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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Data Warehouse.

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Important Considerations When Migrating to a Data Lake

Smart Data Collective

Azure Data Lake Storage Gen2 is based on Azure Blob storage and offers a suite of big data analytics features. If you don’t understand the concept, you might want to check out our previous article on the difference between data lakes and data warehouses. Determine your preparedness.

Data Lake 101
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Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

To enhance security, Microsoft has decided to restrict that kind of direct database access in D365 F&SCM and replace it with an abstraction layer comprised of something called “data entities”. OLAP reporting has traditionally relied on a data warehouse. OLAP reporting has traditionally relied on a data warehouse.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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What I Learned At Gartner Data & Analytics 2022

Timo Elliott

I was at the Gartner Data & Analytics conference in London a couple of weeks ago and I’d like to share some thoughts on what I think was interesting, and what I think I learned…. First, data is by default, and by definition, a liability , because it costs money and has risks associated with it.

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

We’ve covered the basic ideas behind data mesh and some of the difficulties that must be managed. Below is a discussion of a data mesh implementation in the pharmaceutical space. DataKitchen has extensive experience using the data mesh design pattern with pharmaceutical company data. . The new Recipes run, and BOOM!