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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources.

Data Lake 140
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Data governance in the age of generative AI

AWS Big Data

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses.

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Salesforce debuts Zero Copy Partner Network to ease data integration

CIO Business Intelligence

Currently, a handful of startups offer “reverse” extract, transform, and load (ETL), in which they copy data from a customer’s data warehouse or data platform back into systems of engagement where business users do their work. It works in Salesforce just like any other native Salesforce data,” Carlson said.

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Do I Need a Data Catalog?

erwin

Given the value this sort of data-driven insight can provide, the reason organizations need a data catalog should become clearer. It’s no surprise that most organizations’ data is often fragmented and siloed across numerous sources (e.g., Three Types of Metadata in a Data Catalog. Technical Metadata.

Metadata 132
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How to get powerful and actionable insights from any and all of your data, without delay

Cloudera

To address this, they focused on creating an experimentation-oriented culture, enabled thanks to a cloud-native platform supporting the full data lifecycle. This platform, including an ad-hoc capable data warehouse service with built-in, easy-to-use visualization, made it easy for anyone to jump in and start experimenting.

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Ontotext Knowledge Graph Platform: The Modern Way of Building Smart Enterprise Applications

Ontotext

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data. The first challenge here is how to enable agile enterprise information management.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Streaming jobs constantly ingest new data to synchronize across systems and can perform enrichment, transformations, joins, and aggregations across windows of time more efficiently. For building such a data store, an unstructured data store would be best. versions).