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

AWS Big Data

Cloudinary is a cloud-based media management platform that provides a comprehensive set of tools and services for managing, optimizing, and delivering images, videos, and other media assets on websites and mobile applications.

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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. This property is set to true by default. availability.

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Simplify operational data processing in data lakes using AWS Glue and Apache Hudi

AWS Big Data

A modern data architecture is an evolutionary architecture pattern designed to integrate a data lake, data warehouse, and purpose-built stores with a unified governance model. Moreover, the framework should consume compute resources as optimally as possible per the size of the operational tables.

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Introducing Apache Hudi support with AWS Glue crawlers

AWS Big Data

Apache Hudi is an open table format that brings database and data warehouse capabilities to data lakes. Apache Hudi helps data engineers manage complex challenges, such as managing continuously evolving datasets with transactions while maintaining query performance. For CoW tables, queries see the latest data committed.

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Materialized Views in Hive for Iceberg Table Format

Cloudera

Queries containing joins, filters, projections, group-by, or aggregations without group-by can be transparently rewritten by the Hive optimizer to use one or more eligible materialized views. Note that the materialized view definition contains the ‘stored by iceberg’ clause. Materialized views can be partitioned on one or more columns.

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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

It enables data engineers, data scientists, and analytics engineers to define the business logic with SQL select statements and eliminates the need to write boilerplate data manipulation language (DML) and data definition language (DDL) expressions.

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Build a multi-Region and highly resilient modern data architecture using AWS Glue and AWS Lake Formation

AWS Big Data

This solution only replicates metadata in the Data Catalog, not the actual underlying data. To have a redundant data lake using Lake Formation and AWS Glue in an additional Region, we recommend replicating the Amazon S3-based storage using S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication process.