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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

To create and manage the data products, smava uses Amazon Redshift , a cloud data warehouse. In this post, we show how smava optimized their data platform by using Amazon Redshift Serverless and Amazon Redshift data sharing to overcome right-sizing challenges for unpredictable workloads and further improve price-performance.

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BMW Cloud Efficiency Analytics powered by Amazon QuickSight and Amazon Athena

AWS Big Data

BMW Group uses 4,500 AWS Cloud accounts across the entire organization but is faced with the challenge of reducing unnecessary costs, optimizing spend, and having a central place to monitor costs. The ultimate goal is to raise awareness of cloud efficiency and optimize cloud utilization in a cost-effective and sustainable manner.

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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. This ensures that the data is suitable for training purposes. The following diagram illustrates the solution architecture.

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Lay the groundwork now for advanced analytics and AI

CIO Business Intelligence

It also used device data to develop Lenovo Device Intelligence, which uses AI-driven predictive analytics to help customers understand and proactively prevent and solve potential IT issues. Lenovo Device Intelligence can also help to optimize IT support costs, reduce employee downtime, and improve the user experience, the company says.

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Deep dive into the AWS ProServe Hadoop Migration Delivery Kit TCO tool

AWS Big Data

Additionally, a TCO calculator generates the TCO estimation of an optimized EMR cluster for facilitating the migration. After you complete the checklist, you’ll have a better understanding of how to design the future architecture. For the compute-heavy workloads such as MapReduce or Hive-on-MR jobs, use CPU-optimized instances.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 1

AWS Big Data

Data Vault 2.0 allows for the following: Agile data warehouse development Parallel data ingestion A scalable approach to handle multiple data sources even on the same entity A high level of automation Historization Full lineage support However, Data Vault 2.0

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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. Increasingly, organizations are using both.