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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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Data Integration Patterns in Knowledge Graph Building with GraphDB

Ontotext

To deal with this issue, GraphDB implements a smart Graph Replace optimization that helps you calculate the internal data and only shows you the newly added and removed statements. The second approach is to use some Data Integration Platform. Try the data integration pattern that’s best for you!

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

DataKitchen

The Role of Data Journeys in RAG The underlying data must be meticulously managed throughout its journey for RAG to function optimally. This is where DataOps comes into play, offering a framework for managing Data Journeys with precision and agility.

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The Enduring Significance of Data Modeling in the Modern Data-Driven Enterprise

erwin

Let’s explore the continued relevance of data modeling and its journey through history, challenges faced, adaptations made, and its pivotal role in the new age of data platforms, AI, and democratized data access. Embracing the future In the dynamic world of data, data modeling remains an indispensable tool.

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What is a customer data platform? A unified customer database

CIO Business Intelligence

A customer data platform (CDP) is a prepackaged, unified customer database that pulls data from multiple sources to create customer profiles of structured data available to other marketing systems. By applying machine learning to the data, you can better predict customer behavior. Types of CDPs. Segment CDP.

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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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How GamesKraft uses Amazon Redshift data sharing to support growing analytics workloads

AWS Big Data

Amazon Redshift enables you to use SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and machine learning (ML) to deliver the best price-performance at scale.