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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

Contribute

Want to help us build dbt-spark? Check out the Contributing Guide.

Release files for in-dbt-spark 1.9.53

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for in-dbt-spark 1.9.53
File	Size	Uploaded
in_dbt_spark-1.9.53.tar.gz	152.2 kB	Sep 9, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for in-dbt-spark 1.9.53
File	Interpreter	ABI	Platform	Reset
in_dbt_spark-1.9.53-py3-none-any.whl 129.2 kB Sep 9, 2026	Python 3	none	any	Details

Total release size: 281.4 kB

Release files / in_dbt_spark-1.9.53.tar.gz

Download URL	in_dbt_spark-1.9.53.tar.gz
Size	152.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`1187af8057d583097d8b973f50becfca97f6fdebefb8e7be185c31d1c0370065`
BLAKE2b-256 checksum How to use checksums	`d02ecc5c97d45b860228c2ff464d4a3f32a37efa53e928a712b925aef21b18be`
Upload date	Sep 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.4`

Release files / in_dbt_spark-1.9.53-py3-none-any.whl

Download URL	in_dbt_spark-1.9.53-py3-none-any.whl
Size	129.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`c60d6ea8f4d52ec578481f6b10cb508746e85b1d4215a0b2416cb08ba6c5c643`
BLAKE2b-256 checksum How to use checksums	`aad15c4d14cfa4a38c094a1b453856a90c9d52fbc2bc3499381b4e3f9258f1e2`
Upload date	Sep 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.4`