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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Interact with Apache Iceberg tables using Amazon Athena and cross account fine-grained permissions using AWS Lake Formation

AWS Big Data

The Iceberg table keeps track of the snapshots. consumer_iceberg$snapshots" limit 10; We can observe that we have generated multiple snapshots. Use time travel to find the table snapshot. Time travel We have now changed the Iceberg table multiple times. Query the system table: SELECT * FROM "lf-demo-db"."consumer_iceberg$snapshots"

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

AWS Big Data

By analyzing the historical report snapshot, you can identify areas for improvement, implement changes, and measure the effectiveness of those changes. Furthermore, we delved into the seamless integration between Amazon DataZone and AWS Glue Data Quality. To learn more about Amazon DataZone, refer to the Amazon DataZone User Guide.

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Solving the Pain Points of Big Data Management

Cloudera

Today, much of that speed and efficiency relies on insights driven by big data. Yet big data management often serves as a stumbling block, because many businesses continue to struggle with how to best capture and analyze their data. Unorganized data presents another roadblock.

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Synchronize your Salesforce and Snowflake data to speed up your time to insight with Amazon AppFlow

AWS Big Data

With scheduled flows, you can choose either full or incremental data transfer: With full transfer, Amazon AppFlow transfers a snapshot of all records at the time of the flow run from the source to the destination. Amit Shah is a cloud based modern data architecture expert and currently leading AWS Data Analytics practice in Atos.

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Five actionable steps to GDPR compliance (Right to be forgotten) with Amazon Redshift

AWS Big Data

By creating visual representations of data flows, organizations can gain a clear understanding of the lifecycle of personal data and identify potential vulnerabilities or compliance gaps. Note that putting a comprehensive data strategy in place is not in scope for this post. However, this is beyond the scope of this post.

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How Amazon optimized its high-volume financial reconciliation process with Amazon EMR for higher scalability and performance

AWS Big Data

Amazon EMR stands as a dynamic force in the cloud, delivering unmatched capabilities for organizations seeking robust big data solutions. Its seamless integration, powerful features, and adaptability make it an indispensable tool for navigating the complexities of data analytics and ML on AWS.