What Is a DataOps Platform and Why Does Your Team Need One

DataOps (data operations) is a process-oriented methodology for data teams. It encompasses the best qualities of DevOps (development and operations) methodology, such as faster development and easier maintenance, and lets you apply them to big data.

DataOps platforms are used by data teams as centralized command centers that let you orchestrate data pipelines at various stages in one place.

If you’re looking for ways to improve your data practices and make development more efficient, you should consider adopting a DataOps platform. In this guide, you’ll learn about DataOps and the advantages of a DataOps platform as well as how to choose the right one for you.

What Is a DataOps Platform

Many developers are familiar with DevOps, which is a philosophy and a set of tools aimed at streamlining the end to end development cycle and encourages close interaction between developers and others within the organization. This includes things like version control, code reviews, automated testing, CICD, isolated testing environments, and more. The goal is clear: to reduce time to market, ensure flexibility and continuous delivery, improve service quality, and generally, make informed operational decisions.

DataOps has a lot in common with DevOps, but instead of talking about software development, DataOps pertains to data analytics. It’s designed to streamline workflows related to the extraction of valuable insights from data. It also enables productive collaboration both within data teams and with other departments.

DataOps platform is a unified space where a team can collect, study, and use data to make objective business decisions in a consistent centralized way. The platform provides necessary tools for implementing DataOps best practices including version control, CICD, code reviews, access rights, data quality testing, automation, and integration of necessary operations.

A typical data platform meets the needs of an organization’s data handling, like collecting, organizing, storing, and use (i.e. reporting or data science). However, unlike a DataOps platform, there is no guarantee that the development cycle will be efficient and the output will be optimal.

In general, companies who use DataOps platforms and adopt DataOps best practices can expect to improve their development cycle time, reduce data quality issues, provide better support and maintenance, and benefit from more reliable business decisions.

Why You Need a DataOps Platform

First, let’s revisit the four V’s of big data:

  • Data volume: Organizations can have extreme volumes of data sitting in a data lake. For business intelligence or reporting, analytics engineers depend on the data from a data lake to prepare a filtered and aggregated table.
  • Data variety: Not only does data go through multiple stages of processing, it also comes from various data sources, often in different formats. Organizations have transactional databases, SaaS software, product catalogs, social media, etc. With analytical use cases in mind, it makes sense to extract the data from their source systems and store them in a centralized repository like a data lake or data warehouse.
  • Data velocity: Today’s data stacks are able to process batches at a high frequency or real-time events.
  • Data veracity: With large volumes of data coming from so many data sources processed by multiple systems, organizations must have a system to monitor their data’s integrity.

A DataOps platform helps you manage these four data categories by helping data teams understand the lineage of their organizations’ data pipelines: where does it come from? How is it processed? What systems consume it? How long does it take for a batch job to complete?

But it’s not only about maintaining an overview of existing pipelines. A DataOps platform also helps in their development cycle. While software developers tend to focus on applications with small test databases, data analysts and scientists need secure environments that run applications alongside handling terabytes or even hundreds of terabytes of data.

However, data pipelines can be characterized as a Jenga tower. Take one brick away, and the whole thing collapses. A DataOps platform helps you create isolated and secure temporary test environments that allow modification, experimentation, and innovation before putting the desired changes into production.

In other words, a DataOps platform helps organizations to streamline, standardize, and automate the development, production, and management of their data pipelines, in line with DataOps best practices.

Advantages of a DataOps Platform

To talk about the advantages and disadvantages of DataOps, it’s useful to have a look at what a data stack architecture looks like:

A data stack is a set of technologies and software products that a company uses to collect, store, analyze, and use (i.e. reporting or data science) data. If you have a website or application, the process would traditionally look like this:

  • Collect the data: This includes personal data, history of transactions, and purchase history (with regard to regulations, of course).
  • Send it to the data warehouse: Centralizing data from multiple sources in one place lets you combine them to make them more valuable than the sum of their parts.
  • Analyze the data: By using data analytics, machine learning, and other tools, you can extract useful business insights.

The process looks simple, but it can become complicated if you have to integrate multiple third-party services for collecting, storing, and analyzing data.

If you implement a DataOps platform and adopt DataOps best practices, you can reduce labor costs, enhance data quality, improve analytics, and more. Let’s take a more in-depth look at the advantages of implementing a DataOps platform.

Reduce Labor Costs

DataOps focuses on process-oriented methodologies and automation that dramatically improve workforce productivity. By introducing intelligent testing and observation mechanisms into the analytic pipeline, teams can stay focused on strategic tasks rather than plod over spreadsheets looking for anomalies.

Increase Data Quality

The creation of automated, repetitive processes, along with automated code testing, data quality assertions, and controlled deployments reduces the likelihood of wrong or unexpected data downstream.

Improve Analytics

Automated reception, processing, and aggregated analytics of incoming data streams, combined with error elimination, let you instantly understand ​​customer behavior patterns, market shifts, and price fluctuations.

See a Broader Picture

In addition to critical everyday data, DataOps can provide an aggregated view over time of the entire flow of data across an organization to end users. This will help identify macro trends and detect changes in typical patterns of activity over a specified period of time. Getting an overview of data isn’t possible if you respond to anomalies and errors with manual processes.

Limitations of a DataOps Platform

While there are many advantages of using a DataOPs platform, there are also limitations to consider.

Isolation of Subdivisions

While teams of data specialists try to overcome the isolation of certain divisions and departments of the company, they might face system inertia, technological obstacles, and even conscious resistance. It’s important to plan well for database integration, involve employees of both development and operations departments in the process, and pay attention to their feedback.

Other Tools

The implementation of DataOps inevitably goes into discussions about the feasibility of investing in relatively expensive tools or maintaining a custom tool in-house.

While custom solutions, tailored for a specific business model, initially appear cheap, the total cost of ownership can quickly outpace a platform’s license fees, if they are not managed properly. While off-the-shelf tools can be relatively expensive upfront, they allow an organization to quickly shift gears.

Specialist Training

Many data professionals don’t have the opportunity or time to develop their skills to work with a new tool or platform, or they don’t have the bandwidth to take on additional responsibilities. When working on a new platform, like DevOps, it’s inevitable that training or acquiring new personnel would be required. The data plan should take into account the onboarding time or development of staff members.

How You Can Choose the Right Platform

Currently, there are not many robust DataOps platforms in the market. A good DataOps platform should automate data delivery from multiple sources at the same time, supporting both structured and unstructured data across hybrid and cloud environments. In large organizations, this process is messy and can easily become overwhelming. While there are a few vendors who are working on complete platformsMeltano has forged ahead and created an open-source DataOps platform that can manage all the data in your tool stack.

Here are a couple of questions that you can ask yourself when choosing a DataOps platform:

What Are Your Goals?

In some way, whether or not your organization should adopt a DataOps platform is driven by what you’re trying to achieve with your team of data/analytics engineers. What are your specific goals? Do you want to:

  • reduce development time
  • reduce bug (i.e. data quality issues) counts
  • reduce time to bug fixes
  • reduce data pipeline failures

But your decision should also be driven by the data maturity of your organization. Can your team maintain its own DataOps-driven system for developing data pipelines without the support of a DataOps platform? Is there enough IT support for setting up and maintaining development environments? Is there technology in place to collaborate on projects and automate deployments?

How Are You Going to Track Progress?

Choosing a DataOps platform, implementing it, and using it is all about efficiency gains in the data lifecycle. Tracking how a DataOps platform impacts the work of a data team can be measured with a set of KPIs:

  • time to data pipeline development
  • time to data pipeline bug fixes
  • the number of data quality issues
  • the number of data pipeline failures

How Do You Interpret the Results?

Since we’re talking about efficiency gains, the KPIs described in the previous paragraph should decrease. However, in many organizations, implementing a new tool, along with new procedures, doesn’t directly translate into profit. It takes time for people to adopt and get used to new practices. In addition, adopting new tools and practices might expose a lot of issues that weren’t visible before.

Nevertheless, after a couple of weeks, after proper training and an initial catch-up, you can expect the adoption of a DataOps platform to translate into a more efficient data lifecycle.


For most organizations, the process of turning raw data into insights is a difficult task. DataOps applies DevOps concepts and principles to data processing and analytics. As a result, working with data becomes more flexible and less labor-consuming. To help them make the transition, many organizations are adopting a DataOps platform.

To choose the right DataOps platform, you should take into account your organization’s goals and capabilities. Consider Meltano, a DataOps platform that integrates the best open-source tools in the market.

Guest written by Yulia Gavrilova. Thanks Yulia!


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