Depending on the degree of complexity of the business case, data orchestration could be either a small task scheduler or a complex pipeline for a major retailer. And even then, there are two ways to define it:
- Data orchestration as an automation process
- Data orchestration as a data management process
Data orchestration as an automation process
Data orchestration is a process of organizing and automating the process of collecting data from various sources and preparing it for data consumers. It serves as one of the aspects of an end-to-end data process.
Some sources even frame data orchestration as one of the most important building blocks of the data systems infrastructure. It keeps track of how different tasks rely on each other, even when there are huge amounts of data or lots of moving parts, across many systems and teams. This view is backed by Nick Schrock, founder of Elementl:
“We think that orchestration matters because we view it as really the
center of gravity of both the data platform as well as the data lifecycle, the software development lifecycle, as it comes to data.”
Data orchestration is a lot like a conductor of a large orchestra. There are different instruments: cello, violin, trumpet, etc., and each one is played by a skilled musician, who has a detailed music sheet in front of them. They know their parts and how to play them. But the “when” to play is dependent on the other orchestra members. The orchestra conductor is the one who coordinates the musicians. For example, the conductor tells the cello player when to stop playing its part and immediately signals to the violins that this is the moment to start playing theirs.
Similarly, data orchestration coordinates different tasks and aspects of the data pipeline, such as:
- Data cleaning
- Quality checks
- Workflow automation
- Job scheduling
- Gathering data, but only in some cases
Each task could run on its own successfully and in the right order, but if the process requires one task to complete before another can commence, data orchestration ensures the proper coordination, just like an orchestra conductor.
One could argue that orchestration is necessary only for large datasets. One specialist is able to manually handle a small dataset, say one with only a few spreadsheets. However, if, for example, that number grows to one hundred, it becomes impossible for a single specialist to coordinate. Automation, or in this case, orchestration, becomes necessary.
Also, the tools to perform these tasks are the tools used for orchestrations; however, in many cases, orchestration is just one of the uses of these tools.
Data orchestration as a data management process
Another way to understand data orchestration is to see it as a set of activities that are about automating, coordinating, and streamlining the entire flow of data across systems.
Data orchestration ensures that data moves seamlessly from various sources through pipelines to the end users. On the way there, the data is cleaned, transformed, and validated, which are orchestrations and automation tasks.
Some definitions of data orchestration even go as far as to describe it in a way that resembles a definition of the data management process. And there is some truth to this.
Data orchestration involves managing pipelines at a large scale across multiple systems and layers, including data cleaning, quality checks, workflow automation, and job scheduling. The ultimate goal of data orchestration is to simplify routine management by organizing processes.
From this point of view, it’s not a separate process to data management, but something integral to modern pipelines that are designed to generate usable business insights, without unnecessary waste in human labor.
For example, imagine a retail company where stores close at 10 PM. At 11 PM, a data orchestration system automatically pulls together the day’s sales, cleans and verifies the data, and sends performance dashboards to managers before morning. This allows the retail company to operate even when their employees are off work.
Data orchestration can be broken down into three stages:
- Organization
- Transformation
- Activation
It is worth noting that data orchestration only triggers these stages; it is not directly responsible for any of them.
Organization
In the organization stage, raw data is collected from various sources. However, this is not just about data ingestion, but about organizing raw data into usable datasets. For example, in a factory setting, sensor readings from multiple production lines would be stored in separate datasets, each one corresponding to its dedicated production line.
Transformation
Transformation is a stage where organized data is either cleaned, analyzed, refined, etc. There’s no set rule for what kind of tasks will be performed at this stage; however, it can be a combination of standard ones, such as cleaning, aggregation, formatting, normalization, and enrichment.
The purpose of transformation is to further prepare the data for its intended business case.
Activation
In the data activation stage, the prepared data is delivered to tools and business users for consumption. This can take different shapes or forms, for example:
- Feeding it into business intelligence tools or dashboards
- Integrating it into operational workflows
- Sending data to analytics platforms
- Supplying data for APIs, data marts, and reporting interfaces
The goal of the activation stage is to drive business outcomes or enable data-driven decisions.
In the general IT context, orchestration involves setting up and managing various software tools or systems to ensure they work together to complete a task or a series of tasks. These tasks are often automated, meaning they run on their own without someone needing to manually start or manage them.
The main goal of such orchestration is to make routine, repetitive processes easier to manage. By organizing everything to run in the right order and at the right time, teams can handle more complex work without wasting time or making mistakes. The quality of the data they work on is also more consistent.
In big data workflows, data transformation tasks are organized into jobs, which data engineers run and monitor manually, which becomes more difficult as the number of jobs grows to hundreds, or even thousands. This is where automation, or more specifically, job scheduling, comes into play.
Data scheduler in practice
Apache Oozie was created back in 2008 and released as an open-source project in 2010. Currently, it is considered a legacy technology that has been replaced with Apache Airflow. Both Oozie and scheduler which are built into the Apache Airflow, work on a similar principle, which is to plan when jobs should run, based on time or a specific trigger.
For example, Oozie keeps an eye on a specific location in the HDFS and waits for a file with a particular name to show up in it. Once it does, Oozie automatically runs the dedicated job. In this scenario, the appearance of a specific file in a folder becomes a trigger.
Oozie is just one example of a tool used in data orchestration. Some of these tools are not dedicated specifically to orchestration but are instead all-around data management platforms that include features related to data orchestration.
Apache Airflow
Apache Airflow is a platform for creating, scheduling, and monitoring workflows. The Airflow architecture supports scaling and parallel execution, making it suitable for managing complex, data-intensive pipelines. It’s a modern standard and a replacement for Oozie.
Azure Data Factory
It's a cloud-native, fully managed, serverless data integration solution for ingesting, preparing, and transforming all your data at scale.
GCP Composer
GCP Composer is a cloud-native, fully managed workflow orchestration service built on Apache Airflow. It enables users to create, schedule, monitor, and manage workflows across cloud and on-premises environments.
Microsoft Fabric
Microsoft Fabric, which is typically a SaaS tool, can build pipelines from start to finish and involves less coding than traditional ETL tools. It comes with a lineage view: data in reports is taken from the semantic model, from the lakehouse, etc. This visualizes orchestration and how the data flows in it.
Understanding this concept is important for an efficient data pipeline. With a variety of tools in synchronization, it can be set up for an efficient and scalable data pipeline, the backbone of every data-driven business.
Data orchestration goes beyond pipeline efficiency. By automating workflows and enforcing documented standards, it helps in unifying the quality of data across the board. This way, it ensures the trust in your data across your company.
When paired with proper validation, data orchestration allows teams to detect problems early and pinpoint where things go wrong in the pipeline. This becomes especially important in AI-driven organizations, where it's better to refrain from using an insight if it was based on inaccurate data than to make a business decision based on a flawed prediction.
Ultimately, data orchestration helps you make faster, smarter decisions, but remember this rule of thumb: every pipeline is only as strong as its weakest link.
