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The Rise of Unstructured Data

Cloudera

Here we mostly focus on structured vs unstructured data. In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else.

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Your Generative AI LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers

DataKitchen

Your LLM Needs a Data Journey: A Comprehensive Guide for Data Engineers The rise of Large Language Models (LLMs) such as GPT-4 marks a transformative era in artificial intelligence, heralding new possibilities and challenges in equal measure. DataOps ensures that the data retrieved is relevant, high-quality, and up-to-date.

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Do I Need a Data Catalog?

erwin

Data catalogs combine physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals in certain circumstances. The most optimal and streamlined way to achieve this is by using a data catalog, which can provide a first stop for users ahead of working in BI platforms.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

And, as industrial, business, domestic, and personal Internet of Things devices become increasingly intelligent, they communicate with each other and share data to help calibrate performance and maximize efficiency. The result, as Sisense CEO Amir Orad wrote , is that every company is now a data company. This is quantitative data.

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Migrate data from Google Cloud Storage to Amazon S3 using AWS Glue

AWS Big Data

We’ve seen that there is a demand to design applications that enable data to be portable across cloud environments and give you the ability to derive insights from one or more data sources. With this connector, you can bring the data from Google Cloud Storage to Amazon S3.

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How to Take Back 40-60% of Your IT Spend by Fixing Your Data

Ontotext

This is partly because integrating and moving data is not the only problem. The data itself is stored in a way that is not optimal for extracting insight. Unlocking additional value from data requires context, relationships, and structure, none of which are present in the way most organizations store their data today.

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The new challenges of scale: What it takes to go from PB to EB data scale

CIO Business Intelligence

To accomplish this, we will need additional data center space, more storage disks and nodes, the ability for the software to scale to 1000+PB of data, and increased support through additional compute nodes and networking bandwidth. Focus on scalability.