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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Secure cloud fabric: Enhancing data management and AI development for the federal government

CIO Business Intelligence

However, establishing and maintaining such connections can be a complex and costly process, especially as the volume of data being transmitted continues to grow. Similarly, connecting to data lakes presents both privacy and security concerns.

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Dairyland powers up for a generative AI edge

CIO Business Intelligence

Previously head of cybersecurity at Ingersoll-Rand, Melby started developing neural networks and machine learning models more than a decade ago. I was literally just waiting for commercial availability [of LLMs] but [services] like Azure Machine Learning made it so you could easily apply it to your data.

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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

For the past 5 years, BMS has used a custom framework called Enterprise Data Lake Services (EDLS) to create ETL jobs for business users. BMS’s EDLS platform hosts over 5,000 jobs and is growing at 15% YoY (year over year). About the authors Sivaprasad Mahamkali is a Senior Streaming Data Engineer at AWS Professional Services.

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Access Amazon Athena in your applications using the WebSocket API

AWS Big Data

Many organizations are building data lakes to store and analyze large volumes of structured, semi-structured, and unstructured data. In addition, many teams are moving towards a data mesh architecture, which requires them to expose their data sets as easily consumable data products.

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10 Things AWS Can Do for Your SaaS Company

Smart Data Collective

Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms.