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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. One study forecasts that the market will be worth $23.8 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.

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Reference guide to build inventory management and forecasting solutions on AWS

AWS Big Data

Forecasting is another critical component of effective inventory management. However, forecasting can be a complex process, and inaccurate predictions can lead to missed opportunities and lost revenue. However, forecasting can be a complex process, and inaccurate predictions can lead to missed opportunities and lost revenue.

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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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How the BMW Group analyses semiconductor demand with AWS Glue

AWS Big Data

The main requirement is to have an automated, transparent, and long-term semiconductor demand forecast. Additionally, this forecasting system needs to provide data enrichment steps including byproducts, serve as the master data around the semiconductor management, and enable further use cases at the BMW Group.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

For instance, for a variety of reasons, in the short term, CDAOS are challenged with quantifying the benefits of analytics’ investments. Some of the work is very foundational, such as building an enterprise data lake and migrating it to the cloud, which enables other more direct value-added activities such as self-service.

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How DataOps is Transforming Commercial Pharma Analytics

DataKitchen

Marketing invests heavily in multi-level campaigns, primarily driven by data analytics. This analytics function is so crucial to product success that the data team often reports directly into sales and marketing. Figure 3: The vast and varied types of analytics required during the launch phase.

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How CIOs reinterpret their role through AI

CIO Business Intelligence

A strengthening role in IT “Real-time analytics, forecasting, and alerting capabilities enable us to intervene when problems arise and prevent disruptions,” says Deligia. The robot took the data from the old provider to the new one, producing new contracts, and sending them to about 700 branches and merchants with digital signatures.