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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

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. Both data warehouses and data lakes are used when storing big data.

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Modernizing Data Architectures

Data Virtualization

Recently, we have seen the rise of new technologies like big data, the Internet of things (IoT), and data lakes. But we have not seen many developments in the way that data gets delivered. Modernizing the data infrastructure is the.

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Waking Up The World of Big Data

Sisense

The term “Big Data” has lost its relevance. The fact remains, though: every dataset is becoming a Big Data set, whether its owners and users know (and understand) that or not. Big Data isn’t just something that happens to other people or giant companies like Google and Amazon. Big Data Today.

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Apache Iceberg optimization: Solving the small files problem in Amazon EMR

AWS Big Data

In our previous post Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes , we discussed how you can implement solutions to improve operational efficiencies of your Amazon Simple Storage Service (Amazon S3) data lake that is using the Apache Iceberg open table format and running on the Amazon EMR big data platform.

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Otis takes the smart elevator to new heights

CIO Business Intelligence

Otis One’s cloud-native platform is built on Microsoft Azure and taps into a Snowflake data lake. IoT sensors send elevator data to the cloud platform, where analytics are applied to support business operations, including reporting, data visualization, and predictive modeling. based company’s elevators smarter.

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Big Data for Business: A Requirement for Today’s Business Analytics

David Menninger's Analyst Perspectives

Organizations now must store, process and use data of significantly greater volume and variety than in the past.

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Keys to Ensure that Data isn’t Slowing Down your Innovation Efforts

Cloudera

For those models to produce meaningful outcomes, organizations need a well-defined data lifecycle management process that addresses the complexities of capturing, analyzing, and acting on data. If the data goes into a data lake before analysis, extracting it can get pretty complex and time-consuming.