What Is Data Orchestration? A 2025 Guide to Its Benefits & Key Parts

Updated December 09th, 2024
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Data orchestration is the process of automating the collection, transformation, and synchronization of data from multiple sources.
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It eliminates the need for manual scripts by using software to connect and integrate data storage systems. This ensures that data is accessible, accurate, and analysis-ready in real time.

Data orchestration helps organizations overcome challenges such as data silos, bottlenecks, and inefficient workflows.

By automating data integration, businesses can achieve faster insights, enhance data governance, and improve scalability, making it an essential component of modern data management.

Table of contents #

  1. What is data orchestration?
  2. When do businesses need data orchestration?
  3. The 4 parts of data orchestration
  4. Data orchestration example
  5. Why is data orchestration necessary?
  6. Big data challenge being overcome by data orchestration
  7. Data orchestration benefits
  8. Data orchestration tools
  9. Data orchestration in the modern data stack
  10. How organizations making the most out of their data using Atlan
  11. FAQs about Data Orchestration
  12. Data orchestration: Related reads

What is data orchestration? #

Data orchestration is an automated process for bringing data together from multiple sources, standardizing it, and preparing it for data analysis.

Data orchestration doesn’t require data engineers to write custom scripts but relies on software that connects storage systems together so data analysis tools can easily access them.

The challenge of Big Data is that it is, well, BIG. It’s so big that it’s impossible to effectively use manual processes to work with it all. That’s where automated data orchestration comes in.

In this blog, you’ll learn more about data orchestration and how it provides a path to faster business insights.

Previously, when users wanted to work with data, they’d rely on custom-written scripts to extract it from sources such as CSV files, Excel spreadsheets, or databases.

After validating the data, they’d transform it via data cleansing to convert it into an acceptable format.

And finally, the data would be loaded into the target destination. Data orchestration provides freedom from many of the time-intensive and error-prone data handling processes that were once de rigueur.

When do businesses need data orchestration? #

Data orchestration is ideal for organizations with multiple data systems because it doesn’t entail a large migration of data into yet another data store. Rather, it provides access to the data you need, in the format you want, and at the moment you need it. Information that exists across multiple silos can easily be accessed and handled all in perfect synchronization as if the data existed in a centralized repository.

According to a 2024 Gartner report titled “Over 100 Data, Analytics and AI Predictions Through 2030”, 75% of large enterprises have implemented data orchestration solutions to streamline data workflows and enhance decision-making processes.

The 4 parts of data orchestration #

The data orchestration process consists of four parts: 1. preparation, 2. transformation, 3. cleansing, and 4. syncing.

  1. Preparation includes performing checks for integrity and correctness, applying labels and designations, or enriching new third-party data with existing data sets.
  2. Transformation refers to converting data into a standard format. For example, the same date can be written in a variety of ways: March 15, 1990; 3/15/90; 15/3/90; etc. During the transformation process, these dates are converted to the same format.
  3. Cleansing involves locating and correcting (or eliminating) corrupt, inaccurate, duplicated, or outlier data.
  4. Syncing refers to the continuous process of updating data between data sources and destinations for consistency. Think of how your phone and computer might sync so contacts, text messages, and photos are on both devices. It’s the same idea as data synchronization within the data orchestration process.

Data orchestration & ETL #


Each step within the extract, transform, load process (ETL), and the increasingly common ELT process, has a specific script, process, or workflow. Data orchestration automates each step, allowing it to be completed with minimal human intervention.

Data orchestration example #

At 11:59 p.m. each day, automated data orchestration could trigger the entire financial ETL of a business. First, data is extracted from payment processor APIs (Visa, Mastercard, PayPal, Square, etc.). The data is then transformed and cleansed of duplicate charges or charges made in error. Finally, it’s delivered to the data analytics tools or stored in a data warehouse with historical data.

Why is data orchestration necessary? #

Previously, data engineers and developers would schedule jobs, such as ETL, using a tool called “cron” – a Linux-based command-line utility. Building cron jobs to handle Big Data became increasingly complex. To overcome this challenge, data orchestration was popularized in the mid-2010s as a way of streamlining the complexities.

Worth noting is Airbnb became a trailblazer in data orchestration when it developed the popular tool Airflow in 2014. The software was later open-sourced, joining the Apache Software Foundation’s incubation program in 2016.

Big data challenge being overcome by data orchestration #

Data orchestration is useful in overcoming some of the biggest challenges related to Big Data, including:

  • Disparate data sources. An organization might have data coming from a multitude of sources, and much of the data won’t be analysis-ready. Data orchestration automates the process of quickly gathering and preparing the data without introducing human error.
  • Silos. Data a user needs might often be trapped, or siloed within a location, organization, or application making it hard to access and leverage. Orchestration breaks down silos to make that data more accessible. This is done by running a direct acyclic graph (DAG) that illustrates the relationships between tasks within a data system.
  • Bottlenecks. It’s estimated that data practitioners spend 80% of their time cleaning and organizing data. Waiting for analysis-ready data causes bottlenecks that delay the time to insights.
  • Cloud migration. Organizations are increasingly moving data offsite to hybrid and multi-cloud systems. This makes handling data management tricky, but applying data orchestration across frameworks, clouds, and storage systems can provide much-needed assistance.

Data orchestration benefits #

Leveraging data orchestration provides a host of benefits including:

  1. Scalability
  2. Monitoring
  3. Data governance
  4. Real-time information
  5. Faster insights

1. Scalability #


Data orchestration is a cost-effective way of automating synchronization across data silos, enabling organizations to scale data use.

2. Monitoring #


Automating data pipelines and equipping them with alerts and monitoring is a way to quickly identify and remediate issues compared to using scripts and disparate monitoring standards.

