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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. Upload the data file to the S3 bucket created by the CloudFormation stack.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights. Choose Next to create your stack.

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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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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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Query your Iceberg tables in data lake using Amazon Redshift (Preview)

AWS Big Data

Amazon Redshift enables you to directly access data stored in Amazon Simple Storage Service (Amazon S3) using SQL queries and join data across your data warehouse and data lake. With Amazon Redshift, you can query the data in your S3 data lake using a central AWS Glue metastore from your Redshift data warehouse.

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Is Data Virtualization the Secret Behind Operationalizing Data Lakes?

Data Virtualization

Reading Time: 4 minutes The amount of expanding volume and variety of data originating from various sources are a massive challenge for businesses. In attempts to overcome their big data challenges, organizations are exploring data lakes as repositories where huge volumes and varieties of.

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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.