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What is DataOps?

DataOps (Data Operations) is an automated and process-oriented data management practice. It enhances communication, integration, and data flow automation among data sources and consumers.

DataOps optimizes the development and execution of data pipelines by reducing the time it takes to build a data pipeline, accelerating building environments, generating high-quality datasets, and thus achieving reliable and predictable data delivery.

DataOps platform overview

To implement DataOps processes, we've created DataOps.live — a fully-managed DataOps SaaS platform that can help to automate, simplify, and enrich your data flows in a few clicks.

high-level DataOps.live overview __shadow__

It offers:

  1. Automation to manage Snowflake as Infrastructure as Code across all environments, e.g., dev > test > prod
  2. Orchestration to orchestrate all critical components of your modern data platform, from your ingestion tool to your data catalog
  3. Unified Observability to understand, manage, and operate on metadata of your datasets, pipelines, and infrastructure
  4. Federated Governance to consistently bring all the foundational layers to all your data engineering teams
  5. Unified Developer Experience to bring a top-notch developer experience to all your data engineering work

Your DataOps implementation

Using the DataOps platform, you can build a more efficient system that maintains higher data quality, enhanced speed, better governance, and more reliable response to errors. DataOps.live brings the speed and agility of DevOps and CI/CD to data platforms, such as the ability to accurately develop, branch, and deploy both code and data.

The platform comprises many applications providing specialized capabilities ranging from data ingestion, data quality, and data transformation to data observability and governance. Orchestrators are your building blocks to orchestrate all your applications to create high-value data products.

With DataOps.live, you can:

  • Move your source of truth to Git
  • Build and rebuild your data platforms from the Git repository in minutes
  • Clone complete data environments in seconds
  • Create feature branches of the code and the data platform for every new feature
  • Develop new features faster and in parallel
  • Reduce errors with automated testing
  • Increase productivity
  • Track every change ever made

Get started with DataOps

So what's next? To start learning by doing, create your first project and set up your DataOps execution environment.

To learn more about DataOps, see what to read next.