Split Integrates with Amazon S3
December 15, 2020

Split announced a strategic integration with Amazon’s Simple Storage Service (Amazon S3), an object storage service that offers scalability, data availability, security and performance.

Split is making it easy to leverage data no matter where it lives—in a customer data platform (CDP), in an application performance management (APM) tool and now in Amazon S3.

“Our new data integration with AWS is the first of its kind to allow companies to bring customer data like engagement, behavioral and transactional data directly from an S3 data lake into our feature delivery platform,” said Patricio "Pato" Echagüe, co-founder and CTO of Split.io.

Once configured, Split ingests millions of customer events per minute as Parquet files from Amazon S3 to combine with feature flag data using a patented attribution logic that measures the impact of a new feature on key business metrics. Users can also tap into historical data and retroactively calculate new metrics from previous experiments. This allows for greater speed and efficiency in common cases where a user may have forgotten to define a metric prior to an experiment or the results led to further questions that required additional data to answer. By ingesting historical data from Amazon S3, users can add a new metric to a completed experiment and have enough data to reach statistically significant results in minutes.

Echagüe continued, “Early next year, Split will also be able to send feature flag data to Amazon S3, unlocking endless possibilities for users to combine with other types of data (like customer or product data) to run deeper analyses in downstream destinations such as a BI tool, product analytics platform or data warehouse. Running Split experiments and such analyses allow product and engineering teams to iterate faster on new features, align on roadmap priorities and ensure that every release will drive a positive business outcome.”

As an example throughout an experiment, customer data can be brought into Split to quantify a new feature’s impact on engagement, conversion or average order value (AOV). Feature flag data is then sent to other tools via Amazon S3 to validate these results and, by digging into the customer experience with a new feature, determine why certain metrics changed.

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