This article explores how HPE Aruba has successfully automated their supply chain management pipeline by transitioning to a modern data architecture on AWS. The team, consisting of experts such as Jason Ford, Emily Wright, and Raj Patel, undertook the task of re-architecting and deploying their data solution to enhance both cost efficiency and performance, demonstrating the power of AWS technologies.
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Migrating Delta Tables from Azure Data Lake Storage to Amazon S3 with AWS Glue
Written by Sarah Johnson, Mark Lee, and Priya Sharma, this entry, published on September 10, 2024, delves into the increasing trend of organizations utilizing multi-cloud strategies to manage their production workloads. It highlights the requests from customers who initially built their data lakes on Microsoft Azure and now wish to extend access to AWS services.
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Evaluating Amazon Redshift Data Sharing Architecture
In a post by Kevin Brown, Laura Green, and Dave Smith, dated September 10, 2024, the authors guide readers through testing workload isolation architecture using Amazon Redshift Data Sharing and the Test Drive utility. This post showcases how SQL can be employed for advanced price performance analysis, comparing various workloads across different Redshift cluster configurations.
Publishing Real-Time Financial Data Feeds with Amazon MSK and Apache Flink
Authored by Andrew King, Rhea Morgan, and Tom White, this entry discusses how to publish and enrich real-time data feeds on AWS. By leveraging Amazon Managed Streaming for Kafka (Amazon MSK) and the Amazon Managed Service for Apache Flink, organizations can apply this architecture to various use cases in the capital markets industry.
Amazon Redshift Data Ingestion Options
In this foundational post, experts Alice Brown and Victor Chen outline the various options Amazon Redshift offers for ingesting data from a wide range of sources. Whether the data is in operational databases, data lakes, or other AWS services, Redshift provides multiple ingestion methods tailored to meet user needs.
Integrating Sparse and Dense Vectors for Enhanced Knowledge Retrieval
In a technical piece by Jason White, Mia Zhang, and Leo Kim, published on September 5, 2024, the authors introduce a new approach to knowledge retrieval using sparse vector retrieval instead of the traditional BM25 algorithm. The post walks through integrating sparse and dense vectors using Amazon OpenSearch Service, supported by experiments on public datasets.
Provisioning Amazon Redshift Serverless with the AWS CDK
This post, penned by Charlie Evans and Naomi Green, illustrates how to use the AWS Cloud Development Kit (CDK) along with the Data Solutions Framework (DSF) to establish a multi-data warehouse platform based on Amazon Redshift Serverless.
Accelerating Data Integration with Salesforce and AWS
In another insightful article, experts David White, Sofia Kim, and Arjun Rao discuss the new Salesforce connector for AWS Glue, which allows users to quickly integrate their CRM data into AWS. This post provides a detailed walkthrough of building a modern ETL pipeline with AWS Glue ETL scripts.
Integrating Tableau and Microsoft Entra ID with Amazon Redshift
Finally, this guide by Mia Thompson, Oliver Reed, and Sarah Patel offers a step-by-step approach to integrate IAM Identity Center with Microsoft Entra ID and configure Amazon Redshift as an AWS managed application, enabling SSO within Tableau.
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