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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. However, commits can still fail if the latest metadata is updated after the base metadata version is established.

Snapshot 138
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What is a data architect? Skills, salaries, and how to become a data framework master

CIO Business Intelligence

Data architecture is a complex and varied field and different organizations and industries have unique needs when it comes to their data architects. Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes.

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

AWS Big Data

Over the years, data lakes on Amazon Simple Storage Service (Amazon S3) have become the default repository for enterprise data and are a common choice for a large set of users who query data for a variety of analytics and machine leaning use cases. Analytics use cases on data lakes are always evolving.

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Top analytics announcements of AWS re:Invent 2024

AWS Big Data

Amazon SageMaker Lakehouse provides an open data architecture that reduces data silos and unifies data across Amazon Simple Storage Service (Amazon S3) data lakes, Redshift data warehouses, and third-party and federated data sources. connection testing, metadata retrieval, and data preview.

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

AWS Big Data

When evolving such a partition definition, the data in the table prior to the change is unaffected, as is its metadata. Only data that is written to the table after the evolution is partitioned with the new definition, and the metadata for this new set of data is kept separately. SparkActions.get().expireSnapshots(iceTable).expireOlderThan(TimeUnit.DAYS.toMillis(7)).execute()

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How Cloudera Data Flow Enables Successful Data Mesh Architectures

Cloudera

Within the context of a data mesh architecture, I will present industry settings / use cases where the particular architecture is relevant and highlight the business value that it delivers against business and technology areas. Components of a Data Mesh. How CDF enables successful Data Mesh Architectures.

Metadata 124
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The Future Is Hybrid Data, Embrace It

Cloudera

The cause is hybrid data – the massive amounts of data created everywhere businesses operate – in clouds, on-prem, and at the edge. Only a fraction of data created is actually stored and managed, with analysts estimating it to be between 4 – 6 ZB in 2020.

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