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Octopai Acquisition Enhances Metadata Management to Trust Data Across Entire Data Estate

Cloudera

Founded in 2016, Octopai offers automated solutions for data lineage, data discovery, data catalog, mapping, and impact analysis across complex data environments. It allows users to mitigate risks, increase efficiency, and make data strategy more actionable than ever before.

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5 Ways Data Modeling Is Critical to Data Governance

erwin

Then there’s unstructured data with no contextual framework to govern data flows across the enterprise not to mention time-consuming manual data preparation and limited views of data lineage. So here’s why data modeling is so critical to data governance.

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Doing Cloud Migration and Data Governance Right the First Time

erwin

That means your cloud data assets must be available for use by the right people for the right purposes to maximize their security, quality and value. Why You Need Cloud Data Governance. Regulatory compliance is also a major driver of data governance (e.g., GDPR, CCPA, HIPAA, SOX, PIC DSS).

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Integrating Data Governance and Enterprise Architecture

erwin

Why should you integrate data governance (DG) and enterprise architecture (EA)? Data governance provides time-sensitive, current-state architecture information with a high level of quality. Data governance provides time-sensitive, current-state architecture information with a high level of quality.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Eliminate centralized bottlenecks and complex data pipelines. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog.

IoT 101
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What is data governance? Best practices for managing data assets

CIO Business Intelligence

Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

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

AWS Big Data

Despite their advantages, traditional data lake architectures often grapple with challenges such as understanding deviations from the most optimal state of the table over time, identifying issues in data pipelines, and monitoring a large number of tables. It is essential for optimizing read and write performance.

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