What Is Data Orchestration: Definition, Uses, Examples, and Tools

February 22, 2022

header image for What Is Data Orchestration: Definition, Uses, Examples, and Tools

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.

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. It 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.

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.

The 4 parts of data orchestration

The data orchestration process consists of four parts: preparation, transformation, cleansing, 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 with 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, 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:

  • Scalability
  • Monitoring
  • Data governance
  • Real-time information
  • Faster insights

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

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.

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).

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.

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.

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.

Learn more: 5 popular open-source data orchestration tools in 2022

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.

Photo by Isis França from Unsplash

Free Guide: Find the Right Data Catalog in 5 Simple Steps.

This step-by-step guide shows how to navigate existing data cataloging solutions in the market. Compare features and capabilities, create customized evaluation criteria, and execute hands-on Proof of Concepts (POCs) that help your business see value. Download now!