What DataOps isn't...

Just DevOps for data.

One tool that magically solves your data problems.

Getting rid of your existing infrastructure and starting from zero.

Hiring a new team of DataOps specialists.

So, what is DataOps?

DataOps applies the principles of Agile, DevOps and Lean Manufacturing to data management. It brings together the tools you love, the processes you need and your people, in a single place.

1. Agile and DataOps

Agile is an iterative project management principle for software projects. With Agile, IT teams can release new software within a few hours, not months, without compromising on quality.

Data teams can use the principles of Agile to work with big data and drive quick business decision-making.

2. DevOps and DataOps

DevOps breaks down the silos between development and operations teams within organizations. It makes software development and deployment faster, easier and more collaborative.

Data teams working in silos can use the principles of DevOps to collaborate better and deploy faster.

3. Lean Manufacturing and DataOps

Manufacturing happens in pipelines—raw materials flow through various manufacturing workstations to be transformed into finished goods. Minimal waste, greater efficiency without sacrificing product quality.

Data teams build pipelines to transform data into insightful reports or visualizations.

Trusted by 200+ teams globally

Trusted by 200+ teams globally

Atlan is a human-first DataOps platform that powers data teams in 50 countries around the world. Meet the collaboration and orchestration layer that your data team has been waiting for.

Still not convinced why you need DataOps?

Businesses today need data to be present everywhere it is required, for anyone who requires it, in whichever format as desired. However, data environments exploded in terms of scale and complexity with growing security concerns, leading to chaos.

01 Technology overload

From storage, data preparation and analysis tools to governance and data management products, there's an overload of tools and technologies.

02 Complex infrastructure

Only a small fraction of real-world ML systems is the ML code (the black box). The required surrounding infrastructure is vast, complex and costly.

03 Diverse roles and mandates

The humans of data are a diverse lot, each with their own skill sets, responsibilities, tool choices and preferences, leading to a collaboration overhead.

Bring order to chaos with the DataOps movement

DataOps is one of the three innovation triggers listed in data management by Gartner in their 2018 innovation insight report.
At Atlan, we bring together the principles of DataOps with a human-first experience that data teams will actually use.

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