata Aster SQL-MapReduce or the newly developed Teradata Aster SQL-GR engine. The Aster SQL-GR graph engine is scalable, processes big data in parallel and is not limited by system memory, Teradata officials said.  It provides native processing of large-scale analytic graph queries and pre-built graph functions.  
  • Teradata Aster File Store provides storage options that are built for data discovery. Users can select the new Teradata Aster File Store for all data, as well as existing data stored in row or column formats. The software is designed to quickly take in and store petabytes of raw data, provide storage management and then make that data readily available for preprocessing. The Teradata Aster File Store is also compatible with Apache Hadoop and the Hadoop Distributed File System, providing a high-speed connector between Hadoop and Aster. 
  • Teradata has added pre-built analytic functions that can be accessed from a common SQL database framework. The analytic functions help analysts rapidly dig into data with interactive visualizations delivered through a Web browser and common business intelligence tools.

Teradata’s integrated approach is important because both data scientists and analysts can be hampered by piecemeal big-data strategies, rudimentary tools and disparate tools, technologies, and processes, said Dan Vesset, program vice president for business analytics and big data with market researcher IDC.  In fact, recent IDC research revealed that only 10 percent of organizations surveyed have the features and functionality needed to explore data and discover insights.  To address the big data strategy void, “an integrated data discovery platform should be part of every organization’s portfolio,” Vesset said.

About the Author

Rutrell Yasin is is a freelance technology writer for GCN.

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Reader Comments

Fri, Oct 25, 2013 Data101 FL

Rutrell, with the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it’s computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quick and simple. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery. Their built-in Machine Learning Library and Matrix processing algorithms can assist with business intelligence and predictive analytics. More at

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