DataOps

DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization. The goal of DataOps is to deliver value faster by creating predictable delivery and change management of data, data models and related artifacts. DataOps uses technology to automate the design, deployment and management of data delivery with appropriate levels of governance, and it uses metadata to improve the usability and value of data in a dynamic environment.

References

  • A Beginner’s Guide to DataOps
    As businesses race to get the most out of their data, they’re also looking to streamline the data analysis process. Find out why DataOps is gaining traction, what it is, and how it’s different from Data Ops and DevOps.
    🔗Absolutdata
  • DataOps - Wikipedia
    https://en.wikipedia.org/wiki/DataOps
    🔗en.wikipedia.org
  • What is Data Ops?
    Explore the key components of Data Ops in an enterprise and learn about the most common use cases. Implement the right solution using Infrastructure-as-Code.
    🔗Nexla
  • What is DataOps
    Confused by all the DataOps info out there? Find out what is Data Operations, how it works, why modern organizations need it, and how to get started.
    🔗dataops.live
  • Definition of Data Ops - Gartner Information Technology Glossary
    Data ops is the hub for collecting and distributing data, with a mandate to provide controlled access to systems of record for customer and marketing performance data, while protecting privacy, usage restrictions and data integrity.
    🔗Gartner
  • DataOps: Framework, Tools and Everything In Between
    When you hear DataOps, the first thing that comes to mind is most likely DevOps for data analytics. And while that is not entirely inaccurate because it adopts some principles from DevOps, they are two different things. So, we will explain the differences between these two. But first, let us
    🔗The Holistics Blog
  • DataOps Explained: How To Not Screw It Up
    DataOps merges data teams to support an organization’s data needs, in a similar way to DevOps. Here's how to not screw it up.
    🔗Monte Carlo Data
  • What is DataOps? Definition and implementation
    Découvrez comment le DataOps peut aider les entreprises à relever les défis liés aux projets de données et d'analyse, notamment la cohésion entre les équipes, l'efficacité des processus et la diversité des technologies. Apprenez également comment implémenter le DataOps pour automatiser la conception, le déploiement et la gestion des flux de livraison de données grâce à diverses pratiques telles que le CI/CD.
    🔗Saagie
  • What is DataOps? Definition, Platforms & DevOps vs DataOps
    Dive into the world of DataOps and learn its definition, tools, platforms & differences between DevOps and DataOps. Get insights from SprinkleData now!
    🔗sprinkledata.com