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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

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Monitor data pipelines in a serverless data lake

AWS Big Data

The combination of a data lake in a serverless paradigm brings significant cost and performance benefits. By monitoring application logs, you can gain insights into job execution, troubleshoot issues promptly to ensure the overall health and reliability of data pipelines.

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Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

Data analytics on operational data at near-real time is becoming a common need. Due to the exponential growth of data volume, it has become common practice to replace read replicas with data lakes to have better scalability and performance. For more information, see Changing the default settings for your data lake.

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

AWS Big Data

Imperva Cloud WAF protects hundreds of thousands of websites and blocks billions of security events every day. 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.

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Modernize your data observability with Amazon OpenSearch Service zero-ETL integration with Amazon S3

AWS Big Data

The integration is new way for customers to query operational logs in Amazon S3 and Amazon S3-based data lakes without needing to switch between tools to analyze operational data. Amazon S3 is an object storage service offering industry-leading scalability, data availability, security, and performance.

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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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Visualize Confluent data in Amazon QuickSight using Amazon Athena

AWS Big Data

Businesses are using real-time data streams to gain insights into their company’s performance and make informed, data-driven decisions faster. As real-time data has become essential for businesses, a growing number of companies are adapting their data strategy to focus on data in motion.