Control Systems and Computers, N2, 2018, Article 6


Upr. sist. maš., 2018, Issue 2 (274), pp. 51-67.

UDK 004.65:004.7:004.75:004.738.5

A.A. Oursatyev, PhD in Techn. Sciences, Leading Research Associate, International Research and Training Centre of Information Technologies and Systems of the NAS and MES of Ukraine, Glushkov ave., 40, Kyiv, 03187, Ukraine,


Introduction. The article is a continuation of the Big Data and tools study, which is being transformed into technology of the new generation and architecture of the BD platforms and storage for the intelligent output. In this part the review of DB Teradata is presented. The main attention is paid to the issues of changing the infrastructure, the tool environment and the platform for identifying the necessary information and new knowledge from the Big Data, the initial information about the product is given in the product general description.

Purpose. The purpose is to consider and evaluate the application effectiveness of the infrastructure solutions for new developments in the Big Data study, to identify new knowledge, the implicit connections and in-depth understanding, insight into phenomena and processes.

Methods. The informational and analytical methods and technologies for data processing, the methods for data assessment and forecasting, taking into account the development of the most important areas of the informatics and information technology.

Results. Teradata is a relational system of the parallel processing, in which the architecture is used without general access. It is based on technology, consisting of equipment, software, databases and consulting. The system moves data to the storage area where they can be called up and analyzed.

Having created a solution (Database Appliance) between specialized hardware and software, Teradata is successful in the Data Warehouse for a long time, achieving performance in very large databases in the analytical tasks in making strategic decisions. But the Teradata Database Appliance is an instrument with the consequences as follow: there is no possibility of setting up the equipment for the problem space. When you need to scale hardware in one direction or another, you should use the entire device. There are no options for using cloud or elastic style optimization. It is also noted that Teredata has an immature level of data abstraction. Small changes in programming can be made in comparison with other languages RDBMS.

Teredata responded in a timely manner to the urgent need for BigDate analysis and first of all these new formats of media sources. The first solution was Hadoop for the Enterprise, a flexible set of hardware, software and services for integrating Hadoop into the Teradata environment. Then it was a platform for detecting data Teradata Aster Discovery. The analytical functions SQL, SQL-MapReduce®, Graph, time series functions, statistical methods, text analytics and much more for BigDate study are developed and used, the access to multi-structured data is provided in ApacheHadoop ™, Teradata Data Warehouse and other relational database management system (RDBMS).

Teradata QueryGrid ™ ecosystem provides high-performance data access, processing and virtual delivery to systems in heterogeneous analytical environment. This is a kind of matrix that uses parallel data transfer between the exchange objects. The idea of an ecosystem approach that uses Hadoop along with the relational and other environments to cover different types of data is reduced to the task of linking nodal information points stored in the different environments. The accepted unified data architecture Teradata ™ (UDA) does not contradict the unified representation of data, without their movement, — the concept of the logical data stores.

Conclusion. According to Gartner’s analysts in 2014 Teradata received three awards: leadership in the field of analytics, recognition of the Teradata® Unified Data Architecture ™ architecture, and leadership in the use of Hadoop for the large amounts of data. Since 2015, data warehouses have expanded due to the several types of data access, processing mechanisms and repositories. In this regard, in the Gartner report 2017, it is noted that Teradata has built a data management platform that takes into account all the uses of data warehouses: the traditional, operational, logical and context-independent. This reflects the approach to the storage represented by the unified Teradata® UDA ™ data architecture.

Teradata offers also provide solutions for cloud data warehouses both on a private managed cloud and on the infrastructure of a public cloud provider. This facilitates the integration of cloud and local networks in hybrid configurations. Teradata IntelliCloud ™ is the next generation of secure cloud solutions that provides data and analytics software as a SaaS service.

Also, Teradata provides the solutions for cloud data warehouses both on a private managed cloud and on the infrastructure of a public cloud provider. This facilitates the integration of cloud and local networks in hybrid configurations. Teradata IntelliCloud ™ is the next generation of secure cloud solutions that provides data and analytics software as a SaaS model.

Download full text! (In Russian).

Keywords: MPP, Logical Data Warehouse LDW, Warehouse Appliance, SN (Shared Nothing), Teradata Aster Discovery, Teradata IntelliCloud™, SaaS, Teradata QueryGrid™, Unified Data Architecture Teradata™ (UDA).


52. Teradata, [online] Available at: <> [Accessed 21 Oct. 2012].

 53. DEWITT, DAVID, GRAY, JIM. Parallel database systems: the future of highly efficient database systems, [online] Available at: <> [Accessed 02 Apr. 1995].

 54. Teradata’s blog: Teradata, a DBMS parallel to birth, [online] Available at: <> [Accessed 03 Dec. 2012].

 55. Blog of the company Teradata: Statistics in DBMS Teradata, [online] Available at: <> [Accessed 01 Feb. 2013].

 56. Physical design of storage structures in the Teradata DBMS, [online] Available at: <> [Accessed 16 Jan. 2014].

 57. Teradata’s blog: Additional physical modeling techniques in Teradata, [online] Available at: <> [Accessed 17 Jan. 2014].

 58. Teradata’s blog: Speed or volume? Automation of management of storage systems with heterogeneous characteristics, [online] Available at: <> [Accessed 11 Jan. 2013].

 59. MapReduce and Teradata Aster SQL-MapReduce®, [online] Available at: <> [Accessed 16 Jan. 2013].

 60. Teradata Aster Discovery Portfolio, [online] Available at: <> [Accessed 16 Sept. 2016].

 61. Teradata Aster Analytics, [online] Available at: < downloads/Brochures/Teradata_Aster_ Discovery_Platform_EB7573.pdf?processed=1> [Accessed 16 Jan. 2014].

 62. Dean J. MapReduce: Simplified Data Processing on Large Clusters, [online] Available at: <http://research.> [Accessed 16 Oct. 2016].

 63. What is data silo? / Definition…, [online] Available at: <> [Accessed 16 Jan. 2016].

 64. Teradata Unified Data Architecture™, [online] Available at: <> [Accessed 16 Jan. 2016].

 65. Take a Giant Step with Teradata QueryGrid, [online] Available at: <> [Accessed 15 Jan. 2016].

 66. Teradata QueryGrid User Guide, Teradata Documentation, [online] Available at: < ItemID=1007085> [Accessed 28 Febr., 2017].

 67. Edjlali, R., Ronthal, Adam M., Greenwald R. et al. Magic Quadrant for Data Management Solutions for Analytics, [online] Available at: <> [Accessed 28 Febr., 2017].

 68. Teradata Achieves Highest Position for Completeness of Vision in Data Management Solutions for Analytics Magic Quadrant, [online] Available at: <> [Accessed 28 Febr., 2017].

 69. Teradata Announces the World’s Most Powerful Analytic Database, Available Everywhere, [online] Available at: <> [Accessed 12 Sept. 2016].

Received 16.01.2018