Data Operations (DataOps) Explained: What You Need to Know

CEO, Portable

The latest approach to data management is DataOps. It is one of the many '-ops' similar to DevOps, SecOps, and DevsecOps. So, what exactly is DataOps or Data Operations?

Let's find out.

What Is DataOps?

DataOps, a combination of Data and Operations, refers to developing and managing data pipelines. It combines the 3P — people, process, and products — just like DevOps combines development with IT operations to enable better data management within an organization.

But DataOps is more than two or three teams coming together. It's a new approach that changes the way businesses manage data.

Andy Palmer, the CEO of Tamr, popularized the term DataOps. In an article published in 2015, he explained DataOps as a way to manage data in today's complex world, where crucial decision-making is driven by data.

Why DataOps?

The need for DataOps stems from data governance. Gartner estimates that poor data quality resulting from subpar data governance structure costs companies $15 million per year on average.

Furthermore, the onus of securing the data companies collect and process falls on the companies themselves. Failing to do so attracts hefty fines from regulators. Google was fined €57 million by French lawmakers for non-compliance with GDPR rules.

These are grave mistakes that hurt your business value.

With the rise of big data and machine learning, traditional data governance practices like manual version control and metadata control are no longer feasible. You must have a more automated way of managing data flows, which is where DataOps comes into play.

DataOps brings the rigor necessary to modern data pipeline challenges. This is similar to how DevOps bought rigor to software development a decade or so ago.

All in all, here's why DataOps is rising in importance across the board:

  • Better speed — DataOps reduces human errors at scale. This speeds up data transformation processes and allows operations teams to complete projects faster.

  • Reliability — Along with speed, this approach grants data reliability. Traditionally, data science teams had to worry about the reliability of processed data. But DataOps solves that problem.

  • More control — Teams gain more control over the data they process, eliminating silos. When working with different teams, proper data duplication or contamination can occur. But not anymore with the DevOps approach.

  • More collaboration — Just like DevOps, DataOps brings working groups together. Data analysts collaborate through the Entire Data Lifecycle and ensure everyone is on the same page. No wonder why lean manufacturing managers prefer such collaborative approaches.

  • Avoid non-compliance — DataOps will help you stay in compliance with the regulatory bodies. Its holistic approach to managing and storing data is core to what regulators need organizations to do: protect consumers' data.

Principles of DataOps

When it comes to DataOps, it has the following set of principles (as defined by Andy Palmer in his book, Getting DataOps Right):

  • Open — Embrace open-source technologies and adopt the relevant open-source standards. This makes it easier for companies to adopt while lowering the cost. This also avoids lock-ins with a particular vendor, not at least for the long term.

  • Highly automated — Automation is a key principle streamlining many tasks within data governance, like data pipelines, quality checks, monitoring, etc.

  • Implements best-of-breed tools — DataOps approach forces teams to use the best tools for all data jobs. Moreover, the tools will be different for different jobs. DataOps keeps the team on their toes when selecting the tools for their tasks.

  • Use Table(s) In/Table(s) Out protocols — The Table(s) In/Table(s) Out protocols simplify data integration by offering relevant, well-defined interfaces. This allows the team to separate responsibilities between the data management tasks.

  • Layered interfaces — If you need to work with multiple levels of abstraction (raw vs. aggregated data, for example), DataOps can offer a layered approach. This increases efficiency and allows teams to maintain data pipelines.

  • Tracking data lineage — Tracking data lineage is like tracking software versions in DevOps. DataOps borrows the same concept and allows teams to keep track of the data lineage. With this knowledge, stakeholders can understand how and when data was generated, collected, processed, and updated.

  • Space probabilistic data integration — DevOps has multiple data integration options. This grants more flexibility to data teams and allows them to pick the best approach.

  • Combine aggregated and federated access methods — No two data management tasks are created equal. You must pick the best approach. With DataOps, you get to choose the best storage and access methods. Aggregated (where data is stored in one place) and federated (data stored in multiple places) are two storage types.

  • Data processing in batch and streaming modes — Some projects are suited for batch processing, while others are for streaming processing. DataOps is designed to handle both data models. Batch processing is better for analytics where a large amount of data is involved.

DataOps may (and will) evolve in the future. But the principles described above will stay the same. They will always define a DataOps ecosystem.

DataOps Technology and Tools

A DataOps ecosystem consists of tools that collectively help organizations manage their data. From collection to processing to cleansing, they cover it all.

The tools can be divided into three categories. These are:

  • Data pipeline
  • Data operations
  • Data management

Within these categories, you'll find sub-categories of tools. And that's where things get more specific.

