Remove Metadata Remove Metrics Remove Snapshot Remove Testing
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Use AWS Glue ETL to perform merge, partition evolution, and schema evolution on Apache Iceberg

AWS Big Data

Apache Iceberg manages these schema changes in a backward-compatible way through its innovative metadata table evolution architecture. With Lake Formation, you can manage fine-grained access control for your data lake data on Amazon S3 and its metadata in the Data Catalog. Iceberg maintains the table state in metadata files.

Snapshot 108
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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

Many organizations already use AWS Glue Data Quality to define and enforce data quality rules on their data, validate data against predefined rules , track data quality metrics, and monitor data quality over time using artificial intelligence (AI). We use this data source to import metadata information related to our datasets.

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

AWS Big Data

Building a starter version of anything can often be straightforward, but building something with enterprise-grade scale, security, resiliency, and performance typically requires knowledge and adherence to battle-tested best practices, and using the right tools and features in the right scenario.

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Now Available: Cloudera Data Science Workbench Release 1.4

Cloudera

With Experiments, data scientists can run a batch job that will: create a snapshot of model code, dependencies, and configuration parameters necessary to train the model. track model metrics, performance, and any model artifacts the user specifies. save the built model container, along with metadata like who built or deployed it.

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Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

Near-real-time streaming analytics captures the value of operational data and metrics to provide new insights to create business opportunities. These metrics help agents improve their call handle time and also reallocate agents across organizations to handle pending calls in the queue. We use two datasets in this post.

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

AWS Big Data

Finally, by testing the framework, we summarize how it meets the aforementioned requirements. The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker. It also updates technical metadata in the AWS Glue Data Catalog.

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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

To further optimize and improve the developer velocity for our data consumers, we added Amazon DynamoDB as a metadata store for different data sources landing in the data lake. We used the same AWS Glue jobs to further transform and load the data into the required S3 bucket and a portion of extracted metadata into DynamoDB.