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Rapidminer Platform Supports Entire Data Science Lifecycle

David Menninger's Analyst Perspectives

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. Rapidminer Studio is its visual workflow designer for the creation of predictive models.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure. Meet the data lakehouse.

Data Lake 109
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Your 5-Step Journey from Analytics to AI

CIO Business Intelligence

Which type(s) of storage consolidation you use depends on the data you generate and collect. . One option is a data lake—on-premises or in the cloud—that stores unprocessed data in any type of format, structured or unstructured, and can be queried in aggregate. Set up unified data governance rules and processes.

Analytics 103
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Azure Data Sources for Data Science and Machine Learning

Jen Stirrup

You can also use Azure Data Lake storage as well, which is optimized for high-performance analytics. It has native integration with other data sources, such as SQL Data Warehouse, Azure Cosmos, database storage, and even Azure Blob Storage as well. Azure Data Lake Store. Azure Data Lake Analytics.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Most of the data management moved to back-end servers, e.g., databases. So we had three tiers providing a separation of concerns: presentation, logic, data. Note that data warehouse (DW) and business intelligence (BI) practices both emerged circa 1990. We keep feeding the monster data.

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The Cloud Connection: How Governance Supports Security

Alation

Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. Pushing data to a data lake and assuming it is ready for use is shortsighted. They strove to ramp up skills in all manner of predictive modeling, machine learning, AI, or even deep learning.