What Is DataOps? Data Operations Explained

DataOps is a methodology that automates data flow, streamlines data management, and speeds data delivery.

According to a recent study, 98 percent of companies use data to improve customer experience. However, 89 percent say they struggle with data management.

What’s the solution to this problem? DataOps.

But what is DataOps exactly?

In this article, we’ll share everything you need to know about DataOps, including what it is, its benefits and components, and how you can leverage it for your business.

Plus, we’ll share details about our open-source DataOps Operating System (OS) at Meltano and explain why thousands of companies are building with us to create a seamless data integration experience.

Want to learn more about DataOps?

What is DataOps?

DataOps is a methodology that helps organizations apply technology to automate data flow across the organization.

DataOps handles the entire data lifecycle, including aggregation, preparation for machine learning, and reporting.

DataOps is often compared to lean manufacturing — a production methodology that eliminates waste and continually improves the production process and increases productivity. Waste, in this case, refers to processes and activities that take time but don’t bring value.

DataOps uses pipelines to operate. A data pipeline is a set of tools and processes for moving data from the source to a repository or data warehouse. This pipeline presents the flow of data through different stages, starting from extraction and finishing with data visualization.

Think of a conveyor belt where a product moves from one stage to the next. A data pipeline is like a conveyor belt, with data entering on one end of the pipeline, going through a series of steps, and emerging on the other end in the form of reports or visual data.

In a data pipeline, similar to manufacturing lines, production steps are clearly defined and automated to reduce waste, increase efficiency and ensure the quality of the products.

DevOps Versus DataOps: What’s the Difference?

DataOps has some similarities to DevOps, but the two terms have different meanings.

DevOps is the original concept that DataOps was built on to improve data analytics.

DevOps uses Agile methodology to bring together software development (Dev) teams with operations teams (Ops) and speed up build cycles. DevOps breaks up work into short sprints so teams can collaborate on the most urgent elements at the same time.

This integration between software development and IT operation aims to improve both the speed and quality of software application development.

DataOps also applies the same approach to data analytics by bringing together data engineers and data analysts to deliver data products quickly and with a high level of quality.

So what’s the difference?

While both systems were designed to speed work cycles, and they both apply Agile methodology to achieve that goal, DevOps focuses on delivering software applications. In contrast, DataOps focuses on managing and delivering data products.

How Does DataOps Work?

DataOps uses different technologies, including machine learning (ML), artificial intelligence (AI) paired with Agile methodology, and a range of data management tools to streamline data processing, testing, preparing, deploying, and monitoring.

DataOps also uses statistical process control (SPC) to monitor the data pipeline. SPC uses statistical techniques to control a process—a method popularized in lean manufacturing, which we mentioned above.

These statistical techniques enable control over the data flowing through the pipeline, so any anomalies such as inaccurate or duplicate data are automatically detected.

The DataOps process consists of five key stages:

  • Plan
  • Code
  • Test
  • Deploy
  • Monitor
An infographic depicting the data concept
DataOps applies an approach similar to DevOps by bringing data engineers and analysts together to deliver high-quality data products quickly

What Are the Benefits of DataOps?

As volumes of data increase, so do the challenges surrounding them. Processing large amounts of raw data while reducing errors can be complex.

Enabling your data engineers and employees across departments to efficiently collaborate on data projects will help speed the delivery and the quality of products.
Implementing a DataOps methodology is one step toward achieving those benefits.

The main purpose of DataOps is to enable teams to manage data efficiently and at high velocity. It provides control of the processes while simplifying them at the same time.

The benefits of DataOps include:

Increased Productivity

DataOps reduces manual work by automating the data analytics pipeline. Data can be planned, coded, tested, deployed, and monitored without the need for human intervention.

Data Democratization

Data democratization is the process of enabling everyone within a company, regardless of their technical knowledge, to access and use data to make informed decisions.

DataOps opens up data not only to team members but also to stakeholders. It gives access to every employee within the organization so that all teams within a company can use it to improve business processes.

Improved Analytics

In DataOps, the process of receiving, processing, and aggregating data streams are automated, so it provides you with fast insights into customer behavior, market changes, and price fluctuations.

Maximized Value

DataOps helps maximize the value derived from data by improving its quality and shortening the time it takes to make informed decisions.

Thanks to its Agile-based model, DataOps can quickly turn raw data into valuable insights.

Data Security

According to Gartner, Inc., by 2022, 75 percent of all databases will be deployed to the cloud. However, many companies struggle to protect their data once it’s in the cloud, which makes data breaches quite common. According to Statista, over 155 million people were victims of data breaches from 2005 to 2020.

DataOps establishes a set of security policies to ensure data safety and security. These policies also include data compliance to ensure your sensitive data is protected from theft, misuse, or corruption.

An image showing people looking at data and discussing it.
DataOps helps you gain and maintain control over data while improving data quality and collaboration.

How The Meltano DataOps Platform Streamlines Data Management

Meltano is an open-source DataOps platform developed specifically to tackle the challenges surrounding data management.

Meltano supports data integration, orchestration, and containerization and can be customized to suit the data management needs of any business, regardless of its industry.

Our platform enables you to easily extract data from multiple sources and load it to databases, Software as a Service (SaaS) or application programming interfaces (APIs) while optimizing and managing all the tools in your data stack. We also use Apache Airflow to schedule and monitor pipelines automatically.

Meltano works on the extract, load, transform (ELT) process principles to streamline data extraction from anywhere, load it to a data warehouse, and transform it your way using a structured query language (SQL) tool.

Key features of the Meltano platform include:

  • Project creation: Create your Meltano project as code and deploy it to the cloud so you can collaborate with your team of data scientists, engineers, and business intelligence analysts.
  • Integration: Meltano’s command-line interface enables quick integration of your project so you can start replicating data in just a few clicks.
  • Data transformations: Our open-source data tool enables you to easily transform your data from one format to the other (from data source to data destination).
  • Orchestration: Meltano features Airflow, an open-source framework that takes just seconds to start and allows you to build and run workloads.
  • Containerization: Once your code and all the necessary components are defined in your Meltano project, you can deploy it right away.

Benefits of the Meltano platform include:

  • Deploy anywhere: You have complete flexibility over how and where you deploy your data.
  • Access the largest connector library: Use any of the 300+ taps and targets to extract and load your data.
  • Control everything: Meltano applies the DevOps best practices. allowing you to run and oversee workflow in isolated environments and use end-to-end testing methodology to access quality and performance.
  • Customize the platform. Meltano is an open-source platform so you can modify, enhance and share it as needed to create the perfect tool for your projects.
  • Manage and monitor your data stack. With Meltano’s built-in user interface, you can easily manage your data stack and monitor every step of the process.

Meltano can be integrated in your existing environment simply by adding ELT(P) to your data stack.

Our open-source platform was developed to process data while eliminating errors, improving data quality, enhancing collaboration, and ensuring transparency across pipelines.

Want to Know How Meltano Works?

Wrapping Up On DataOps

Processing high volumes of raw data and using the insights to provide clean, high-quality data and make it accessible can be much easier when you have a system in place.

This system is what DataOps provides. It solves some of the biggest problems of data processing and delivers:

  • Automation
  • Collaboration
  • Innovation

Meltano makes working with data easier and more flexible. It enables you to:

  • Create your own project and integrate it with Meltano’s open-source code platform
  • Transform your data from the source to data warehouse with ease using Singer taps and extractors
  • Oversee everything from workflows, from isolated environments to end-to-end-testing
  • Deploy your data anywhere
  • Manage and monitor your data stack

With Meltano, you can keep all data-related processes in one place, automate them to save time and build better data products, and have control over the whole process.

Interested in Meltano?


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