Remove Data Architecture Remove Data Science Remove Data Transformation
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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Two use cases illustrate how this can be applied for business intelligence (BI) and data science applications, using AWS services such as Amazon Redshift and Amazon SageMaker.

IoT 111
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Automate discovery of data relationships using ML and Amazon Neptune graph technology

AWS Big Data

Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern data architecture is critical in order to become a data-driven organization.

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

AWS Big Data

The downstream consumers consist of business intelligence (BI) tools, with multiple data science and data analytics teams having their own WLM queues with appropriate priority values. Consequently, there was a fivefold rise in data integrations and a fivefold increase in ad hoc queries submitted to the Redshift cluster.

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Lay the groundwork now for advanced analytics and AI

CIO Business Intelligence

At Paytronix, which manages customer loyalty, online ordering, and other systems for its customers, director of data science ­Jesse Marshall wanted to reduce the custom coding of data transformations—the conversion, cleaning, and structuring of data into a form usable for analytics and reports.

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Connecting the Data Lifecycle

Cloudera

Data transforms businesses. That’s where the data lifecycle comes into play. Managing data and its flow, from the edge to the cloud, is one of the most important tasks in the process of gaining data intelligence. . The company needed a modern data architecture to manage the growing traffic effectively. .

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The Chief Marketing Officer and the CDO – A Modern Fable

Peter James Thomas

It may well be that one thing that a CDO needs to get going is a data transformation programme. This may purely be focused on cultural aspects of how an organisation records, shares and otherwise uses data. It may be to build a new (or a first) Data Architecture. It may be to introduce or expand Data Governance.

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Choosing A Graph Data Model to Best Serve Your Use Case

Ontotext

It accelerates data projects with data quality and lineage and contextualizes through ontologies , taxonomies, and vocabularies, making integrations easier. RDF is used extensively for data publishing and data interchange and is based on W3C and other industry standards. LPGs are rudimentary knowledge graphs.