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vr_Big_Data_Analytics_02_defining_big_data_analyticsTeradata continues to expand its information management and analytics technology for big data to meet growing demand. My analysis last year discussed Teradata’s approach to big data in the context of its distributed computing and data architecture. I recently got an update on the company’s strategy and products at the annual Teradata analyst summit. Our big data analytics research finds that a broad approach to big data is wise: Three-quarters of organizations want analytics to access data from all sources and not just one specific to big data. This inclusive approach is what Teradata as designed its architectural and technological approach in managing the access, storage and use of data and analytics.

Teradata has advanced its data warehouse appliance and database technologies to unify in-memory and distributed computing with Hadoop, other databases and NoSQL in one architecture; this enables it to move to center stage of the big data market. Teradata Intelligent Memory provides optimal accessibility to data based on usage characteristics for DBAs, analysts and business users consuming data from Teradata’s Unified Data Architecture (UDA). Teradata also introduced QueryGrid technology, which virtualizes distributed access to and processing of data across many sources, including the Teradata range of appliances, Teradata Aster technology, Hadoop through its SQL-H, other databases including Oracle’s and data sources including the SAS, Perl, Python and even R languages. Teradata can provide push-down processing of getting data and analytics processed through parallel execution in its UDA including data from Hadoop. Teradata QueryGrid data virtualization layer can dynamically access data and compute analytics as needed making it versatile to meet a broadening scope of big data needs.

Teradata has embraced Hadoop through a strategic relationship with Hortonworks. Its commercial distribution, Teradata Open Distribution for Hadoop (TDH) 2.1, and originates from Hortonworks. It recently announced Teradata Portfolio for Hadoop 2, which has many components. There is also a new Teradata Appliance for Hadoop; this is its fourth-generation machine and includes previously integrated and configured software with the hardware and services. Teradata has embraced and integrated Hadoop into its UDA to ensure it is a unified part of its product portfolio that is essential as Hadoop is still maturing and is not ready to operate in a fully managed and scalable environment.

Teradata has enhanced its existing portfolio of workload-specific appliances. It includes the Integrated Big Data Platform 1700, which handles up to 234 petabytes, the Integrated Data Warehouses 2750 for up to 21 petabytes for scalable data warehousing and the 6750 for balanced active data warehousing. Each appliance is configured for enterprise-class needs, works in a multisystem environment and supports balancing and shifting of workloads with high availability and disaster recovery. They are available in a variety of ratios including disks, arrays and nodes, which makes them uniquely focused for enterprise use. The appliances run version 15 of the Teradata database with Teradata Intelligent Memory and interoperate through integrated workload management. In a virtual data warehouse the appliances can provide maximum compute power, capacity and concurrent user potential for heavy work such as connecting to Hadoop and Teradata Aster. UDA enables distributed management and operations of workload-specific platforms to use data assets efficiently. Teradata Unity now is more robust in moving and loading data, and Ecosystem Manager now supports monitoring of Aster and Hadoop systems across the entire range of data managed by Teradata.

Teradata is entering the market for legacy SAP applications with Teradata Analytics for SAP, which provides integration and data models across lines of business to use logical data from SAP applications more efficiently. Teradata acquired this product from a small company in last year; it uses an approach common among data integration technologies today and can make data readily available through new access points to SAP HANA. The product can help organizations that have not committed to SAP and its technology roadmap, which proposes using SAP HANA to streamline processing of data and analytics from business applications such as CRM and ERP. For others that are moving to SAP, Teradata Analytics for SAP can provide interim support for existing SAP applications.

Teradata continues to advance JavaScript Object Notation (JSON) integration for support of document-oriented databases that are schemaless and semistructured. JSON has become a critical tool as more applications need to store and access data efficiently. NoSQL databases have become more popular recently: 25 percent of organizations in our big data analytics research are using them today, 20 percent  plan to use them within two years, and another 23 percent are evaluating NoSQL. With this focus Teradata provides for its customers application and operational support beyond just supporting data for analytic purposes.

