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Understanding Structured and Unstructured Data

Sisense

Different types of information are more suited to being stored in a structured or unstructured format. 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.

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Salesforce debuts Zero Copy Partner Network to ease data integration

CIO Business Intelligence

For instance, a Data Cloud-triggered flow could update an account manager in Slack when shipments in an external data lake are marked as delayed. Sharing Customer 360 insights back without data replication. Currently, Data Cloud leverages live SQL queries to access data from external data platforms via zero copy.

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Rocket Mortgage lays foundation for generative AI success

CIO Business Intelligence

Modernizing data operations CIOs like Woodring know well that the quality of an AI model depends in large part on the quality of the data involved — and how that data is injected from databases, data warehouses, cloud data lakes, and the like into large language models.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

The Basel, Switzerland-based company, which operates in more than 100 countries, has petabytes of data, including highly structured customer data, data about treatments and lab requests, operational data, and a massive, growing volume of unstructured data, particularly imaging data.

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Databricks’ new data lakehouse aims at media, entertainment sector

CIO Business Intelligence

The data lakehouse is a relatively new data architecture concept, first championed by Cloudera, which offers both storage and analytics capabilities as part of the same solution, in contrast to the concepts for data lake and data warehouse which, respectively, store data in native format, and structured data, often in SQL format.

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Advancing AI: The emergence of a modern information lifecycle

CIO Business Intelligence

Although less complex than the “4 Vs” of big data (velocity, veracity, volume, and variety), orienting to the variety and volume of a challenging puzzle is similar to what CIOs face with information management. When data is stored in a modern, accessible repository, organizations gain newfound capabilities. Connect/Activate.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications.