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Multicloud data lake analytics with Amazon Athena

AWS Big Data

Many organizations operate data lakes spanning multiple cloud data stores. In these cases, you may want an integrated query layer to seamlessly run analytical queries across these diverse cloud stores and streamline your data analytics processes.

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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

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Using AWS AppSync and AWS Lake Formation to access a secure data lake through a GraphQL API

AWS Big Data

Data lakes have been gaining popularity for storing vast amounts of data from diverse sources in a scalable and cost-effective way. As the number of data consumers grows, data lake administrators often need to implement fine-grained access controls for different user profiles.

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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. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. Apache Iceberg integration is supported by AWS analytics services including Amazon EMR , Amazon Athena , and AWS Glue. AWS Glue 3.0

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Announcing the AWS Well-Architected Data Analytics Lens

AWS Big Data

We are delighted to announce the release of the Data Analytics Lens. Using the Lens in the Tool’s Lens Catalog, you can directly assess your Analytics workload in the console, and produce a set of actionable results for customized improvement plans recommended by the Tool. What’s new in the Data Analytics Lens?

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How Salesforce optimized their detection and response platform using AWS managed services

AWS Big Data

This is a guest blog post co-authored with Atul Khare and Bhupender Panwar from Salesforce. The Normalized Parquet Logs are stored in an Amazon Simple Storage Service (Amazon S3) data lake and cataloged into Hive Metastore (HMS) on an Amazon Relational Database Service (Amazon RDS) instance based on S3 event notifications.