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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

In other words, data warehouses store historical data that has been pre-processed to fit a relational schema. Data lakes are much more flexible as they can store raw data, including metadata, and schemas need to be applied only when extracting data. Target User Group.

Data Lake 140
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The most valuable AI use cases for business

IBM Big Data Hub

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions.

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Ontotext’s Semantic Approach Towards LLM, Better Data and Content Management: An Interview with Doug Kimball and Atanas Kiryakov

Ontotext

This is the case with the so-called intelligent data processing (IDP), which uses a previous generation of machine learning. Luckily, the text analysis that Ontotext does is focused on tasks that require complex domain knowledge and linking of documents to reference data or master data.

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Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Doesn’t this seem like a worthy goal for machine learning—to make the machines learn to work more effectively?

Metadata 105
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Building a Beautiful Data Lakehouse

CIO Business Intelligence

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

Data Lake 101
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The Superpowers of Ontotext’s Relation and Event Detector

Ontotext

Quality assurance process, covering gold standard creation , extraction quality monitoring, measurement, and reporting via Ontotext Metadata Studio. Using machine learning, RED indicates the impact of events on stock prices. It compares actual price changes to expected changes based on historical data.