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1010data, Inc. is a Platform as a service and Software as a service company that provides a cloud-based software platform and associated services for business analytics and database publishing of large data sets.[1][2][3][4]


1010data was founded by Wall Street veterans Joel Kaplan and Sandy Steier in 2000 to bring the technologies of array programming, columnar databases and distributed computing, which had for decades proved valuable in the financial services industry but were not well known in mainstream IT, to bear on the nascent field of big data analytics.[5] 1010data was an early pioneer of cloud computing, providing[6] its analytics platform exclusively over the Web for almost a decade before the term, and the practice in its modern sense, became common.

In contrast to the bulk of modern database technology, which is based upon the relational paradigm, as well as to recent non-relational challengers in the big data analytics field such as Hadoop, the roots of 1010data's technology lie in the array programming model pioneered by the early programming language APL. Along with Kdb+, the high-throughput time series database from Kx Systems, with which it shares a significant common heritage[7] in the K programming language, the analytical database at the core of 1010data's offering represents one of the more successful exponents of array programming in the modern era, powering large-scale data analytics for hundreds of companies working with trillions[8] of rows of data.

In 2010, 1010data announced a $35M minority equity investment from Norwest Venture Partners.[9]

In 2015, 1010data was acquired for $500M by Advance Publications.[10]


The 1010data platform is strongly vertically-integrated, attempting to provide all the functionality necessary for the various stages of the big-data lifecycle, from ETL, through data storage, analytics, visualization, and dashboard/application building, without relying on third-party software for some of these functions. This runs counter to the modern trend towards large number of interacting components (for example, ETL tools, databases, and BI tools) in a heterogeneous ecosystem. The platform comprises a columnar analytical database, an interactive, HTML-based spreadsheet-like interface (styled the "Trillion Row Spreadsheet"[11]), a data visualization engine, web-based tools for developing and deploying interactive applications that run within the database, a proprietary XML-based language for representing queries and applications, an HTTP-based API for querying the system non-interactively, and various standalone tools for interacting with that API.

1010data partners with a number of data providers to provide access via its platform to data sets and data-driven applications in various areas.

Array-programming model

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1010data counts among its customers[12] firms such as Procter & Gamble, the New York Stock Exchange,[13] Dollar General,[14] Rite Aid, Sysco, as well as many Wall Street banks and financial-services companies.

Stock exchange

The New York Stock Exchange has used [15][16] 1010data since 2001 to distribute market data, e.g. end-of-day trades and quotes information, and more recently analytics.

Use in MBS/ABS analytics

1010data is widely used[17][18][19][20] in the area of mortgage-backed security and asset-backed security analytics, especially for analysis that is granular to the level of individual loans (e.g. using the characteristics and individual payment histories of the loans in a pool to estimate the risk of prepayment or default. Datasets for such analysis can comprise billions of records (the complete history of tens or hundreds of millions of loans each of which is serviced monthly over the course of years). 1010data became popular for such analysis in the booming MBS market of the early 2000's for a number of reasons: it was capable of running complex queries in seconds or minutes even on such large granular datasets; it was capable of linking together disparate datasets (e.g. granular loan history and housing price indices or consumer credit data), its columnar database engine was well-suited to time-series analysis such as modeling the lagged effect of such indices on loan repayments and defaults; and the company partnered with data providers such as CoreLogic and Equifax to provide immediate access to licensees of those provider's databases on the plaform. In the runup to the 2008 financial crisis, accurate and fast analytics at the individual loan level became all the more important to understand the fast-moving market changes and 1010data experienced rapid growth. 1010data was used[21][22] by the investment management firm Paulson & Co. to model and predict the effect of a slowing housing market on subprime mortgage defaults, leading to a successful multi-billion-dollar bet against the subprime mortgage market.

Use in retail data analytics

1010data is used extensively in the retail and CPG industry for data warehousing and analytics.

