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Is DataOps more than DevOps for data?

Dataconomy

DataOps and DevOps are collaborative approaches between developers and IT operations teams. This communication and collaboration approach was then applied to data processing. The trend started with DevOps first.

DataOps 239
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Five Important Trends in Big Data Analytics

Flipboard

Over the last few years, with the rapid growth of data, pipeline, AI/ML, and analytics, DataOps has become a noteworthy piece of day-to-day business New-age technologies are almost entirely running the world today. Among these technologies, big data has gained significant traction. This concept is …

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Big data engineer

Dataconomy

Big data engineers are essential in today’s data-driven landscape, transforming vast amounts of information into valuable insights. As businesses increasingly depend on big data to tailor their strategies and enhance decision-making, the role of these engineers becomes more crucial.

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Why FinOps Needs DataOps Observability

insideBIGDATA

In this special guest feature, Chris Santiago, Vice President/Solutions Engineering, Unravel Data, talks about controlling cloud spend through three phases of the FinOps lifecycle.

DataOps 243
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Big data management

Dataconomy

Big data management encompasses the intricate processes and technologies that organizations employ to handle vast amounts of data. As businesses increasingly rely on data to drive strategies and decisions, effective management of this information becomes essential for achieving competitive advantage and insights.

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AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

Driven by significant advancements in computing technology, everything from mobile phones to smart appliances to mass transit systems generate and digest data, creating a big data landscape that forward-thinking enterprises can leverage to drive innovation. However, the big data landscape is just that.

Big Data 106
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Improving Data Pipelines with DataOps

Dataversity

It was only a few years ago that BI and data experts excitedly claimed that petabytes of unstructured data could be brought under control with data pipelines and orderly, efficient data warehouses. But as big data continued to grow and the amount of stored information increased every […].

DataOps 59