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5 modern challenges in data integration and how CIOs can overcome them

CIO Business Intelligence

The growing volume of data is a concern, as 20% of enterprises surveyed by IDG are drawing from 1000 or more sources to feed their analytics systems. Data integration needs an overhaul, which can only be achieved by considering the following gaps. Heterogeneous sources produce data sets of different formats and structures.

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Navigating the Data Mesh Paradigm: Opportunities, Challenges, and the Path Forward

Data Virtualization

Reading Time: 5 minutes The data landscape has become more complex, as organizations recognize the need to leverage data and analytics for a competitive edge. Companies are collecting traditional structured data as well as text, machine-generated data, semistructured data, geospatial data, and more.

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Navigating the Data Mesh Paradigm: Opportunities, Challenges, and the Path Forward

Data Virtualization

Reading Time: 5 minutes The data landscape has become more complex, as organizations recognize the need to leverage data and analytics for a competitive edge. Companies are collecting traditional structured data as well as text, machine-generated data, semistructured data, geospatial data, and more.

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Data governance in the age of generative AI

AWS Big Data

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. Data discoverability Unlike structured data, which is managed in well-defined rows and columns, unstructured data is stored as objects.

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From Blob Storage to SQL Database Using Azure Data Factory

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Azure data factory (ADF) is a cloud-based ETL (Extract, Transform, Load) tool and data integration service which allows you to create a data-driven workflow. In this article, I’ll show […].

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

DataKitchen

Feeding this unstructured data into LLMs without proper contextualization risks creating noise instead of clarity. Data Connectivity: Mergers and acquisitions complicate data integration, making it challenging for LLMs to consolidate data across disparate systems.

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Why You’re Not Ready for Knowledge Graphs!

Ontotext

Data integration If your organization’s idea of data integration is printing out multiple reports and manually cross-referencing them, you might not be ready for a knowledge graph. Data quality Knowledge graphs thrive on clean, well-structured data, and they rely on accurate relationships and meaningful connections.