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DataOps vs. DevOps: What’s the Difference?

Alation

DataOps and DevOps are two distinctly different pursuits. But where DevOps focuses on product development, DataOps aims to reduce the time from data need to data success. At its best, DataOps shortens the cycle time for analytics and aligns with business goals. What is DataOps? What is DevOps?

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Big data engineer

Dataconomy

Data transformation and preprocessing Big Data Engineers apply algorithms and transformations to raw data, converting it into structured formats suitable for analysis and preparation for downstream applications. Average salary overview The average salary for a Big Data Engineer in the U.S.

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Turnkey Cloud DataOps: Solution from Alation and Accenture

Alation

Data people face a challenge. They must put high-quality data into the hands of users as efficiently as possible. DataOps has emerged as an exciting solution. As the latest iteration in this pursuit of high-quality data sharing, DataOps combines a range of disciplines. Accenture’s DataOps Leap Ahead.

DataOps 52
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Secrets from Data Governance Leaders: DGIQ West 2023 (June 5 – 9)

Alation

The Data Governance & Information Quality Conference (DGIQ) is happening soon — and we’ll be onsite in San Diego from June 5-9. If you’re not familiar with DGIQ, it’s the world’s most comprehensive event dedicated to, you guessed it, data governance and information quality. The best part?

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The Audience for Data Catalogs and Data Intelligence

Alation

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., ML and DataOps teams). After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that.

DataOps 52
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Demystifying Data Mesh

Precisely

Watch Preparing for a Data Mesh Strategy Key pillars when preparing for a data mesh strategy include: A mature data governance strategy to manage and organize a decentralized data system. Proper governance ensures that data is uniformly accessible and the appropriate security measures are met.

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

IBM Journey to AI blog

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

Big Data 106