Teradata continues expansion of its Aster Discovery Platform to process analytics for discovery and exploration and also advances visualization and interactivity with analytics, which could encroach on partners that provide advanced analytics capabilities like discovery and exploration. Organizations looking for analytic discovery tools should consider this technology overlap. Teradata provides a broad and integrated big data platform and architecture with advanced resource management to process data and analytics efficiently. In addition it provides archiving, auditing and compliance support for enterprises. It can support a range of data refining tasks including fast data landing and staging, lower workload concurrency, and multistructured and file-based data.

Teradata efforts are also supported in what I call a big data or data warehouse as a service and is called Teradata Cloud. Its approach is can operate across and be accessed from a multitenant environment where it makes its portfolio of Teradata, Aster and Hadoop available in what they call cloud compute units. This can be used in a variety of cloud computing approaches including public, private, hybrid and for backup and discovery needs. It has gained brand name customers like BevMo and Netflix who have been public references on their support of Teradata Cloud. Utilizing this cloud computing approach eliminates the need for placing Teradata appliances in the data center while providing maximum value from the technology. Teradata advancements in cloud computing comes at a perfect time where our information optimization research finds that a quarter of organizations now prefer a cloud computing approach with eight percent prefer it to be hosted by a supplier in a specific private cloud approach.

vr_Info_Optimization_10_reasons_to_change_information_availabilityWhat makes Teradata’s direction unique is moving beyond its own appliances to embrace the enterprise architecture and existing data sources; this makes it more inclusive in access than other big data approaches like those from Hadoop providers and in-memory approaches that focus more on themselves than their customers’ actual needs. Data architectures have become more complex with Hadoop, in-memory, NoSQL and appliances all in the mix. Teradata has gathered this broad range of database technology into a unified approach while integrating its products directly with those of other vendors. This inclusive approach is timely as organizations are changing how they make information available, and our information optimization benchmark research finds improving operational efficiency (for 67%) and gaining a competitive advantage (63%) to be the top two reasons for doing that. Teradata’s approach to big data helps broaden data architectures, which will help organizations in the long run. If you have not considered Teradata and its UDA and new QueryGrid technologies for your enterprise architecture, I recommend looking at them.

Regards,

Mark Smith

CEO & Chief Research Officer

Many businesses are close to being overwhelmed by the unceasing growth of data they must process and analyze to find insights that can improve their operations and results. To manage this big data they find a rapidly expanding portfolio of technology products. A significant vendor in this market is SAS Institute. vr_Big_Data_Analytics_02_defining_big_data_analyticsI recently attended the company’s annual analyst summit, Inside Intelligence 2014 (Twitter Hashtag #SASSB). SAS reported more than $3 billion in software revenue for 2013 and is known globally for its analytics software. Recently it has become a more significant presence in data management as well. SAS provides applications for various lines of business and industries in areas as diverse as fraud prevention, security, customer service and marketing. To accomplish this it applies analytics to what is now called big data, but the company has many decades of experience in dealing with large volumes of data. Recently SAS set a goal to be the vendor of choice for the analytic, data and visualization software needs for Hadoop. To achieve this aggressive goal the company will have to make significant further investments in not only its products but also marketing and sales. Our benchmark research on big data analytics shows that three out of four (76%) organizations view big data analytics as analyzing data from all sources, not just one, which sets the bar high for vendors seeking to win their business.

In the last few years SAS has been investing heavily to expand its portfolio in big data. Today its in-memory infrastructure can operate within Hadoop, execute MapReduce jobs, access the various commercial distributions of Hadoop, conduct data preparation and modeling in Hadoop and extend it to its data and visual discovery and exploration tools. SAS has architected its analytics tools and platform to use Hadoop’s Pig and Hive interfaces, apply MapReduce to process large data sets and use Hadoop Distributed File System (HDFS) to store and access the big data. To exploit Hadoop more deeply, the SAS LASR Analytic Server (part of SAS Visual Analytics) connects directly to HDFS to speed performance. SAS LASR Analytic Server is an in-memory computing platform for data processing and analysis that can scale up and operate in parallel within Hadoop to distribute the computation and data workloads. This flexibility in the architecture enables users to adapt SAS to any type of big data, especially Hadoop deployments, for just about any scale and configuration. To work with other database-oriented technologies the company has built technical partnerships not only with major players Teradata and SAP but also with the new breed of Hadoop vendors Cloudera, Hortonworks and Pivotal, as well as with IBM BigInsights. SAS also engineered access to SAP HANA, which establishes further integrated into SAP’s data platform for analytics and other applications.

