Remove Big Data Remove Data Architecture Remove Data Integration
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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

While traditional extract, transform, and load (ETL) processes have long been a staple of data integration due to its flexibility, for common use cases such as replication and ingestion, they often prove time-consuming, complex, and less adaptable to the fast-changing demands of modern data architectures.

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Amazon Web Services named a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools

AWS Big Data

Amazon Web Services (AWS) has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools. This recognition, we feel, reflects our ongoing commitment to innovation and excellence in data integration, demonstrating our continued progress in providing comprehensive data management solutions.

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Big Data Ingestion: Parameters, Challenges, and Best Practices

datapine

Operations data: Data generated from a set of operations such as orders, online transactions, competitor analytics, sales data, point of sales data, pricing data, etc. The gigantic evolution of structured, unstructured, and semi-structured data is referred to as Big data. Big Data Ingestion.

Big Data 100
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How HPE Aruba Supply Chain optimized cost and performance by migrating to an AWS modern data architecture

AWS Big Data

This post describes how HPE Aruba automated their Supply Chain management pipeline, and re-architected and deployed their data solution by adopting a modern data architecture on AWS. The new solution has helped Aruba integrate data from multiple sources, along with optimizing their cost, performance, and scalability.

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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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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

SageMaker brings together widely adopted AWS ML and analytics capabilities—virtually all of the components you need for data exploration, preparation, and integration; petabyte-scale big data processing; fast SQL analytics; model development and training; governance; and generative AI development.

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

AWS Big Data

Conclusion In this post, we walked you through the process of using Amazon AppFlow to integrate data from Google Ads and Google Sheets. We demonstrated how the complexities of data integration are minimized so you can focus on deriving actionable insights from your data.