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dbt-spark

README.md

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dbt

dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

dbt-spark

dbt-spark enables dbt to work with Apache Spark. For more information on using dbt with Spark, consult the docs.

Getting started

Review the repository README.md as most of that information pertains to dbt-spark.

Running locally

A docker-compose environment starts a Spark Thrift server and a Postgres database as a Hive Metastore backend. Note: dbt-spark now supports Spark 3.3.2.

The following command starts two docker containers:

docker-compose up -d

It will take a bit of time for the instance to start, you can check the logs of the two containers. If the instance doesn't start correctly, try the complete reset command listed below and then try start again.

Create a profile like this one:

spark_testing:
  target: local
  outputs:
    local:
      type: spark
      method: thrift
      host: 127.0.0.1
      port: 10000
      user: dbt
      schema: analytics
      connect_retries: 5
      connect_timeout: 60
      retry_all: true

Connecting to the local spark instance:

  • The Spark UI should be available at http://localhost:4040/sqlserver/
  • The endpoint for SQL-based testing is at http://localhost:10000 and can be referenced with the Hive or Spark JDBC drivers using connection string jdbc:hive2://localhost:10000 and default credentials dbt:dbt

Note that the Hive metastore data is persisted under ./.hive-metastore/, and the Spark-produced data under ./.spark-warehouse/. To completely reset you environment run the following:

docker-compose down
rm -rf ./.hive-metastore/
rm -rf ./.spark-warehouse/

Additional Configuration for MacOS

If installing on MacOS, use homebrew to install required dependencies.

brew install unixodbc

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