Cloud Data Warehouse

Cloud Data Warehouse for Semi-Structured Data

We are a Data Warehouse Service (DWS) available on major cloud providers like Rackspace and Amazon. We are designed from the ground up for high performance analytics on JSON data from fast-changing applications including web & mobile analytics and NoSQL stores like MongoDB. We don’t impact your operational store or app and best of all, as a fully managed service, we take the headache out of having to set up and manage another data platform.
With BitYota, you can easily consolidate your data from multiple sources for adhoc exploration, regular reports and complex analysis. Focus your time on driving insights from your data. Leave the hassles of collecting and housing data and management to us.


MongoDB JSON / BSON Data Integration

Easy data integration

Use BitYota’s free MongoDB oplog tool or our upcoming streaming events service to collect and load data in native JSON/BSON format from your database or mobile/web app. No need for a complex ETL and no need to modify anything, even if you add new attributes to your JSON documents.
Or just point us to where your data is stored in the cloud and we do the rest, understanding the rate of arrival/change of new data and seamlessly loading only the new data on your chosen schedule. All from within our service. The data is immediately available after load – to view, to analyze, to aggregate, join with something else, as you see fit.


Analyze Customer Value

Complete customer view

Get a complete view of your customers when all of your data is available in the same place. From web traffic, user profiles to transactions. Join, aggregate and run simple or complex analysis on your customer data that may be coming from many different sources. Strategically increase business performance with understanding user interaction and the impact of seasonal trends or events.


Real-time analytics at scale

Fast analytics at scale

Load your JSON documents directly into tables using BitYota’s semi-structured data types like JSON, XML and key-value. No need to do any pre-load transformation or upfront data modeling. Once loaded, you can directly access the attributes within that JSON document using standard SQL as if they were regular columns, even if they are nested within arrays.
BitYota provides out-of-the-box high performance analytics over native JSON. Additionally, we allow easy transformation of a subset of your JSON attributes into columns for faster access. BitYota supports multiple table layout (row, columnar) and partitioning schemes (hash, range, random) for optimal query performance.


SQL over JSON, ODBC with Tableau, Python, Perl and Javascript

Familiar tools

Use SQL OLAP operators or script based UDFs in Javascript, Python or Perl for complex analysis including joins, aggregations, window functions, etc. UDF support enables developers to leverage their existing data manipulation and analytics algorithms. Whether you are a data scientist, engineer, business analyst or product manager, get broader access to relevant data and analytics with the ability to use SQL directly over JSON data.


Event driven mobile analytics

Event driven decisions

By ensuring only new data is loaded, we are able to incrementally load large volumes of data at high velocity. You don’t need to use any third party data integration tools nor do they need any transformation from standard  file formats. The data is then immediately available for analysts to view, to analyze, to aggregate, and to join with other data sources, as needed. Discover exactly what to improve on your product.


Predictive analysis through an open API

Open and Accessible

Access BitYota from wherever you are in the world, you just need an internet connection. Analysts, developers and product owners can write analytical queries to generate timely insights without having to rely on other developers. Open the gates to the data in your business. Quickly discover complex business metrics such as user value, effect of marketing, trending analysis, and then easily progress into more predictive explorations like viral features, identifying effective customer acquisition channels, etc using large volumes of their raw historical data.