The Story of Batching to Streaming Analytics at Optimizely

Our mission at Optimizely is to help decision makers turn data into action. This requires us to move data with speed and reliability. We track billions of user events, such as page views, clicks and custom events, on a daily basis. To provide our customers with immediate access to key business insights about their users has always been our top most priority. Because of this, we are constantly innovating on our data ingestion pipeline.

In this article we will introduce how we transformed our data ingestion pipeline from batching to streaming to provide our customers with real-time session metrics.

Motivations 

Unification. Previously, we maintained two data stores for different use cases - HBase is used for computing Experimentation metrics, whereas Druid is used for calculating Personalization results. These two systems were developed with distinctive requirements in mind:

As our business requirements evolve, however, things quickly became difficult to scale. Maintaining a Druid + HBase Lambda architecture (see below) to satisfy these business needs became a technical burden for the engineering team. We need a solution that Continue reading