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BitYota Data Warehouse as a Service on Azure

February 7, 2015 Leave a comment

I’m excited to have joined BitYota as an evangelist for our data warehouse as a service after spending the last 20 years helping customers implement on-premise data warehouse architectures. The time is right for the cloud data warehouse – the technology has matured, networks have matured, and the mindset of the enterprise data architect is turning to the cloud.

Inside BitYota we hear a lot about the principles the founders wanted to establish for the service – analytics over multiple sources of data, rapid agile delivery of results, and built for the cloud. It’s where the market for data warehouse is headed long term.

Late last year we announced a partnership with Microsoft to deliver BitYota on the Azure cloud. A day doesn’t go by when someone doesn’t ask me how we fit in that ecosystem. I’m going to layout my answer utilizing a diagram provided by Microsoft here: http://www.microsoft.com/en-us/server-cloud/ (source: Chappell and Associates)

Azure Four Quadrants

The four quadrants in this diagram categorize the current database offerings on Azure; Operational SQL Technologies, Operational NoSQL Technologies, Analytic SQL Technologies, and Analytic NoSQL Technologies.   We define the Operational Data as transactional, with lots of inserts, updates and deletes.   The Analytic Data is typically queries, with lots of search and analytics.

For our purposes, we will be looking more closely at the Analytic data column.   Here, we can see that the characteristics of the NoSQL quadrant requires parallel data loading, fast parallel queries and response times, and the ability to manipulate structured and unstructured data.

A Closer Look at the Analytic Data Offerings

Analytic Data on Azure

The SQL quadrant requires a SQL interface to access the data, stores data in rows and columns, and enables data cubes for ease of end user reporting.

Now imagine an Azure data offering that loads data in parallel, performs massively parallel queries with response times in seconds, using your existing SQL tools and scripts, over relational data and JSON and XML. You’ve just imagined BitYota on Azure!

MPP Data Warehouse Service on Azure

BitYota On Azure

Of course there is much more to the service than what is shown on this diagram, but I hope it helps to communicate where BitYota fits in the Azure data ecosystem today.   This is just the beginning for BitYota as the service evolves to enhance the customer experience doing discovery and analysis of data using Azure.