How Gusto Built Their Data Pipeline with Amazon Redshift
Gusto, founded in 2011, is a company that provides a cloud-based payroll, benefits and workers’ compensation solution for businesses. Their business has grown steadily over the years, currently topping to around 60 thousand customers. By early 2015, there was a growing demand within the company for access to data. Up until then, the engineering team and product managers were running their own ad-hoc SQL scripts on production databases. There was obviously a need to build a data-informed culture, both internally and for their customers. When coming to the crossroad to either build a data science or data engineering team, Gusto seems to have done the right choice: first build a data infrastructure which then would support analysts in generating insights and drawing prediction models.
The first step for Gusto was to replicate and pipe all of their major data sources into a single warehouse. The warehouse choice landed on an AWS Redshift cluster, with S3 as underlying data lake. Moving data from production app databases into Redshift was then facilitated with Amazon’s Database Migration Service. On the other side of the pipeline, Looker is used as a BI front-end that teams throughout the company can use to explore data and build core dashboards. Aleph is a shared web-based tool for writing ad-hoc SQL queries. Finally, monitoring (in the form of event tracking) is done by Snowplow, which can easily integrate with Redshift, and as usual, Airflow is used to orchestrate the work through the pipeline.
Building such pipeline massively simplified data access and manipulation across departments. For instance, analysts can simply build their own datasets as part of an Airflow task, and expose it to Looker to use in dashboards and further analyses.
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