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Analytics databases

Dataconomy

Analytics databases, also referred to as analytical databases, are specialized systems designed specifically for analyzing large volumes of historical data. Definition and functionality The primary purpose of analytics databases is to provide a platform for businesses to efficiently analyze historical metrics.

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Tableau Data Types: Definition, Usage, and Examples

Pickl AI

Summary: Tableau is fantastic for data visualization, but understanding your data is key. Data types in Tableau act like labels, telling Tableau if it’s a number for calculations, text for labels, or a date for trends. Using the right type ensures accuracy and avoids misleading visuals.

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Business Intelligence for Fairs, Congresses and Exhibitions

Smart Data Collective

If you occasionally run business stands in fairs, congresses and exhibitions, business stands designers can incorporate business intelligence to aid in better business and client data collection. Business intelligence tools can include data warehousing, data visualizations, dashboards, and reporting.

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Deciphering The Seldom Discussed Differences Between Data Mining and Data Science

Smart Data Collective

This new approach has proven to be much more effective, so it is a skill set that people must master to become data scientists. Definition: Data Mining vs Data Science. Data mining is an automated data search based on the analysis of huge amounts of information. Data Mining Techniques and Data Visualization.

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Self-Service Analytics for Google Cloud, now with Looker and Tableau

Tableau

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. This partnership makes data more accessible and trusted.

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Build your gen AI–based text-to-SQL application using RAG, powered by Amazon Bedrock (Claude 3 Sonnet and Amazon Titan for embedding)

AWS Machine Learning Blog

JSONs inherently structured format allows for clear and organized representation of complex data such as table schemas, column definitions, synonyms, and sample queries. This structure facilitates quick parsing and manipulation of data in most programming languages, reducing the need for custom parsing logic.

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How to Optimize Power BI and Snowflake for Advanced Analytics

phData

Two of the platforms that we see emerging as a popular combination of data warehousing and business intelligence are the Snowflake Data Cloud and Power BI. Debuting in 2015, Power BI has undergone meaningful updates that have made it a leader not just in data visualization, but in the business intelligence space as well.