Remove Data Architecture Remove Data Transformation Remove Machine Learning
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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 100
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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

We also examine how centralized, hybrid and decentralized data architectures support scalable, trustworthy ecosystems. As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprises core has never been more significant.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

Within seconds of transactional data being written into Amazon Aurora (a fully managed modern relational database service offering performance and high availability at scale), the data is seamlessly made available in Amazon Redshift for analytics and machine learning.

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How Open Universities Australia modernized their data platform and significantly reduced their ETL costs with AWS Cloud Development Kit and AWS Step Functions

AWS Big Data

AWS Step Functions With AWS Step Functions, you can create workflows, also called State machines, to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning pipelines. The following Diagram 2 shows this workflow.

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Supercharge Your Data Lakehouse with Apache Iceberg in Cloudera Data Platform

Cloudera

Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP) — including Cloudera Data Warehousing ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). We can handle any data anywhere, in hybrid and multi-cloud.

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Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow

AWS Big Data

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.

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BMW Cloud Efficiency Analytics powered by Amazon QuickSight and Amazon Athena

AWS Big Data

They can use their own toolsets or rely on provided blueprints to ingest the data from source systems. Once released, consumers use datasets from different providers for analysis, machine learning (ML) workloads, and visualization. The difference lies in when and where data transformation takes place.