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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

This authority extends across realms such as business intelligence, data engineering, and machine learning thus limiting the tools and capabilities that can be used. The landscape of data technology is swiftly advancing, driven frequently by projects led by the open source community in general and the Apache foundation specifically.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

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

AWS Big Data

The Salesforce Trust Intelligence Platform (TIP) log platform team is responsible for data pipeline and data lake infrastructure, providing log ingestion, normalization, persistence, search, and detection capability to ensure Salesforce is safe from threat actors. Headquartered in San Francisco, Salesforce, Inc.

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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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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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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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Enable business users to analyze large datasets in your data lake with Amazon QuickSight

AWS Big Data

Events and many other security data types are stored in Imperva’s Threat Research Multi-Region data lake. Imperva harnesses data to improve their business outcomes. As part of their solution, they are using Amazon QuickSight to unlock insights from their data.