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Low Friction Data Governance With Immuta

Data Engineering Podcast

Summary Data governance is a term that encompasses a wide range of responsibilities, both technical and process oriented. One of the more complex aspects is that of access control to the data assets that an organization is responsible for managing. What is data governance? How is the Immuta platform architected?

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Building A Data Governance Bridge Between Cloud And Datacenters For The Enterprise At Privacera

Data Engineering Podcast

Summary Data governance is a practice that requires a high degree of flexibility and collaboration at the organizational and technical levels. The growing prominence of cloud and hybrid environments in data management adds additional stress to an already complex endeavor.

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The Future of Data Governance: 4 Trends to Watch Out For

Monte Carlo

Data is among your company’s most valuable commodities, but only if you know how to manage it. More data, more access to data, and more regulations mean data governance has become a higher-stakes game. Data Governance Trends The biggest data governance trend isn’t really a trend at all—rather, it’s a state of mind.

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Data Warehouse vs. Data Lake

Precisely

Data warehouse vs. data lake, each has their own unique advantages and disadvantages; it’s helpful to understand their similarities and differences. In this article, we’ll focus on a data lake vs. data warehouse. It is often used as a foundation for enterprise data lakes.

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What Are the Best Data Modeling Methodologies & Processes for My Data Lake?

phData: Data Engineering

With the amount of data companies are using growing to unprecedented levels, organizations are grappling with the challenge of efficiently managing and deriving insights from these vast volumes of structured and unstructured data. What is a Data Lake? Consistency of data throughout the data lake.

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Top Data Lake Vendors (Quick Reference Guide)

Monte Carlo

Data lakes are useful, flexible data storage repositories that enable many types of data to be stored in its rawest state. Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption.

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Add Version Control To Your Data Lake With LakeFS

Data Engineering Podcast

Summary Data lakes are gaining popularity due to their flexibility and reduced cost of storage. Along with the benefits there are some additional complexities to consider, including how to safely integrate new data sources or test out changes to existing pipelines.

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