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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 Lake 106
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Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

Ostensibly, the new product represents Microsoft’s transition to a newer, more cloud-friendly ERP for midsized enterprises. OLAP reporting has traditionally relied on a data warehouse. OLAP reporting based on a data warehouse model is a well-proven solution for companies with robust reporting requirements.

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

AWS Big Data

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. Apache Iceberg integration is supported by AWS analytics services including Amazon EMR , Amazon Athena , and AWS Glue. AWS Glue 3.0

Data Lake 114
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Modernizing Data Analytics Architecture with the Denodo Platform on Azure

Data Virtualization

Reading Time: 2 minutes Today, many businesses are modernizing their on-premises data warehouses or cloud-based data lakes using Microsoft Azure Synapse Analytics. Unfortunately, with data spread.

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What is a Data Mesh?

DataKitchen

The data mesh design pattern breaks giant, monolithic enterprise data architectures into subsystems or domains, each managed by a dedicated team. But first, let’s define the data mesh design pattern. The past decades of enterprise data platform architectures can be summarized in 69 words.

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

The analytics team is under tremendous pressure during the early phases of the drug’s lifetime. The business users propose questions and ideas for new analytics and require rapid response time. Figure 1: Data requirements for phases of the drug product lifecycle. The third set of domains are cached data sets (e.g.,

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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.