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Modern Data Architecture for Telecommunications

Cloudera

Data has continued to grow both in scale and in importance through this period, and today telecommunications companies are increasingly seeing data architecture as an independent organizational challenge, not merely an item on an IT checklist. Why telco should consider modern data architecture. The challenges.

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Empowering data-driven excellence: How the Bluestone Data Platform embraced data mesh for success

AWS Big Data

Four-layered data lake and data warehouse architecture – The architecture comprises four layers, including the analytical layer, which houses purpose-built facts and dimension datasets that are hosted in Amazon Redshift. This enables data-driven decision-making across the organization.

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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. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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Empowering data mesh: The tools to deliver BI excellence

erwin

The data mesh framework In the dynamic landscape of data management, the search for agility, scalability, and efficiency has led organizations to explore new, innovative approaches. One such innovation gaining traction is the data mesh framework. This empowers individual teams to own and manage their data.

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The essential check list for effective data democratization

CIO Business Intelligence

In particular, companies that were leaders at using data and analytics had three times higher improvement in revenues, were nearly three times more likely to report shorter times to market for new products and services, and were over twice as likely to report improvement in customer satisfaction, profits, and operational efficiency.

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The power of remote engine execution for ETL/ELT data pipelines

IBM Big Data Hub

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models.

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Data Governance and Strategy for the Global Enterprise

Cloudera

Adam Wood, director of data governance and data quality at a financial services institution (FSI). Sam Charrington, founder and host of the TWIML AI Podcast. For example, the concept of nationalism in data regulation means that countries might craft a different set of rules based on where data originates.