3. Data Governance #


Orchestration allows users to track customer data as it’s collected throughout a system. This is especially important when handling data across a variety of geographical regions that have their own rules and regulations regarding privacy and security (i.e, GDPR, FedRAMP, HIPAA).

4. Real-time information #


Automatic data orchestration allows for real-time data analysis or storage since data can be extracted and processed at the moment it’s created.

5. Faster insights #


Automated data orchestration streamlines data workflows so you can get business intelligence and actionable insights fast.

Data orchestration tools #

There are a plethora of data orchestration tools available today that can be used to optimize a data pipeline.

The following are some attributes of modern data orchestration tools:

  • They all manage data and enhance productivity, largely by automating, scheduling, and monitoring workflows.
  • The tools are often scalable, dynamic, and extensible, helping to create a streamlined data migration process.
  • They simplify data for multi-cloud storage and assist in data governance.
  • An added bonus: some tools are free, open-source software created by developers specifically for data orchestration.

When evaluating data orchestration software to integrate into your data stack, look for easy-to-use, intuitive tools that are cloud-based, allowing for remote operation by practically any authorized user on your team (not just the tech wizards!).

You can find excellent tools that seamlessly integrate with your current data systems and come with templates so you can run operations straight out of the box, rather than spend a lot of time on setup.

And because security is a priority, look for tools that provide excellent user management, audit logs, and encryption so that your sensitive data remains safe.

As per an article on global data orchestration tool market by AccessWire in 2024, the global data orchestration tool market is projected to reach USD 1.3 billion in 2024, with expectations to surge to USD 4.3 billion by 2034. This growth is driven by the adoption of DataOps practices and the need for efficient data management solutions.

Learn more: 5 popular open-source data orchestration tools in 2025

Data orchestration in the modern data stack #

Automation is leveraged across industries around the world and relied on for the speed it brings to operations. The same is true with today’s complex, modern data stack. Automated data orchestration helps data practitioners quickly gather and make use of data to derive faster insights and add value to an organization.

Quoting Gartner:

The increased demand for orchestrating existing and new systems has rendered traditional metadata practices insufficient. Organizations are demanding “active metadata” to assure augmented data management capabilities.

This is where Atlan can help. Atlan is a metadata management and data catalog solution thoughtfully built to meet the ever-changing demands of modern data teams.

Also, read → Big Data Predictions: Centralized data orchestration takes center stage | What’s next for Orchestration and Observability | McKinsey Technology Trends Outlook 2024

How organizations making the most out of their data using Atlan #

The recently published Forrester Wave report compared all the major enterprise data catalogs and positioned Atlan as the market leader ahead of all others. The comparison was based on 24 different aspects of cataloging, broadly across the following three criteria:

  1. Automatic cataloging of the entire technology, data, and AI ecosystem
  2. Enabling the data ecosystem AI and automation first
  3. Prioritizing data democratization and self-service

These criteria made Atlan the ideal choice for a major audio content platform, where the data ecosystem was centered around Snowflake. The platform sought a “one-stop shop for governance and discovery,” and Atlan played a crucial role in ensuring their data was “understandable, reliable, high-quality, and discoverable.”

For another organization, Aliaxis, which also uses Snowflake as their core data platform, Atlan served as “a bridge” between various tools and technologies across the data ecosystem. With its organization-wide business glossary, Atlan became the go-to platform for finding, accessing, and using data. It also significantly reduced the time spent by data engineers and analysts on pipeline debugging and troubleshooting.

A key goal of Atlan is to help organizations maximize the use of their data for AI use cases. As generative AI capabilities have advanced in recent years, organizations can now do more with both structured and unstructured data—provided it is discoverable and trustworthy, or in other words, AI-ready.

Tide’s Story of GDPR Compliance: Embedding Privacy into Automated Processes #


  • Tide, a UK-based digital bank with nearly 500,000 small business customers, sought to improve their compliance with GDPR’s Right to Erasure, commonly known as the “Right to be forgotten”.
  • After adopting Atlan as their metadata platform, Tide’s data and legal teams collaborated to define personally identifiable information in order to propagate those definitions and tags across their data estate.
  • Tide used Atlan Playbooks (rule-based bulk automations) to automatically identify, tag, and secure personal data, turning a 50-day manual process into mere hours of work.

Book your personalized demo today to find out how Atlan can help your organization in establishing and scaling data governance programs.

FAQs about Data Orchestration #

1. What is data orchestration, and why is it important? #


Data orchestration is the automated process of collecting, standardizing, and preparing data from multiple sources for analysis. It eliminates the need for custom scripts, streamlining data workflows and enabling faster business insights.

2. How does data orchestration differ from data integration? #


While data integration focuses on merging data from different sources into a unified view, data orchestration automates the process of managing data workflows, ensuring that data is ready for analysis efficiently and consistently.

3. What are the benefits of using data orchestration tools? #


Data orchestration tools reduce manual effort, improve data accuracy, and accelerate data preparation. They enhance pipeline efficiency and provide scalability to handle large volumes of data from diverse sources.

4. How do I set up a data orchestration framework? #


Setting up a data orchestration framework involves identifying data sources, selecting orchestration tools, defining workflows, and configuring automation for data transformation and preparation. Popular tools include Apache Airflow and Prefect.

5. What are the top platforms for data orchestration in 2024? #


Leading platforms for data orchestration in 2024 include Apache Airflow, Dagster, Prefect, and Atlan. These tools offer robust features for managing complex data workflows in both on-premises and cloud environments.

6. How does data orchestration work in cloud environments? #


In cloud environments, data orchestration leverages cloud-native tools to automate data movement, transformation, and storage. This approach ensures scalability, reduces infrastructure management, and supports real-time data processing.

Photo by Isis França from Unsplash

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