Remove Data Architecture Remove Data Integration Remove Experimentation
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Companies to shift AI goals in 2025 — with setbacks inevitable, Forrester predicts

CIO Business Intelligence

As they look to operationalize lessons learned through experimentation, they will deliver short-term wins and successfully play the gen AI — and other emerging tech — long game,” Leaver said. They predicted more mature firms will seek help from AI service providers and systems integrators.

ROI 127
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Need for a data mesh architecture Because entities in the EUROGATE group generate vast amounts of data from various sourcesacross departments, locations, and technologiesthe traditional centralized data architecture struggles to keep up with the demands for real-time insights, agility, and scalability.

IoT 111
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Load data incrementally from transactional data lakes to data warehouses

AWS Big Data

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure. Various data stores are supported in AWS Glue; for example, AWS Glue 4.0

Data Lake 137
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Four starting points to transform your organization into a data-driven enterprise

IBM Big Data Hub

This capability will provide data users with visibility into origin, transformations, and destination of data as it is used to build products. The result is more useful data for decision-making, less hassle and better compliance. Data integration. Data science and MLOps. AI is no longer experimental.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. These robust capabilities ensure that data within the data lake remains accurate, consistent, and reliable.

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What Stands Between IT and Business Success? Data Complexity

CIO Business Intelligence

A real-time data technology stack has to shrink this innovation gap for the business. . Analysts and data scientists need flexibility when working with data; experimentation fuels the development of analytics and machine learning models. Often, enterprise data ecosystems are built with a mindset that’s too narrow.

IT 135