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DataOps vs. MLOps: Similarities, Differences, and How to Choose

Databand.ai

DataOps , short for Data Operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data management processes. It aims to streamline the entire data lifecycle—from ingestion and preparation to analytics and reporting.

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5 Layers of Data Lakehouse Architecture Explained

Monte Carlo

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake. This starts at the data source.

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Data Lakehouse Architecture Explained: 5 Layers

Monte Carlo

This architecture format consists of several key layers that are essential to helping an organization run fast analytics on structured and unstructured data. A visualization of the flow of data in data lakehouse architecture vs. data warehouse and data lake. This starts at the data source.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. These layers help teams delineate different stages of data processing, storage, and access, offering a structured approach to data management. What is Data in Use?