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Data Lakes Meet Data Warehouses

David Menninger's Analyst Perspectives

In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends. He explores the relationship between data warehouses and data lakes and share some of Ventana Research’s findings on the subject.

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DataOps For Business Analytics Teams

DataKitchen

Business analysts must rapidly deliver value and simultaneously manage fragile and error-prone analytics production pipelines. Data tables from IT and other data sources require a large amount of repetitive, manual work to be used in analytics. In business analytics, fire-fighting and stress are common.

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Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Sisense

Data warehouse vs. databases Traditional vs. Cloud Explained Cloud data warehouses in your data stack A data-driven future powered by the cloud. We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Data warehouse vs. databases.

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What is a business intelligence analyst? A key role for data-driven decisions

CIO Business Intelligence

This is done by mining complex data using BI software and tools , comparing data to competitors and industry trends, and creating visualizations that communicate findings to others in the organization.

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Don’t Blink: You’ll Miss Something Amazing!

Cloudera

We can get to faster root-cause analysis and become proactive instead of reactive to changes in markets, business operations, and customer behavior. Making sure data is able to land in real time and be accessed just as fast requires a “best fit” partitioning scheme. Kudu has this covered. appeared first on Cloudera Blog.

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Understanding Structured and Unstructured Data

Sisense

Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Structured vs unstructured data. However, both types of data play an important role in data analysis.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data scientists will often perform data analysis tasks to understand a dataset or evaluate outcomes. Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes.