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Diego Ferreira

Birdzi Provides Digital Shopper Engagement with DataStax Enterprise

By Diego FerreiraJanuary 10, 2017

This post is one in a series of quick-hit interviews with companies using Apache Cassandra™ and/or DataStax Enterprise (DSE) for key parts of their business. For this interview, we talked with Ashish Shrowty, Head of Data Engineering at Birdzi.

DataStax: Hello Ashish! Could you please tell us about Birdzi and your role there?
Ashish: Birdzi is a shopper engagement and analytics company. We provide retailers, CPG companies and wholesalers an integrated omni-channel platform that helps them deeply connect with their shoppers in-store while shopping as well as out-of-store. My role at Birdzi is to head big data engineering and analytics i.e. harnessing all the large amount of rich data that we generate and delivering valuable insights to our customers.

DataStax: Did you use a different technology before DataStax Enterprise?
Ashish: We used PostgreSQL as our database technology. We are still using it effectively on the OLTP side.

DataStax: Why did you pick DataStax Enterprise? What kind of data is stored there?
Ashish: We were looking for a scalable, mature and dependable technology that we could grow with. Apache Cassandra™ offers a robust peer-to-peer architecture that has great momentum in the marketplace with high-growth companies. Additionally, DataStax Enterprise provides integrated Search (Apache Solr™) and Analytics (Apache Spark™) offering that we could immediately leverage for some of the use cases. Last but not the least, DataStax has a great start-up program for small companies like us. This was a key consideration for us when deciding on the target architecture. Currently we have all the transaction data as well as click-through data stored in Apache Cassandra™.

DataStax: What features from the DataStax Enterprise stack are you using at the moment?
Ashish: We are utilizing DSE Search as well as DSE Analytics for a couple of applications. We are using the strong, flexible indexing capabilities of DSE Search to create a ‘data-cube’. This virtual cube forms the backend that is used within our Campaign Analytics application that lets the user slice and dice impressions, activations and redemptions in real-time. We use DSE Analytics to crunch through the transaction data to do customer segment calculations as well as generate aggregates that are surfaced to the users via a ‘Scorecards’ application.

DataStax: What advice would you give to other startups that are thinking about using Apache Cassandra™ for the first time in their solutions?
Ashish: There is a lot of great information that is available on the DataStax website as well on Stack Overflow and other websites. DataStax experts are very active in the community and I have learned a lot by interacting with these experts and going through the documentation. I would only reiterate that understanding the data model and query paths is key to building a successful application with Apache Cassandra™. For first time users, it is important to take 1-2 representative use cases and iteratively build out functions and features and learn along the way. Also, before spending too much time scratching your head, go to Stack Overflow or DataStax documentation. 8/10 times, I found that someone else had already faced the issue and had posted a solution.



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