The quickest way to understand data orchestration and how you can best implement it is to dissect how data workflows are interconnected. A data workflow consists of steps like data extraction, loading, and transformation (ELT), which generate useful insights that can be directly used by cross-functional teams.
Data orchestration is what powers all of these processes. It incorporates data cleansing, organizing, and storing; computing business metrics; maintaining data infrastructure; and more.
Think of data orchestration as the conductor of a symphony orchestra. The conductor uses hand gestures to cue the musicians so they can play in sync, with good timing and perfect harmony.
In the same way, data orchestration helps streamline data workflows to execute actions in a particular order. It automates, manages, and coordinates workflows to ensure all your tasks are completed successfully. Without data orchestration, workflows are prone to errors since they work individually, often in data silos. As your data scales, this can be alarming.
But data orchestration is often misunderstood as configuration management or data infrastructure itself—more on that later. For now, we’ll deep dive into the specifics of how data teams can leverage data orchestration, exactly what steps are involved, why data orchestration is critical to your workflows, how it can eliminate bottlenecks, and some use cases.
What is data orchestration?
Data orchestration is a well-defined execution order that consolidates siloed data from different data storage locations (such as data lakes and warehouses) to combine and organize that data, making it accessible for data analysis.
Beyond just requiring data engineers and other data people to manually write custom scripts, data orchestration relies on software to connect all the platforms and scripts together. The data is processed efficiently into a format that can be used by various teams in an organization.
At the core of it, data orchestration authors data pipelines and workflows to move data from a source to a different destination. But data orchestration could range from executing simple tasks at a specific time and day to a more sophisticated approach of automating and monitoring multiple data workflows over longer periods of time, all with the ability to handle failures and potential errors.
The larger your organization, the greater your data management needs and the more complex your workflows. And as the number of data points, tools, and users increases, it can lead to unidentified errors—for instance, by making data incompatible when there are changes in the data model downstream.
Data orchestration helps you quickly identify errors and their root causes so you can fix them. Your data workflows can continue to function as intended without restarting.
So what does data orchestration look like in action? Let’s say you want to do the following:
- Run a data pipeline every day at midnight that will transform your financial data collected from various tools
- Validate that the data exists in cloud storage (e.g. APIs like PayPal, Mastercard, Stripe, etc) before running the pipeline
- Execute a BigQuery job to create a view of the newly processed data
- Delete raw data from a cloud storage bucket once the pipeline is complete
- Be notified via Slack if any of the above fails
Previously, data engineers and developers would write individual scripts to run pipelines (such as ETL) using a tool called cron—a command-line utility for UNIX-based operating systems like Linux. But handling big data using cron jobs has become an increasingly complex task. If you don’t clean up after running cron jobs, they could severely deplete your resources and cause system lag issues. Relying on a scheduling system to individually execute scripts at the right time can come with its own set of headaches. Plus, if you have a large dataset, cron jobs can sometimes take hours to run or use lots of memory.
Data orchestration helps overcome the challenges presented by cron jobs and makes the process easier and faster—while baking in high-quality tests and security throughout the data processing.
Steps of data orchestration
The most important steps of data orchestration are data collection, transformation, and activation. Your data orchestration should be built around these stages. To understand what each of these stages involve, let’s take a closer look:
Collection and preparation
You might have data in legacy systems, cloud-based tools, APIs, third-party services, data lakes, or data warehouses. Typically data stored in these sources is raw, unstructured, and siloed.
Data orchestration tools like Shipyard help you grab data from relevant sources to understand how data is collected straight from the source. This process may involve methods to validate the authenticity, integrity, and accuracy of the data being collected.
Transformation
No matter what type of data you’re working with, it needs to be in a singular format to accelerate the process of data analysis.
For example, if a product management tool formats dates as DD/MM/YY and a marketing tool formats them as MM/DD/YY, the format needs to be standardized so workflows can integrate seamlessly without your needing to sort different types of data manually.
Data orchestration tools process all different kinds of input data to remove disparities and present data in a unified, usable format.
Activation
The most important part of data orchestration is activation. Data orchestration solutions aren’t limited to facilitating workflows—they can also take relevant and contextual-based decisions (powered by machine learning and artificial intelligence) in other systems.
For example, data orchestration tools can identify duplicate leads in Salesforce and automatically merge them using logic-based rules to ensure all the campaigns related to that lead run smoothly. This can also help your marketing team measure attribution more accurately and avoid duplicate results from the same lead.