Data pipeline

A data pipeline is a set of data processing tools or elements connected in series. Raw data enters this pipeline from data sources. It either serves as raw data for another pipeline or exits the process.

Within a data pipeline, you'll have three components, which are:

  • Data ingestion
  • Data engineering
  • Data analytics

Data ingestion is how raw data is collected. Data engineering tools process or transform the data as per the requirements. Lastly, the data analytics pipeline analyzes the processed data.

However, a data pipeline is iterative. That's because data scientists have to rejig their processing logic and gather new data for processing/analysis.


There are several technologies are core to DataOps. These are:


CI (continuous integration) and CD (continuous deployment) are practices often grouped in software engineering. The tools that facilitate CI/CD are core to DataOps too. Developers will prepare software that will autonomously generate data for analysis. DataOps must offer an environment where developers can create such tools.


Orchestration tools orchestrate or regulate the interactions of software that work with data in the pipeline. With these tools, developers and data engineers define the workflows, manage software dependencies, and schedule jobs.


Continuous testing is critical to DataOps to ensure data accuracy. Test automation tools facilitate this aspect and help data scientists generate reliable data for analysis and processing without spending too many resources.


DataOps requires you to monitor everything from end to end. Thus, you need to employ certain monitoring or performance management tools. These tools don't test the code base or quality of the data but rather monitor the underlying systems and their impacts on users and applications.

Platform components

Within this sub-category, you have tools like agile collaboration tools, GitHub, container software, and configuration repository to serve as platforms.

Data Management

The third category of DevOps tools is data management. These are the traditional tools used for managing data. Many of them have been incorporated into the DevOps ecosystem. Within the data management category, you have the following sub-category of tools and technologies:

Data capture

In modern-day data capture, companies prefer a streaming-first approach. Here they capture and process data in real-time in a continuous stream.

Data integration

The generated data must be integrated into a staging area, data warehouse, or data lake. There are various tools within the DevOps ecosystem to facilitate data integration, both physically and semantically.

Data preparation

These tools further process the integrated data and turn them into business-ready datasets.

Data analysis

The datasets are then analyzed by data analytics tools. After analysis, the data is presented in a dashboard. Many visualization tools take the analyzed data and prepare business reports. Such tools fall under this category.

Data platform

DevOps ecosystem must have a scalable computing infrastructure. Data platforms like Microsoft Azure fill that need.

DataOps is a continuously evolving field. So, expect to have more or fewer tools to work with in the future. You'll have all-in-one DataOps tools with everything you need to start. Or else, you can choose the tools individually to customize everything according to your business needs.

How to Get Started with DataOps? (The DataOps Framework)

Any data-driven organization must have DataOps in place. Traditional data management is no longer viable. To get started, you need to follow the DataOps Framework and follow these steps:

  • Define goals — Give an aim to your data science project and define the goals. This will remind you and the analytics team why the project is being undertaken. Along with goals, define the key metrics you'll use to track the undertaking's progress, successes, and failures.

  • Get upper management involved — DataOps is something that's going to impact many aspects of the business. And it'd require you to do things differently. Therefore, it's necessary to have the senior shareholders involved and get their buy-ins. Get everyone on the same page to avoid bottlenecks.

  • Create teams with cross-functional roles — Like DevOps, DataOps is a collaborative project. Therefore, you should have cross-functional development team members with years of experience and divide them into teams. Then have them collaborate with a strategic goal.

  • Build a platform team — You should also conceptualize a DataOps team responsible for managing the platform where data will be stored and processed. You can also outsource the same to a specialized firm to focus more on the tasks rather than on the platform.

  • Create a DataOps Enablement function — A DataOps enablement function concerns the tools, processes, and internal team culture to use data to make management decisions. Therefore, you must create a DataOps enablement function to become more data-driven and derive business insights.

  • Incorporate Agile methodologies — DataOps is about making your business more agile. So, you must adopt the agile methodologies and break down the project into several phases.

  • Automate repetitive tasks — As already mentioned, automation is a core principle of DataOps. As far as possible, you should automate tasks and avoid repetition. Look upon artificial intelligence and machine learning for automation.

  • Create a company-wide data governance policy — Data governance policy outlines the steps, procedures, and guidelines for handling data within an organization. It must apply to everyone in the organizational structure based on their roles. Creating such a policy is also crucial from a regulation point of view.

  • Measure and monitor — Once the DataOps ecosystem is up and running, you must measure the progress and monitor everything end-to-end. This is where setting goals and KPIs will give you further guidance.

As you can see, the core concepts of DataOps were more or less the same as DevOps, even though both are different.

As a modern data-driven organization, you cannot afford not to undertake a DataOps project within your company. The barrier to entry is at an all-time low, and the rapid adoption and development landscape is exciting.