DataOps orchestration solutions play a critical role in the day and life of the modern data team. This evolving category aids in the central management of complex data pipelines.
Because DataOps is a relatively new discipline, let's explore how orchestration solutions solve some of the most significant issues for data engineers and architects.
But first, let's set the scene
The weekend is just about to begin. The last of your colleagues waves goodbye as your computer starts the shutdown process. It's beautiful outside, and you've allowed your mind to wander toward this evening's dinner plans. That was a mistake.
Shattering the serenity of the moment, your mobile phone screams to life. Your heart skips a beat as you see your boss's name hover on the screen. It's probably nothing, you tell yourself. Then, taking a nervous gulp, you slide an unsteady finger across the screen.
It turns out that one of your execs needs a dashboard that isn't updating. To anybody else, the problem seems innocuous. But as part of the data team, you know it's not. The data pipeline, which flows into the exec's dashboard, is strung together with point integrations and a few custom scripts. Uggh.
Your shoulders drop as your finger taps the phone. It's going to take hours to root-cause the breakdown. You'll have to text your friends. Dinner will have to wait.
Now let's imagine this differently. The whole scene is the same. That is, it's the same right up to the point where you hang up the phone.
In this scenario, you take a deep breath of relief. Turning your computer back on, you quickly pop open your DataOps Orchestration solution. In a few minutes, you'll know where the breakdown is and be able to restart the service.
Dinner plans are on.
So what is a DataOps Orchestration solution?
Well, aside from a time and weekend saver, a DataOps orchestration solution is used as a meta-orchestrator. This evolving category provides a centralized lens to manage all of the automated processes within the tools used along your data pipeline.
Most enterprises use a mix of data tools to create a data pipeline. These tools include source systems, ETL, data storage, machine learning and predictive tools, and analytics or delivery solutions.
A DataOps orchestration solution does not replace your existing data pipeline and analytics tools. Instead, it serves as a platform that integrates with these tools. Once integrated, you may control and automate the actions and processes within each application or platform from the DataOps orchestration solution.
You may be thinking: But I use a scheduler to automate my data pipeline. It is true that data teams commonly use a mix of schedulers to automate their pipelines. Typical scheduling solutions include:
As an orchestration layer that can connect to your schedulers and your data tools, DataOps is designed to either automate your scheduler(s) or replace them altogether. You massively reduce complexity and gain observability by using a single platform to orchestrate all automation.
In addition, DataOps teams are empowered to apply DevOps-like practices to data management. Data architects and engineers gain control over data pipelines with the ability to create visual workflows, automate processes, test data simulations, and promote code between environments.
Ways that data teams leverage a DataOps orchestration solution:
- Visually Design Workflows: Create workflows that include each step of the end-to-end data pipeline. Drag-and-drop capabilities make it simple to create complex workflows that span multiple big data tools in a low-code or even no-code environment.
- DataOps Lifecycle Management: Create, simulate, and promote workflows from dev to test to prod environments using DevOps-like methodologies.
- Integrated Managed File Transfer (MFT): With MFT built into a DataOps orchestration solution, it's easy to incorporate MFT tasks into each workflow. An MFT process is especially helpful at the beginning of a data pipeline, where you're pulling the raw data from its source system.
- Move Data in Real-Time: With event-based triggers, data teams can do away with batch jobs. Data moves in the moment, based on system event triggers. This functionality allows business users to gain real-time insights into the business. For example, when combining system events with MFT, source data can be pushed into an ETL tool the second a new file is added to a monitored file folder.
- Enforce Governance and Compliance with Observability: Auto-created log files track each change along the data pipeline. Easily create reports to understand the five W's of your data (who, what, where, when, and why). Ultimately, gain visibility and observability into the movement of data across your organization.
- Solve Small Problems Before They Become Big Issues: With detailed reports and proactive alerts, you'll know when there's any type of failure before anyone else does. Plus, you can set alerts that trigger service tickets in whatever ITSM system you use. Identify and root-cause issues in minutes instead of hours or days.
What to Expect Once The Data Pipeline is Orchestrated
Thinking back to the scene of your boss calling with a problem, a more likely scenario is that your boss would never have called in the first place. And, he would never have received a call from the exec. With a DataOps orchestration solution, you would have been proactively alerted that something broke. As such, you would then have entered the orchestration dashboard and been shown exactly what failed. Then you would have fixed it before anybody, including your boss and the exec, ever knew something went down in the first place.
And even more likely, nothing would have failed in the first place. Your integrations would have been rock-solid. In the end, your confidence that the data pipeline was running properly would have left you at ease while heading into the weekend.
Next Step: Check out Stonebranch's Data Pipeline Orchestration solution for DataOps.