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Bityota Enables Unified Data Collection and Processing from Third-Party APIs, NoSQL Databases

Flagship data warehouse service delivers streamlined analytics.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Data warehouse service (DWS) provider BitYota has updated its flagship DWS for big data analytics. This update delivers the platform’s data collection framework, an in-database processing pipeline for ELT (extract, load, and transform), enhanced resource management, and platform-specific improvements to further boost analytics performance. The new capabilities provide greater power, versatility, and convenience to one of the industry’s leading platforms for multi-structured data analytics.

“Some of the most valuable data available today comes from external sources such as third-party analytics APIs. With this new version of our data warehouse service, BitYota offers users the ability to bring data in from numerous external sources, process it using their custom business rules, and immediately begin interrogating data in multiple structures, using industry-standard SQL query language, all from within the DWS,” said Dev Patel, CEO of BitYota. 

The new DWS version also offers a range of features and upgrades that provide new performance and flexibility:

  • BitYota’s data collection framework provides a unified way to funnel data from a wide variety of upstream third-party API sources such as Mixpanel and Flurry as well as NoSQL databases (such as MongoDB) for real-time analysis. BitYota is making its MongoDB and Mixpanel extract plugins with source code available through its public Git Repository. These are available for use under the Apache 2.0 license, enabling users to modify code for their use in their environment.

  • The ability to build a custom data pipeline using SQL within the DWS that can be run on a schedule. By using standard SQL or user-defined functions, customers can now leverage the true benefits of ELT to extract and load the data in its raw form and use the powerful BitYota massively parallel-processing (MPP) engine for data transformations such as data quality checks, aggregations on data arrival boundaries, creation of cubes, and other data manipulation tasks directly in the DWS. No external data pipelines need to be built, so users can make business decisions on insights much faster because data is available in minutes instead of hours.  This also reduces cost, complexity, and operational steps.

  • Availability of compute and storage groups manageable by end users. Building on BitYota’s capability to separate and elastically grow or shrink compute and storage nodes within a cluster, this feature collects BitYota instances running on these nodes into discrete storage or compute groups that can be assigned to individual users or business roles. This eliminates resource contention between long- and short-running jobs and enables better allocation of resources to improve performance and ability to meet service-level agreements (SLAs).

  • Numerous performance improvements enable faster loads, queries, scan and join optimizations, as well as improve aggregation and exploration directly on semi-structured JSON. Our customers have seen performance improvements between 20 and 40 percent.

The BitYota DWS is now available in multiple new configurations. An entry-level free node with up to a 1TB of storage and more powerful Premium and Enterprise offerings that can scale up from 6TB to 100s of TBs, creating multiple affordable price/performance points to scale your DWS as your needs and usage grows.

This release of BitYota’s DWS is immediately available at no additional cost. For more about BitYota and its cloud-based DWS, visit

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