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BitYota Introduces Breakthrough Data Warehouse Technology

Bityota_logoData Warehouse Service (DWS) provider BitYota recently announced latest release of 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-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 3rd 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, BitYota.

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

  • BitYota’s data collection framework is providing a unified way to funnel data from a wide variety of upstream 3rd party API sources like Mixpanel and Flurry and NoSQL databases like MongoDB for real-time analysis. BitYota is making its MongoDB and Mixpanel extract plugins with source code available through its public Git Repository (https://github.com/bityota-support/downloads). 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 Extract-Load-Transform (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. Since no external data pipelines need to be built, users are able to make business decisions on insights much faster as data in available in minutes instead of hours, while also reducing cost, complexity and operational steps.
  • Availability of compute and storage groups manageable by end users. Building on BitYota’s unique capability to separate and elastically grow/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 that enable faster loads, queries, scan and join optimizations as well as improved aggregation and exploration directly on semi-structured JSON. Our customers have seen performance improvements in the range of 20% to 40%.
  • 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.

At CloudOn, we believe in a human-first design philosophy, the core of which lies in delivering delightful experiences,” said Jay Zaveri, Chief Product Officer, CloudOn, a cloud storage provider enabling users to create, review and share files from any device. “Over 80 million documents have been created and edited on CloudOn and our ambition has always been to provide a gesture-first experience so we can push the boundaries on mobile content creation in ways that were never possible. In order to do so, we have to crunch data on 1 billion user actions (collected as raw JSON) every quarter that inform us on how we can fulfill this bold promise. BitYota serves as a cost-effective, high performance data warehouse that enables us to analyze raw session data from millions of users in seconds. A traditional analytics system just wouldn’t work given the price and the flexibility we need. We load data into BitYota every hour, store, and explore this raw data. We look deep into user behavior with complete ease, and run ad-hoc queries, for example understanding churn and usage funnels, all using SQL over native JSON.”

 

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