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To better serve Wise business and customer needs, the PipelineWise codebase needs to shrink. We have made the difficult decision that, going forward many components of PipelineWise will be removed or incorporated in the main repo. The last version before this decision is v0.64.1

We thank all in the open-source community, that over the past 6 years, have helped to make PipelineWise a robust product for heterogeneous replication of many many Terabytes, daily

PipelineWise

PyPI - Python Version License: Apache2

PipelineWise is a Data Pipeline Framework using the Singer.io specification to ingest and replicate data from various sources to various destinations. Documentation is available at https://transferwise.github.io/pipelinewise/

Logo

Table of Contents

Features

  • Built with ELT in mind: PipelineWise fits into the ELT landscape and is not a traditional ETL tool. PipelineWise aims to reproduce the data from the source to an Analytics-Data-Store in as close to the original format as possible. Some minor load time transformations are supported but complex mapping and joins have to be done in the Analytics-Data-Store to extract meaning.

  • Managed Schema Changes: When source data changes, PipelineWise detects the change and alters the schema in your Analytics-Data-Store automatically

  • Load time transformations: Ideal place to obfuscate, mask or filter sensitive data that should never be replicated in the Data Warehouse

  • YAML based configuration: Data pipelines are defined as YAML files, ensuring that the entire configuration is kept under version control

  • Lightweight: No daemons or database setup are required

  • Extensible: PipelineWise is using Singer.io compatible taps and target connectors. New connectors can be added to PipelineWise with relatively small effort

Official docker images

Pipelinewise images are published to: dockerhub

Pull image with:

docker pull transferwiseworkspace/pipelinewise:{tag}

Connectors

Tap extracts data from any source and write it to a standard stream in a JSON-based format, and target consumes data from taps and do something with it, like load it into a file, API or database

Type Name Extra Latest Version Description
Tap Postgres PyPI version Extracts data from PostgreSQL databases. Supporting Log-Based, Key-Based Incremental and Full Table replications
Tap MySQL PyPI version Extracts data from MySQL databases. Supporting Log-Based, Key-Based Incremental and Full Table replications
Tap Kafka PyPI version Extracts data from Kafka topics
Tap S3 CSV PyPI version Extracts data from S3 csv files (currently a fork of tap-s3-csv because we wanted to use our own auth method)
Tap Zendesk PyPI version Extracts data from Zendesk using OAuth and Key-Based incremental replications
Tap Snowflake PyPI version Extracts data from Snowflake databases. Supporting Key-Based Incremental and Full Table replications
Tap Salesforce PyPI version Extracts data from Salesforce database using BULK and REST extraction API with Key-Based incremental replications
Tap Jira PyPI version Extracts data from Atlassian Jira using Base auth or OAuth credentials
Tap MongoDB PyPI version Extracts data from MongoDB databases. Supporting Log-Based and Full Table replications
Tap Google Analytics Extra PyPI version Extracts data from Google Analytics
Tap Oracle Extra PyPI version Extracts data from Oracle databases. Supporting Log-Based, Key-Based Incremental and Full Table replications
Tap Zuora Extra PyPI version Extracts data from Zuora database using AQAA and REST extraction API with Key-Based incremental replications
Tap GitHub PyPI version Extracts data from GitHub API using Personal Access Token and Key-Based incremental replications
Tap Shopify Extra PyPI version Extracts data from Shopify API using Personal App API Password and date based incremental replications
Tap Slack PyPI version Extracts data from a Slack API using Bot User Token and Key-Based incremental replications
Tap Mixpanel PyPI version Extracts data from the Mixpanel API.
Tap Twilio PyPI version Extracts data from the Twilio API using OAuth and Key-Based incremental replications.
Target Postgres PyPI version Loads data from any tap into PostgreSQL database
Target Redshift PyPI version Loads data from any tap into Amazon Redshift Data Warehouse
Target Snowflake PyPI version Loads data from any tap into Snowflake Data Warehouse
Target S3 CSV PyPI version Uploads data from any tap to S3 in CSV format
Transform Field PyPI version Transforms fields from any tap and sends the results to any target. Recommended for data masking/ obfuscation

Note: Extra connectors are experimental connectors and written by community contributors. These connectors are not maintained regularly and not installed by default. To install the extra packages use the --connectors=all option when installing PipelineWise.