What’s more, data orchestration platforms can automatically trigger real-time notifications to dedicated teams. For example, when a prospective customer performs an activity that indicates strong purchasing intent (such as signing up for a free trial from your website), a data orchestration tool can notify sales or marketing teams. These teams would then know when to send promotional content to the prospect so they become a paying customer.
Why is data orchestration essential?
Even knowing what data orchestration can do for you and how crucial data processing is, you may still be wondering, “Why should I bother with data orchestration at all when my data workflows are running just fine?”
Data orchestration sits at the intersection of all your data, input sources, and users. Imagine a Venn diagram.
Different kinds of data workflows need to be managed, and data orchestration tools offer exactly that—which makes them incredibly valuable for organizations. Data orchestration tools help by doing the following:
- Removing data bottlenecks by automatically handling the heavy lifting of data processing
- Connecting all your data systems and enforcing a data governance strategy
- Handling multiple users with ease and scaling with your data needs
- Simplifying data analytics by removing lengthy evaluations of audit logs built manually from hand-coded utilities
- Reducing dependencies between different jobs that teams handle manually, thereby improving speed and agility
- Employing intelligent, rule-based logic regarding how and where users can access data, which improves user control access
- Removing data redundancy across all systems and saving teams from unnecessary busywork
Data orchestration tools also have built-in mechanisms to safeguard against data loss and restore data if you need to roll back changes.
Data orchestration use cases
Let’s explore some different use cases of data orchestration:
Marketing data integration
Marketing teams use multiple tools and platforms for web analytics, social media information, customer feedback, and email marketing. With data orchestration, you can pull customer data from all your marketing systems into a unified cloud data warehouse and directly analyze the performance of each campaign. Once the performance is analyzed, you can calculate the changes you want to make to your campaigns (updating bids, budgets, ads, etc.) and directly apply these changes via an API. Plus, you can always augment the data with location coordinates, device IDs, customer names, currencies, pricing plans, and seller URLs so that performance is continuously being optimized with the most relevant data.
Business intelligence
Nothing is worse than when someone views a dashboard and finds that the data is either incorrect or missing. Data orchestration allows you to connect your BI tool (like Tableau, Looker, or PowerBI) directly to your data processing workflows so that the contents of the dashboard update as soon as processing is successfully completed.
Plus, you can use the data orchestration to download the latest visuals from your BI tool and send fancy, accurate reports directly to your customers or teammates.
Batch data processing
If you’re a growing company with fast-moving customer data, you’re better off with a data orchestration platform that can execute tasks at a lightning speed. Instead of relying on automated scripts living on a single machine, orchestration tools leverage scalability on the cloud to move huge volumes of data between two or more systems in a scheduled manner. You can always transform this raw data into a structured and standardized format so your teams can interpret it faster to gain useful insights. Batch data processing is especially helpful in making timely decisions, ensuring your team always has access to accurate data.
Human resources automation
Talent acquisition teams are increasingly using automated platforms to reduce paperwork and sign-offs. Data orchestration platforms can automate the onboarding process and save TA teams a significant amount of time and effort while streamlining employee training programs.
Why is data orchestration often misunderstood?
Data orchestration is often associated with ETL pipelines and nothing more. From the use cases we’ve helped our customers achieved, we know that data orchestration can be so much more. Orchestration can handle tasks like ML model deployment, data alerts, dashboard refreshes, reverse ETL, website updates, metadata management, and more. At its core, data orchestration is just a way to use your data to accomplish any task.
Orchestration helps teams connect their data touchpoints end-to-end so they can know when breakages occur immediately and prevent bad data from ever being caught by business users downstream. That means that an effective orchestration strategy involves mapping out the flow of data from beginning to end and looping every team that interacts with the data into the picture. No data should ever be touched without knowing how it connects to the larger picture.
Interested in exploring how you can implement data orchestration?
Adopting a data orchestration tool shouldn’t be a challenge. You can launch, monitor, and share resilient data workflows with your team at record speeds using a powerful platform like Shipyard. With features like pre-designed open source low-code templates, visual workflow builder, GitHub integration, accurate reporting, and real-time diagnosing, Shipyard truly empowers teams to build workflows faster.
We designed Shipyard so that every team—regardless of their experience with coding—could build data workflows and orchestrate them the right way. Whether you want to run dbt Cloud after verifying data loads in Amazon Redshift or send a Slack message for data quality issues in Snowflake, Shipyard lets you do it all.
You can sign up for a free plan of the Shipyard app (no credit card required) and start orchestrating your data workflows right away.
