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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

With the growing emphasis on data, organizations are constantly seeking more efficient and agile ways to integrate their data, especially from a wide variety of applications. We take care of the ETL for you by automating the creation and management of data replication. Glue ETL offers customer-managed data ingestion.

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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

In this blog post, we’ll discuss how the metadata layer of Apache Iceberg can be used to make data lakes more efficient. You will learn about an open-source solution that can collect important metrics from the Iceberg metadata layer. This ensures that each change is tracked and reversible, enhancing data governance and auditability.

Metadata 124
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iostudio delivers key metrics to public sector recruiters with Amazon QuickSight

AWS Big Data

Our previous solution offered visualization of key metrics, but point-in-time snapshots produced only in PDF format. In this post, we discuss how we built a solution using QuickSight that delivers real-time visibility of key metrics to public sector recruiters.

Metrics 98
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How Cloudinary transformed their petabyte scale streaming data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Since Apache Iceberg is well supported by AWS data services and Cloudinary was already using Spark on Amazon EMR, they could integrate writing to Data Catalog and start an additional Spark cluster to handle data maintenance and compaction. For example, for certain queries, Athena runtime was 2x–4x faster than Snowflake.

Data Lake 125
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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Using Apache Iceberg’s compaction results in significant performance improvements, especially for large tables, making a noticeable difference in query performance between compacted and uncompacted data. These files are then reconciled with the remaining data during read time.

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Data Observability and Monitoring with DataOps

DataKitchen

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. That’s a fair point, and it places emphasis on what is most important – what best practices should data teams employ to apply observability to data analytics. Location Balance Tests.

Testing 214
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Patterns for updating Amazon OpenSearch Service index settings and mappings

AWS Big Data

Check the disk.avail metric for hot storage tier nodes to validate your available disk space. Use the reindex API operation The _reindex operation snapshots the index at the beginning of its run and performs processing on a snapshot to minimize impact on the source index. v The following screenshot shows the output.

Snapshot 106