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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

In modern data architectures, Apache Iceberg has emerged as a popular table format for data lakes, offering key features including ACID transactions and concurrent write support. The Data Catalog provides the functionality as the Iceberg catalog. Determine the changes in transaction, and write new data files.

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Use open table format libraries on AWS Glue 5.0 for Apache Spark

AWS Big Data

These formats, exemplified by Apache Iceberg, Apache Hudi, and Delta Lake, addresses persistent challenges in traditional data lake structures by offering an advanced combination of flexibility, performance, and governance capabilities. These are useful for flexible data lifecycle management.

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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.

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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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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

In the era of big data, data lakes have emerged as a cornerstone for storing vast amounts of raw data in its native format. They support structured, semi-structured, and unstructured data, offering a flexible and scalable environment for data ingestion from multiple sources.

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Implement historical record lookup and Slowly Changing Dimensions Type-2 using Apache Iceberg

AWS Big Data

As organizations process vast amounts of data, maintaining an accurate historical record is crucial. History management in data systems is fundamental for compliance, business intelligence, data quality, and time-based analysis. You can obtain the table snapshots by querying for db.table.snapshots.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x