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Is Your Team in Denial of Data Quality? Here’s How to Tell

DataKitchen

Is Your Team in Denial of Data Quality? Here’s How to Tell In many organizations, data quality problems fester in the shadowsignored, rationalized, or swept aside with confident-sounding statements that mask a deeper dysfunction. That’s not data quality; that’s data folklore.

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Manage concurrent write conflicts in Apache Iceberg on the AWS Glue Data Catalog

AWS Big Data

Concurrent UPDATE/DELETE on overlapping partitions When multiple processes attempt to modify the same partition simultaneously, data conflicts can arise. For example, imagine a data quality process updating customer records with corrected addresses while another process is deleting outdated customer records.

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

AWS Big Data

Today, we are pleased to announce that Amazon DataZone is now able to present data quality information for data assets. Other organizations monitor the quality of their data through third-party solutions. Additionally, Amazon DataZone now offers APIs for importing data quality scores from external systems.

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Use open table format libraries on AWS Glue 5.0 for Apache Spark

AWS Big Data

These formats, exemplified by Apache Iceberg, Apache Hudi, and Delta Lake, addresses persistent challenges in traditional data lake structures by offering an advanced combination of flexibility, performance, and governance capabilities. Branching Branches are independent lineage of snapshot history that point to the head of each lineage.

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

AWS Big Data

This ensures that each change is tracked and reversible, enhancing data governance and auditability. History and versioning : Iceberg’s versioning feature captures every change in table metadata as immutable snapshots, facilitating data integrity, historical views, and rollbacks.

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Implement historical record lookup and Slowly Changing Dimensions Type-2 using Apache Iceberg

AWS Big Data

As organizations process vast amounts of data, maintaining an accurate historical record is crucial. History management in data systems is fundamental for compliance, business intelligence, data quality, and time-based analysis. You can obtain the table snapshots by querying for db.table.snapshots.

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Data Observability and Monitoring with DataOps

DataKitchen

Make sure the data and the artifacts that you create from data are correct before your customer sees them. It’s not about data quality . In governance, people sometimes perform manual data quality assessments. It’s not only about the data. Data Quality. Location Balance Tests.

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