When it works, DataOps delivers the right data, at the right time, to the right stakeholder within complex organizations, and the results of this superpower are hard to overstate. That's why companies across the spectrum continue to adopt the DataOps framework. Once a meaningless term, DataOps is now becoming one of the few industry buzzwords that live up to the hype. But with so many moving parts, if DataOps lacks a strong foundation, it can quickly fall apart and cause more harm than good.

To deploy DataOps effectively, organizations must make data integration a core priority within the framework. Without a fast, agile process for adding data sources, the whole DataOps framework collapses on itself.  Sometimes organizations find out the hard way: 'Ops' is useless without 'Data.'

DataOps: The Promise, and Perils, of a Multi-Pronged Framework  

Organizations leverage DataOps to deliver high-quality, on-demand data to institutional customers. The framework automates data orchestration across an entire organization by combining agile development, DevOps, data personnel, and data management into a single data framework. DataOps is designed to speed up the creation and deployment of automated, end-to-end data workflows, from data source to data consumer.

As organizations grow and data demands become more complex, this flexible data framework offers a customizable approach for delivering data, when, where, and how it's needed. But with so many components in DataOps, teams can sometimes forget the most basic but essential functionality of the framework: data integration.

Why Data Integration is the Foundation for DataOps

Data itself remains the most essential part of a DataOps framework. Without data streams, data pipelines are empty, SQL transformations are never executed, data analysts have nothing to do, and insights are seldom uncovered. If the DataOps framework is the 'engine,' then data is the 'fuel.' And the 'pumps' that put this 'fuel' in the 'engine' are data connectors.

Many teams still build data connectors for data source APIs in-house. The in-house team – composed of data engineers and an assortment of dev talent – has to code, test, and deploy the data connectors using internal resources. This manual process takes time and specialized labor, which slows down the integration of the data source into the framework. In this scenario, internal customers might have to wait weeks or months to access the data they need. And teams not only suffer from data blindness but also lose the core advantages: speed and agility.

The whole point of DataOps is to quickly deliver bespoke data to institutional customers. Teams must build, launch, and modify data infrastructure near-instantly. Creating data connectors manually does the exact opposite, bogging down data teams in weeks of data infrastructure construction. This is how the basic foundation of DataOps – the ability to rapidly add new data streams – falls apart.

Organizations that build data connectors in-house might be able to sustain a DataOps framework initially. But as the company scales, the framework will break down. The process of adding new data sources, and updating API changes, will not only waste labor and resources. The associated inefficiencies – lack of data, longer time-to-launch, slower projects – will undermine the essential premise of the framework. At that point, the organization is not practicing DataOps at all. It's simply doubling down on the old, uncompetitive data management model of yesteryear.

That's why many DataOps teams have turned to data management platforms to integrate data sources. Data management platforms offer pre-built data connectors for API data sources. Pre-built connectors eliminate the manual, laborious process of constructing new data connectors from scratch. With pre-built connectors, organizations can integrate data sources immediately, rather than waiting weeks or months.

There's a caveat though: data management platforms do not offer pre-built connectors for every data source. Teams will inevitably encounter coverage gaps. But true DataOps platforms will provide a solution for these blind spots. Some data management platforms will create pre-built data connectors for clients upon request. Other data management platforms maintain custom APIs that clients can leverage to build their own data connectors – without all the hair-pulling.

Whatever the solution, all that matters is that new data sources can be integrated into the data framework quickly. This allows teams to move at the speed of DataOps.

Back to Basics: Build a Scalable DataOps Foundation

With DataOps, there's often much talk about the 'Ops' – the agile development, the DevOps for data, the version control. While these components are critical, they're not worth anything if teams cannot access the data they need. Without data, the sprints, the source control, the CI/CD, and all the other 'Ops' are irrelevant.

Sure, data integration isn't as sexy as many of the other components. But if teams want to build a sturdy, scalable DataOps framework, they need to be able to integrate new data sources into the framework rapidly and efficiently. Without a strong foundation of data integration, a DataOps framework will fall under its own weight, or at least buckle enough to undermine critical advantages.

Author

Itamar Ben Hamo, CEO of Rivery