Running from docker

If you have Docker installed then using docker is the recommended and easiest method to start using PipelineWise.

Use official image

PipelineWise images are built on each release and available on Dockerhub

```sh
$ docker pull transferwiseworkspace/pipelinewise
```

Build your own docker image

  1. Build an executable docker image that has every required dependency and is isolated from your host system.

By default, the image will build with all connectors. In order to keep image size small, we strongly recommend you change it to just the connectors you need by supplying the --build-arg command:

```sh
$ docker build --build-arg connectors=tap-mysql,target-snowflake -t pipelinewise:latest .
```

  1. Once the image is ready, create an alias to the docker wrapper script:

    $ alias pipelinewise="$(PWD)/bin/pipelinewise-docker"

  2. Check if the installation was successful by running the pipelinewise status command:

    $ pipelinewise status

Tap ID    Tap Type      Target ID     Target Type      Enabled    Status    Last Sync    Last Sync Result
--------  ------------  ------------  ---------------  ---------  --------  -----------  ------------------
0 pipeline(s)

You can run any pipelinewise command at this point. Tutorials to create and run pipelines is at creating pipelines.

Running tests:

Building from source

  1. Make sure that all dependencies are installed on your system:

    • Python 3.x
    • python3-dev
    • python3-venv
    • mongo-tools
    • mbuffer

  2. Run the Makefile that installs the PipelineWise CLI and all supported singer connectors into separate virtual environments:

    $ make pipelinewise  all_connectors

    Press Y to accept the license agreement of the required singer components. To automate the installation and accept every license agreement run:

    $ make pipelinewise all_connectors -e pw_acceptlicenses=y

    And to install only a specific list of singer connectors:

    $ make connectors -e pw_connector=<connector_1>,<connector_2>

    Run make or make -h to see the help for Makefile and all options.

  3. To start the CLI you need to activate the CLI virtual environment and set PIPELINEWISE_HOME environment variable:

    $ source {ACTUAL_ABSOLUTE_PATH}/.virtualenvs/pipelinewise/bin/activate
$ export PIPELINEWISE_HOME={ACTUAL_ABSOLUTE_PATH}

    (The ACTUAL_ABSOLUTE_PATH differs on every system, running make -h prints the correct commands for CLI)

  4. Check if the installation was successful by running the pipelinewise status command:

    $ pipelinewise status

Tap ID    Tap Type      Target ID     Target Type      Enabled    Status    Last Sync    Last Sync Result
--------  ------------  ------------  ---------------  ---------  --------  -----------  ------------------
0 pipeline(s)

You can run any pipelinewise command at this point. Tutorials to create and run pipelines can be found here: creating pipelines.

To run unit tests:

$ pytest --ignore tests/end_to_end

To run unit tests and generate code coverage:

$ coverage run -m pytest --ignore tests/end_to_end && coverage report

To generate code coverage HTML report.

$ coverage run -m pytest --ignore tests/end_to_end && coverage html -d coverage_html

Note: The HTML report will be generated in coverage_html/index.html

To run integration and end-to-end tests:

To run integration and end-to-end tests you need to use the Docker Development Environment. This will spin up a pre-configured PipelineWise project with pre-configured source and target databases in several docker containers which is required for the end-to-end test cases.

Developing with Docker

If you have Docker and Docker Compose installed, you can create a local development environment that includes not only the PipelineWise executables but also a pre-configured development project with some databases as source and targets for a more convenient development experience and to run integration and end-to-end tests.

For further instructions about setting up local development environment go to Test Project for Docker Development Environment.

Contribution

To add new taps and targets follow the instructions on

Links

License

Apache License Version 2.0

See LICENSE to see the full text.

Important Note:

PipelineWise as a standalone software is licensed under Apache License Version 2.0 but bundled components can use different licenses and may overwrite the terms and conditions detailed in Apache License Version 2.0. You can customise which connectors you want to include into the final PipelineWise build and the final license of your build depends on the included connectors. For further details please check the Licenses section in the documentation.