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  • admin 9:52 am on October 26, 2015 Permalink
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    Teradata QueryGrid Changes the Data Access Paradigm 

    Q3-15_Applied Solutions_QueryGridby Dirk Anderson

    There has been a lot of recent industry buzz about Teradata® QueryGrid™, and for good reason. It is one of those rare products that fundamentally changes the way organizations work.

    The access layer allows queries that run on the Teradata Database to seamlessly access information on external servers such as Apache™ Hadoop®, Oracle Database and MongoDB, which offers a huge time savings. This ability opens up vast opportunities for data scientists and power users who spend a lot of time gathering and assembling data for analysis.

    For example, data scientists conducting research with information from the Teradata Database and other data sources often spend a significant amount of time extracting data from disparate sources. Additional time is spent moving the data into SAS or another location for analysis. Plus, when dozens of users load information into data stores, enterprise data is often duplicated on various departmental or personal platforms. Not only is this a capacity concern, but having multiple copies of the data on individual machines also poses a security risk. Teradata QueryGrid solves the problem.

    Simplify a Complex Process

    With Teradata QueryGrid, data scientists can develop a single script using SQL syntax that runs on the Teradata Database to join all the data sources together. This removes the complexities of connecting to external servers, writing extraction scripts, transferring data, allocating storage space, translating data types into formats recognized by the local machine, compressing the local data and remembering to encrypt sensitive information.

    When data scientists need to run SAS or R analytics, they can run a rich set of in-database functions within the massively parallel architecture of the Teradata Database to achieve extreme performance. The results can be saved and shared securely among colleagues with Teradata Data Lab. This process simplifies coding by reducing (or eliminating) the amount of data that needs to be stored locally and dramatically increases the speed-to-market for analytics.

    Remove the Bottlenecks

    Each new generation of processing nodes brings faster CPUs and increased I/O bandwidth for processing and moving data. Still, the biggest performance challenge in the current era of big data is channeling information into and out of the Teradata platform.

    Single-channel data movement is not effective for handling large volumes of data, and can cause bottlenecks. Teradata QueryGrid solves that by facilitating high-volume, multi-channel data movement between Teradata, Teradata Aster and Hadoop platforms. Ideally, the Hadoop and Teradata platforms should be connected using BYNET® over InfiniBand.

    Another bottleneck stems from the expense and time-to-market required for a new ETL project. Developing traditional ETL to load the data warehouse can be so prohibitive that it is cost-effective only for high-value data. With the access layer, scripts can be rapidly developed to select data directly from source tables and insert or upsert them directly into target tables. This process can often be completed with just a few simple SQL statements. Since coding is reduced and simplified, maintenance and support are easier, which greatly reduces costs and accelerates speed-to-market.

    Although Teradata QueryGrid is not a replacement for ETL tools, the capability gap is narrowing. Teradata has been adding ETL-like capabilities with each release of the Teradata Database. For example, Teradata Database 15.0 includes the ability for SQL to invoke non-SQL languages such as Ruby, Python and Perl. These enhancements dramatically shift the balance in determining which ETL processes can be reasonably performed in-database with Teradata QueryGrid and which should be done using traditional methods.

    From Hours to Minutes

    The access layer can move data to and from an external server. This bi-directional capability opens up options for users. For instance, a single script can extract data from a source system, run referential integrity checks on the Teradata Database and send any data that failed the checks back to the source system for review.

    Bi-directional data movement is beneficial when information needs to be loaded in both the enterprise-class Teradata server for general users and also in a Teradata Aster server for data scientists. A single script loads the data onto the enterprise server, at which point the data is integrated with other information, and the result is pushed over to the Teradata Aster Database. This process used to take hours. With Teradata QueryGrid, it now takes just minutes.

    Leverage the Solution’s Full Potential

    Teradata QueryGrid is causing a paradigm shift in the way organizations work with and benefit from the Teradata Database. By understanding the potential of the access layer and how to better leverage the solution, companies are better positioned for their future data warehousing and big data analytics projects.

    Dirk Anderson is a senior vice president of a major financial institution and a Teradata Certified Master. He has worked hands-on with Teradata solutions for more than 20 years. 

    For this article as well as more technical solutions and insights visit TeradataMagazine.com.

