coxautomotivedatasolutions / waimak

Waimak is an open-source framework that makes it easier to create complex data flows in Apache Spark.

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What is Waimak?


Waimak aims to abstract the more complex parts of Spark application development (such as orchestration) away from the business logic, allowing users to get their business logic in a production-ready state much faster. By using a framework written by Data Engineers, the teams defining the business logic can write and own their production code.

Our metaphor to describe this framework is the braided river – it splits and rejoins to itself repeatedly on its journey. By describing a Spark application as a sequence of flow transformations, Waimak can execute independent branches of the flow in parallel making more efficient use of compute resources and greatly reducing the execution time of complex flows.


From Waimak version 2.9 onwards we will be focusing our attention on Spark 3+ and Scala 2.12. While some of the jars will still be published for 2.11 we are focusing on having the build be 2.12 first. This is due to an increasing number of the dependencies (databricks config and deequ for example) now being produced for Scala 2.12. If you still require Scala 2.11 support for new versions please open an issue and we can work with you on this.

Why would I use Waimak?

We developed Waimak to:

  • allow teams to own their own business logic without owning an entire production Spark application
  • reduce the time it takes to write production-ready Spark applications
  • provide an intuitive structure to Spark applications by describing them as a sequence of transformations forming a flow
  • increase the performance of Spark data flows by making more efficient use of the Spark executors

Importantly, Waimak is a framework for building Spark applications by describing a sequence of composed Spark transformations. To create those transformations Waimak exposes the complete Spark API, giving you the power of Apache Spark with added structure.

How do I get started?

You can import Waimak into your Maven project using the following dependency details:


Waimak marks the Spark dependency as optional so as not to depend on any specific release of Spark, therefore you must specify the version of Spark you wish to use as a dependency. Waimak should run on any version of Spark 2.2+, however the list of officially tested versions is given below.

The following code snippet demonstrates a basic Waimak example taken from the unit tests:

// Required imports
import com.coxautodata.waimak.dataflow.Waimak

// Initialise basic Waimak objects
val emptyFlow = Waimak.sparkFlow(spark)

// Add actions to the flow
val basicFlow = emptyFlow
    .openCSV(basePath)("csv_1", "csv_2")
    .alias("csv_1", "items")
    .alias("csv_2", "person")
    .writeParquet(baseDest)("items", "person")

// Run the flow

This example is very small, but in practice flow definitions can become very large depending of the number of inputs and outputs in a job.

The project wiki page provides best practices for structuring your project when dealing with large flows.

What Waimak modules are available?

Waimak currently consists of the following modules:

Artifact ID Purpose Maven Release
waimak-core Core Waimak functionality and generic actions Maven Central
waimak-configuration-databricks Databricks-specific configuration provider using secret scopes (Scala 2.11 only) Maven Central
waimak-impala Impala implementation of the HadoopDBConnector used for commiting labels to an Impala DB Maven Central
waimak-hive Hive implementation of the HadoopDBConnector used for commiting labels to a Hive Metastore Maven Central
waimak-rdbm-ingestion Functionality to ingest inputs from a range of RDBM sources Maven Central
waimak-storage Functionality for providing a hot/cold region-based ingestion storage layer Maven Central
waimak-app Functionality providing Waimak application templates and orchestration Maven Central
waimak-experimental Experimental features currently under development Maven Central
waimak-dataquality Functionality for monitoring and alerting on data quality Maven Central
waimak-deequ Amazon Deequ implementation of data quality monitoring (Scala 2.11 only) Maven Central

What versions of Spark are supported?

Waimak is tested against the following versions of Spark:

Package Maintainer Spark Version Scala Version
Apache Spark 2.4.5 2.11
Apache Spark 3.0.1 2.12

Other versions of Spark >= 2.2 are also likely to work and can be added to the list of tested versions if there is sufficient need.

Where can I learn more?

You can find the latest documentation for Waimak on the project wiki page. This README file contains basic setup instructions and general project information.

You can also find details of what's in the latest releases in the changelog.

Finally, you can also talk to the developers and other users directly at our Gitter room.

Can I contribute to Waimak?

We welcome all users to contribute to the development of Waimak by raising pull-requests. We kindly ask that you include suitable unit tests along with proposed changes.

How do I test my contributions?

Waimak is tested against different versions of Spark 2.x to ensure uniform compatibility. The versions of Spark tested by Waimak are given in the <profiles> section of the POM. You can activate a given profile in the POM by using the -P flag: mvn clean package -P apache-2.3.0_2.11

The integration tests of the RDBM ingestion module require Docker therefore you must have the Docker service running and the current user must be able to access the Docker service.

What is Waimak licensed under?



Copyright 2018 Cox Automotive UK Limited