See also


  1. Bloor, R. (May 23, 2011). Big Data Analytics - This Time It's Personal. The Bloor Group. http://1010data.com/images/Downloads/PDFs/big-data-analytics.pdf. 
  2. "16 Top Big Data Analytics Platforms - InformationWeek". http://www.informationweek.com/big-data/big-data-analytics/16-top-big-data-analytics-platforms/d/d-id/1113609. 
  3. Morabito, Vincenzo (2015-01-31) (in en). Big Data and Analytics: Strategic and Organizational Impacts. Springer. ISBN 9783319106656. https://books.google.com/books?id=9lx0BgAAQBAJ. 
  4. Resources, Management Association, Information (2016-04-20) (in en). Big Data: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications. IGI Global. ISBN 9781466698413. https://books.google.com/books?id=BKEoDAAAQBAJ. 
  5. Denne, Scott. "Big Data Success Stories: 1010data". http://blogs.wsj.com/venturecapital/2011/11/02/big-data-success-stories-1010data/. 
  6. Pethuru, Raj (2014-03-31) (in en). Handbook of Research on Cloud Infrastructures for Big Data Analytics. IGI Global. ISBN 9781466658653. https://books.google.com/books?id=m95GAwAAQBAJ. 
  7. "1010data: A Kx Systems Case Study". https://kx.com/_papers/Kx_Case_Study-1010data.pdf. 
  8. Simon, Phil (2013-03-05) (in en). Too Big to Ignore: The Business Case for Big Data. John Wiley & Sons. ISBN 9781118641866. https://books.google.com/books?id=1ekYIAoEBrEC. 
  9. Tam, Pui-Wing. "Norwest: Starting to Put $1.2 Billion to Work". http://blogs.wsj.com/digits/2010/03/09/norwest-starting-to-put-12-billion-to-work/. 
  10. http://blogs.wsj.com/venturecapital/2015/08/03/in-big-data-deal-advancenewhouse-acquires-1010data-for-500m/
  11. Granville, Vincent (2014-03-24) (in en). Developing Analytic Talent: Becoming a Data Scientist. John Wiley & Sons. ISBN 9781118810095. https://books.google.com/books?id=tp46AwAAQBAJ. 
  12. "Customers". https://www.1010data.com/company/customers/. 
  13. "1010data Makes Big Splash with Big Data -- Enterprise Systems". https://esj.com/articles/2008/08/20/1010data-makes-big-splash-with-big-data.aspx. 
  14. Dollar General Corporation Becomes First Leading Retailer to Outsource Their Enterprise Data Warehouse to the 1010data Cloud.. 2009-10-22. https://www.highbeam.com/doc/1G1-209755780.html. 
  15. Groenfeldt, Tom. "NYSE Delivers Analyzed Data To Clients". http://www.forbes.com/sites/tomgroenfeldt/2012/11/13/nyse-delivers-analyzed-data-to-clients/#50cd4e565136. 
  16. "NYSE is Refining its Data Services - Wall Street & Technology". http://www.wallstreetandtech.com/trading-technology/nyse-is-refining-its-data-services/d/d-id/1259862?. 
  17. Chinco, Alex; Mayer, Christopher (2016-02-01). "Misinformed Speculators and Mispricing in the Housing Market" (in en). Review of Financial Studies 29 (2): 486–522. Template:Citation error. ISSN 0893-9454. http://rfs.oxfordjournals.org/content/29/2/486. 
  18. Mayer, Christopher; Morrison, Edward; Piskorski, Tomasz; Gupta, Arpit (2014-09-01). "Mortgage Modification and Strategic Behavior: Evidence from a Legal Settlement with Countrywide". The American Economic Review 104 (9): 2830–2857. Template:Citation error. http://www.ingentaconnect.com/content/aea/aer/2014/00000104/00000009/art00008. 
  19. Goodman, Laurie (March 2012). "The U.S. Residential Mortgage Market: Sizing the Problem and Proposing Solutions". CFA Institute Conference Proceedings Quarterly 29 (1). Template:Citation error. http://www.cfapubs.org/doi/abs/10.2469/cp.v29.n1.8. 
  20. Glaeser, Edward L.; Sinai, Todd (2013-08-19) (in en). Housing and the Financial Crisis. University of Chicago Press. ISBN 9780226030616. https://books.google.com/books?id=MW8zAAAAQBAJ. 
  21. Teitelbaum, Richard (2015-08-31) (in en). The Most Dangerous Trade: How Short Sellers Uncover Fraud, Keep Markets Honest, and Make and Lose Billions. John Wiley & Sons. ISBN 9781118505212. https://books.google.com/books?id=d1PKCQAAQBAJ. 
  22. Zuckerman, Gregory (2009-11-03) (in en). The Greatest Trade Ever: The Behind-the-Scenes Story of How John Paulson Defied Wall Street and Made Financial History. Crown Publishing Group. ISBN 9780385529938. https://books.google.com/books?id=pJO1LdAZ0KEC. 

External links