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5 Layers of Data Lakehouse Architecture Explained

Monte Carlo

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. But they should!

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Data Lakehouse Architecture Explained: 5 Layers

Monte Carlo

You know what they always say: data lakehouse architecture is like an onion. …ok, Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. But they should!

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[O’Reilly Book] Chapter 1: Why Data Quality Deserves Attention Now

Monte Carlo

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard?

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Innovating Operations in Agriculture: Kramp’s Real-Time Analytics Journey

Striim

Kramp, a stalwart in the distribution of agricultural spare parts and accessories across Europe, embarked on a transformative journey five years ago with a bold vision to overhaul its data management system. Striim’s platform provided a developer-friendly environment and stability across Kramp’s data operations.

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

DataKitchen

Data in Place refers to the organized structuring and storage of data within a specific storage medium, be it a database, bucket store, files, or other storage platforms. In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers.

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How to Treat Your Data As a Product

Monte Carlo

For example, when you think about a data warehouse , it’s really just a codebase—primarily composed of SQL—that’s serving internal customers like other analysts, data scientists, and product managers who are using that data to go and make business decisions.

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