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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.

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

AWS Big Data

Open table formats are emerging in the rapidly evolving domain of big data management, fundamentally altering the landscape of data storage and analysis. By providing a standardized framework for data representation, open table formats break down data silos, enhance data quality, and accelerate analytics at scale.

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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. and later supports the Apache Iceberg framework for data lakes. The snapshot points to the manifest list. AWS Glue 3.0

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

AWS Big Data

In this blog post, we dive into different data aspects and how Cloudinary breaks the two concerns of vendor locking and cost efficient data analytics by using Apache Iceberg, Amazon Simple Storage Service (Amazon S3 ), Amazon Athena , Amazon EMR , and AWS Glue. SparkActions.get().expireSnapshots(iceTable).expireOlderThan(TimeUnit.DAYS.toMillis(7)).execute()

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Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes

AWS Big Data

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. availability. Note the configuration parameters s3.write.tags.write-tag-name write.tags.write-tag-name and s3.delete.tags.delete-tag-name

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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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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.