Slack’s data team works to “provide an ecosystem to help people in the company quickly and easily answer questions about usage, so they can make better and data informed decisions.” To achieve that goal, that rely on a complex data pipeline.

An in-house tool call Sqooper scrapes MySQL backups and pipe them to S3. Job queue and log data is sent to Kafka then persisted to S3 using an open source tool called Secor, which was created by Pinterest.

For compute, Amazon’s Elastic MapReduce (EMR) creates clusters preconfigured for Presto, Hive, and Spark.

Presto is then used for ad-hoc questions, validating data assumptions, exploring smaller datasets, and creating visualizations for some internal tools. Hive is used for larger data sets or longer time series data, and Spark allows teams to write efficient and robust batch and aggregation jobs. Most of the Spark pipeline is written in Scala.

Thrift binds all of these engines together with a typed schema and structured data.

Finally, the Hive Metastore serves as the ground truth for all data and its schema.

Data Wrangling at Slack – Several People Are Coding (
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