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Data Trustability: The Bridge Between Data Quality and Data Observability

Dataversity

So, what can you do to ensure your data is up to par and […]. The post Data Trustability: The Bridge Between Data Quality and Data Observability appeared first on DATAVERSITY. You might not even make it out of the starting gate.

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Highlights from the Data Engineering Summit Now Available On Demand

ODSC - Open Data Science

It also addresses the strategies and best practices for implementing a data mesh. Applying Engineering Best Practices in Data Lakes Architectures Einat Orr | Ceo and Co-Founder | Treeverse This talk examines why agile methodology, continuous integration, and continuous deployment and production monitoring are essential for data lakes.

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Modern Data Architectures Provide a Foundation for Innovation

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The group kicked off the session by exchanging ideas about what it means to have a modern data architecture. Atif Salam noted that as recently as a year ago, the primary focus in many organizations was on ingesting data and building data lakes.

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Popular Machine Learning Libraries, Ethical Interactions Between Humans and AI, and 10 AI Startups…

ODSC - Open Data Science

Automating Remediation Processes for Data Security Posture Management Before we look into how we can automate it, it is important to understand how data security posture management helps you achieve your goals.

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5 Fast-Growing Data Management Trends in 2023

ODSC - Open Data Science

Data Mesh More data management systems in 2023 will also shift toward a data mesh architecture. This decentralized architecture breaks data lakes into smaller domains specific to a given team or department. Automation and artificial intelligence (AI) will see particular growth in the realm of observability.

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Modern Data Management Essentials: Exploring Data Fabric

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Without access to all critical and relevant data, the data that emerges from a data fabric will have gaps that delay business insights required to innovate, mitigate risk, or improve operational efficiencies. You must be able to continuously catalog, profile, and identify the most frequently used data.

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4 Key Trends in Data Quality Management (DQM) in 2024

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It’s important to note that end-to-end data observability of your complex data pipelines is a necessity if you’re planning to fully automate the monitoring, diagnosis, and remediation of data quality issues. Standardized processes for remediation of enterprise-wide data quality issues are beginning to gain traction.”