At the Inside Intelligence gathering, SAS demonstrated its new Visual Statistics product. Like its Visual Analytics this one is available online for evaluation. It offers sophisticated support for analysts and data professionals who need more than just a visually interactive analytic tool of the sort that many providers now sell. Developing a product like Visual Statistics is a smart move according to our research, which finds that predictive analytics and statistics is the most important area of big data analytics, cited by 78 percent of organizations. At this point visual and data discovery are most common, but we see that users are looking for more. SAS Visual Statistics can conduct in-memory statistical processing and compute results inside Hadoop before the data is transferred to another analytic data repository or read directly into an analytics tool. A demonstration of these capabilities at the analyst summit revealed how these capabilities along with the use of tools in SAS 9.4 could raise the bar for sophisticated analytics tools for business.

vr_Info_Optimization_04_basic_information_tasks_consume_timeSAS also has a data management software suite for data integration, quality, mastering and governance and is working to make the product known for its big data support. This is another important area: Our research in big data analytics finds quality and consistency of data to be significant challenges for 56 percent of organizations and also that 47 percent are not satisfied with integration of information for creating big data analytics. SAS is expanding to provide data quality tools for Hadoop. Its portfolio is expansive in this area, but it should take steps to market these capabilities better, which spokespeople said it will do in 2014. Our recent research in information optimization found that organizations still are spending disproportionate amounts of time in preparing data (47%) and reviewing it (45%) for analytics. They need to address these difficulties to free their analysts to spend more time on analysis that produces recommendations for decision-makers and to collaborate on business improvement. SAS’s efforts to integrate data and analytics should help reduce the time spent on preparation and help analysts focus on what matters.

SAS also will expand its SAS Stream Processing Engine with a new release coming by midyear. This product can process data as it is being generated, which facilitates real-time analytics – that’s the vr_oi_how_operational_intellegence_is_usedthird-most important type of big data analytics according to our research. Applying analytics in real time is the most important aspect of in-memory computing for two-thirds (65%) of organizations and is critical as SAS expands its SAS LASR Analytic Server. Our benchmark research on operational intelligence shows that the processing of event data is critical for areas like activity or event monitoring (said 62% of participants) and alerting and notification (59%). SAS will need to expand its portfolio in these areas but it is fulfilling on what I call the four types of discovery for big data.

SAS also is moving deeper into cloud computing with support for both private and public clouds through investments in its own data centers. Cloud computing is an increasingly popular approach to building a sandbox environment for big data analytics. Our research finds that more than one-fifth of organizations prefer to use cloud computing in an on-demand approach. SAS will have to provide even more of its portfolio using big data in the cloud or risk customers turning to Amazon and others for processing and potentially other computing uses. SAS asserts it is investing and expanding in cloud computing.

vr_Big_Data_Analytics_15_new_technologies_enhance_analyticsSAS’s efforts to make it easier to work with big data and apply analytics is another smart bet; our research finds that most organizations today don’t have enough skilled resources in this area. One way to address this gap is to design software that is more intuitive, more visual and more interactive but sophisticated in how it works with the primitives of Hadoop; SAS is addressing this challenge. Our research finds growth of in-memory (now used by 42%) and Hadoop (50%) technologies, which will have more impact as they are applied directly to business needs and opportunities. SAS is at the intersection of data management and analytics for big data technologies, which could position it well for further growth in revenue. SAS is betting that big data will become a focal point in many deployments and they can help unify data and analytics across the enterprise. Our research agenda for 2014 finds this to be the big opportunity and SAS is fixated on being the vendor of choice for it. If you have not examined how SAS can connect big data architectures and facilitate use of this important technology, it will be worth your time to do so.

Regards,

Mark Smith

CEO & Chief Research Officer

Mark Smith – Twitter

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