Remove Data Lake Remove Data Quality Remove Strategy
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Build Write-Audit-Publish pattern with Apache Iceberg branching and AWS Glue Data Quality

AWS Big Data

Equally crucial is the ability to segregate and audit problematic data, not just for maintaining data integrity, but also for regulatory compliance, error analysis, and potential data recovery. One of its key features is the ability to manage data using branches. Each strategy offers unique advantages and considerations.

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Visualize data quality scores and metrics generated by AWS Glue Data Quality

AWS Big Data

AWS Glue Data Quality allows you to measure and monitor the quality of data in your data repositories. It’s important for business users to be able to see quality scores and metrics to make confident business decisions and debug data quality issues. An AWS Glue crawler crawls the results.

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

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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Steps taken to build Sevita’s first enterprise data platform

CIO Business Intelligence

But because of the infrastructure, employees spent hours on manual data analysis and spreadsheet jockeying. We had plenty of reporting, but very little data insight, and no real semblance of a data strategy. Second, the manual spreadsheet work resulted in significant manual data entry.

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

However, they do contain effective data management, organization, and integrity capabilities. As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. On the other hand, they don’t support transactions or enforce data quality.

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Data Architecture and Strategy in the AI Era

Cloudera

But, even with the backdrop of an AI-dominated future, many organizations still find themselves struggling with everything from managing data volumes and complexity to security concerns to rapidly proliferating data silos and governance challenges. The benefits are clear, and there’s plenty of potential that comes with AI adoption.