     

     

    The post Teradata QueryGrid Changes the Data Access Paradigm appeared first on Magazine Blog.

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  • admin 9:49 am on August 31, 2015 Permalink
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    Japan-Data Analysis Using Teradata QueryGrid 


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  • admin 9:51 am on July 17, 2015 Permalink
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    Spark Innovation With Teradata QueryGrid 

    by Richard Hackathorn tech-knowledge

    These are confusing times for IT management. Accepted practices and established technologies seem limited or even irrelevant in light of today’s opportunities and challenges with incorporating big data and advanced analytics into enterprise systems.

    To understand these issues, Bolder Technology, Inc., conducted a study entitled “Analytics in Action with Teradata® QueryGrid™” that looked at how nine companies are using analytic solutions. These industry leaders blend data among the Teradata Database, Teradata Aster Database and open-source Apache™ Hadoop®. Their approach is to adopt the Teradata Unified Data Architecture™, an ecosystem for enterprise analytics.

    The glue for this architecture is Teradata QueryGrid, whose goal is to orchestrate analytic processes as a single unit of work, based on SQL, across various platforms. Supporting a transparent access layer with parallelized data flows, Teradata QueryGrid can minimize data movement so business users are unaware—and don’t need to be concerned about—where data is stored. To leverage the unique processing capabilities of specific platforms, the solution also supports pushdown processing so remote platforms can perform specialized analytic functions.

    Access Layer Simplifies Problem Solving

    Teradata QueryGrid enables data movement to and from the Hadoop platform to support enterprise analytics. Here is one example:

    A travel reservations company uses the solution for its website, call center and A/B testing. One of the organization’s goals is to better understand the “conversion funnel,” which is when online customers convert from browsing to purchasing. Gaining that understanding requires analyzing two data sets—behavior data from website logs and analytic vendors, and booking data.

    Web logs provide insights into the conversion funnel so the company can answer questions such as, “Where should we spend marketing funds to improve website bookings?” These Web logs contain a significant amount of data that needs to be parsed to assess the unique customer journey for every individual visit.

    While Hadoop is useful in parsing the Web logs, analysis is challenging. That’s because booking data must be transferred into the integrated data warehouse from an ERP system on a regular basis. Teradata QueryGrid resolves the issue by enabling the Teradata platform to process the finalized customer journey data coming from Hadoop.

    Knowing more about the conversion funnel also requires using interactive voice response (IVR) data sets. These complex sequences of text and audio from call centers are stored on the Hadoop platform. Teradata QueryGrid can be used to combine booking and IVR data to uncover customer conversion insights.

    In addition, Teradata QueryGrid can support A/B testing for improving website content design. The travel reservations company constantly tests new ideas for website content and style, and assesses the impact of dozens of website changes by observing randomly selected customers.

    Using Hadoop, an A/B testing team looks at clickstream data to track each website change. Throughout each day, Teradata QueryGrid transfers booking data from the Teradata platform to Hadoop. The team is then able to match the two data sets to calculate metrics such as dollar volume for each test.

    Recurring Themes Emerge With Enterprise Analytics

    These seven themes recurred through the majority of the use cases:

    • Cultural bridge. The access layer of Teradata QueryGrid acts as the bridge between two technical cultures, such as the community around the Teradata and Hadoop platforms, enabling collaboration within the same information ecosystem. This results in a more natural work environment that brings together relational and non-relational data. For example, the efficiency of Teradata QueryGrid allowed a company to parallel load Hadoop data into the Teradata Aster Database, where text conditioning and analysis detected 50% more bad emails.
    • Data placement. Organizations realize that where data is placed and processed is an important configuration issue. Synchronizing data movement among platforms optimizes processing where the data resides. However, users want to access data through the platform they prefer. Therefore, the tradeoffs of data storage and processing must be balanced among the various platforms.
    • Data marriages. Business value is created when new data is married with older reference data on customers, purchases and the like. Proper analytics increases the value of the data, and that value is further enhanced when insights are combined with reporting and dissemination tools. These marriages drive the justification for data movement among the platforms, such as when a travel reservations company needs to integrate the booking data from the Teradata platform with data in Hadoop to complete its conversion funnel and A/B testing analyses.
    • Messy data storage. Messy data comes from sources including Web logs, sensors, text and social media. Organizations need the ability to quickly and efficiently store this data using Hadoop, then access the information to support business applications. A company can use Teradata QueryGrid to move messy data from the Teradata platform and from Hadoop into the Teradata Aster Discovery Platform to perform analytics for new applications development or other business needs.
    • Event sequencing. A critical analytics solution is the Teradata Aster nPath™ function that discovers the event sequence that precedes a significant business event, such as a customer switching to a competitor. One organization used Teradata QueryGrid to pull Web log data from Hadoop and billing data from a data warehouse into the Teradata Aster Discovery Platform where nPath could be employed to answer event-sequencing questions such as, “What is the last step the customer performs before going away from our website?”
    • Parallelizing data streams. The benefits of running parallelized data streams include eliminating bottlenecks and changing workflows. Analysts will be able to ask more questions and get more answers so they can explore more alternatives and better validate business solutions. Teradata QueryGrid initiates a massively parallel connection, resulting in hundreds of concurrent data streams between platforms.
    • SQL views. SQL views simplify usage and ensure security for Teradata QueryGrid. For instance, a company relied on the access layer to enable the Teradata Aster Discovery Platform to collect data from Hadoop and the Teradata Database for analysis. The results were shared using Teradata Aster Lens™ visualizations. The company used Teradata QueryGrid via SQL views to hide Hadoop configurations and protect the data since Hadoop lacks adequate security.

    A New Approach

    The IT industry is in the beginning stages of redefining the data warehouse and advanced analytics as an integrated information ecosystem within the enterprise. As implied by the use cases in the study, an approach is emerging that supports enterprise analytics at scale. This approach is enabled by federated workflows within an integrated information ecosystem among a fabric of closely coupled purpose-built platforms. The objective is a constant cycle of discovery and innovation, resulting in incremental improvements to business processes.

    Richard Hackathorn is founder and president of Bolder Technology, Inc., a consultancy focused on enterprise analytics, business intelligence and data warehousing. 

    Read the full article and more in the Q2 2015 issue of Teradata Magazine.

     

    The post Spark Innovation With Teradata QueryGrid appeared first on Magazine Blog.

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  • admin 9:53 am on May 20, 2015 Permalink
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    Teradata QueryGrid: One Solution to Connect Multiple Systems 

    Organizations want to access and benefit from growing data volumes coming from an expanding array of sources. The challenge is to be able to efficiently retrieve and analyze the data since some non-integrated systems from various vendors have complex processing requirements.

    A solution that lets businesses leverage all data, regardless of where it’s stored, is Teradata® QueryGrid™. It optimizes and simplifies access to data across the Teradata Database, Teradata Aster Database and open-source Apache™ Hadoop® that comprise the Teradata Unified Data Architecture™, as well as other source systems.

    Teradata QueryGrid is an enabling software engineered to tightly link specialized processing engines to act as one solution from the user’s perspective. This intelligent, seamless and transparent access lets users perform multi-system analytics and have queries, or even parts of queries, sent to the appropriate platforms for execution.

    Want to learn more? Read about the real-world benefits of Teradata QueryGrid in Teradata Magazine. 

    Brett Martin
    Editor-in-Chief
    Teradata Magazine

     

    The post Teradata QueryGrid: One Solution to Connect Multiple Systems appeared first on Magazine Blog.

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  • admin 9:47 am on April 30, 2015 Permalink
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    Teradata unveils improved QueryGrid connectors 

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  • admin 9:54 am on April 28, 2015 Permalink
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    Teradata QueryGrid Innovation Video 


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  • admin 9:52 am on April 25, 2015 Permalink
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    Teradata QueryGrid Expands Customer Choice for Best of Breed Analytic Technologies 

    Enhanced QueryGrid delivers greater choice of Hadoop distributions, architectural flexibility, and analytic extensibility
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  • admin 9:47 am on March 3, 2015 Permalink
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    A Technical Deep Dive into Teradata Loom QueryGrid and Optimizing Hadoop 


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  • admin 9:56 am on February 13, 2015 Permalink
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    See how it works QueryGrid Teradata Hadoop 


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  • admin 9:48 am on February 6, 2015 Permalink
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    Analytics in Action with Teradata QueryGrid 


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