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Use AWS Glue ETL to perform merge, partition evolution, and schema evolution on Apache Iceberg

AWS Big Data

As enterprises collect increasing amounts of data from various sources, the structure and organization of that data often need to change over time to meet evolving analytical needs. Schema evolution enables adding, deleting, renaming, or modifying columns without needing to rewrite existing data.

Snapshot 108
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Speed up queries with the cost-based optimizer in Amazon Athena

AWS Big Data

Athena provides a simplified, flexible way to analyze petabytes of data where it lives. You can analyze data or build applications from an Amazon Simple Storage Service (Amazon S3) data lake and 30 data sources, including on-premises data sources or other cloud systems using SQL or Python.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. in lieu of simply landing in a data lake.

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Generate security insights from Amazon Security Lake data using Amazon OpenSearch Ingestion

AWS Big Data

Optionally, specify the Amazon S3 storage class for the data in Amazon Security Lake. For more information, refer to Lifecycle management in Security Lake. Review the details and create the data lake. Choose Next. For sts_role_arn , enter the ARN of pipeline-role. Set region as us-east-1.

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Themes and Conferences per Pacoid, Episode 12

Domino Data Lab

I mention this here because there was a lot of overlap between current industry data governance needs and what the scientific community is working toward for scholarly infrastructure. The gist is, leveraging metadata about research datasets, projects, publications, etc., 2018 – Global reckoning about data governance, aka “Oops!