Remove Data Analytics Remove Data Integration Remove Data Transformation Remove Snapshot
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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Specifically, the system uses Amazon SageMaker Processing jobs to process the data stored in the data lake, employing the AWS SDK for Pandas (previously known as AWS Wrangler) for various data transformation operations, including cleaning, normalization, and feature engineering.

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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

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“You Complete Me,” said Data Lineage to DataOps Observability.

DataKitchen

To capture a more complete picture of the data’s journey, it is important to have a DataOps Observability system in place. Data lineage is static and often lags by weeks or months. Data lineage is often considered static because it is typically based on snapshots of data and metadata taken at a specific time.

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