Atum Service

Atum Service is a data completeness and accuracy application meant to be used for data being processed by Apache Spark.

One of the challenges regulated industries face is the requirement to track and prove that their systems preserve the accuracy and completeness of data. In an attempt to solve this data processing problem in Apache Spark applications, we propose the approach implemented in this application.

The purpose of Atum Service is to add the ability to specify "checkpoints" in Spark applications. These checkpoints are used to designate when and what metrics are calculated to ensure that critical input values have not been modified as well as allow for quick and efficient representation of the completeness of a dataset. This application does not implement any checks or validations against these control measures, i.e. it does not act on them - Atum Service is, rather, solely focused on capturing them.

The application provides a concise and dynamic way to track completeness and accuracy of data produced from source through a pipeline of Spark applications. All metrics are calculated at a DataFrame level using various aggregation functions and are stored on a single central place, in a relational database. Comparing control metrics for various checkpoints is not only helpful for complying with strict regulatory frameworks, but also helps during development and debugging of your Spark-based data processing.


Agent agent/

This module is intended to replace the current Atum repository. It provides functionality for computing and pushing control metrics to the API located in server/.

For more information, see the Vocabulary section or agent/ for more technical documentation.

Server server/

An API under construction that communicates with the Agent and with the persistent storage. It also provides measure configuration to the agent.

The server accepts metrics potentially from several agents and saves them into database. In the future, it will be also able to send the metrics definitions back if requested.

Important note: the server never receives any real data - it only works with the metadata and metrics defined by the agent!

See server/ for more technical documentation.

Data Model model/

This module defines a set of Data Transfer Objects. These are Atum-specific objects that carry data that are being passed from agent to server and vice versa.


This section defines a vocabulary of words and phrases used across the codebase or this documentation.

Atum Agent

Basically, the agent is supposed to be embedded into your application and its responsibility is to measure the given metrics and send the results to the server. It acts as an entity responsible for spawning the Atum Context and communicating with the server.

A user of the Atum Agent must provide certain Partitioning with a set of Measures he or she wants to calculate, and execute the Checkpoint operation. A server details are also needed to be configured.


Atum Partitioning uniquely defines a particular dataset (or a subset of a dataset, using Sub-Partitions) that we want to apply particular metrics on. It's similar to data partitioning in HDFS or Data Lake. The order of individual Partitions in a given Partitioning matters. It's a map-like structure in which the order of keys (partition names) matters.

It's possible to define an additional metadata along with Partitioning - as a map-like structure with which you can store various attributes associated with a given Partitioning, that you can potentially use later in your application. Just to give you some ideas for these:

  • a name of your application, ETL Pipeline, or your Spark job
  • a list of owners of your application or your dataset
  • source system of a given dataset
  • and more

Atum Context

This is a main entity responsible for actually performing calculations on a Spark DataFrame. Each Atum Context is related to particular Partitioning - or to put in other words, each Atum Context contains all Measures for a specific data, defined by a given Partitioning, that are supposed to be calculated.


A Measure defines what and how a single metric should be calculated. So it's a type of control metric to compute, such as count, sum, or hash, that also defines a list of columns (if applicable) that should be used when actually executing the calculation against a given Spark DataFrame.

Some Measures define no columns (such as count), some require exactly one column (such as sum of values for particular column), and some require more columns (such as hash function).


Practically speaking, a single Measurement contains a Measure and result associated with it.


Each Checkpoint defines a sequence of Measurements (containing individual Measures and their results) that are associated with certain Partitioning.

A Checkpoint is defined on the agent side, the server only accepts it.

Atum Context stores information about a set of Measures associated with specific Partitioning, but the calculations of individual metrics are performed only after the Checkpoint operation is being called. We can even say, that Checkpoint is a result of particular Measurements (verb).

Data Flow

The journey of a dataset throughout various data transformations and pipelines. It captures the whole journey, even if it involves multiple applications or ETL pipelines.

How to generate Code coverage report

sbt jacoco

Code coverage wil be generated on path:


How to Run in IntelliJ

To make this project runnable via IntelliJ, do the following:

  • Make sure that your Spring related configuration in server/src/main/resources/ is configured according to your needs
  • Create a new Spring Boot configuration (see the screenshot below)

Intellij Spring Boot Run Configuration Intellij Run Configuration for Spring Boot, Server configuration

How to Release

Please see this file for more details.