InfoQ Homepage Data Management Content on InfoQ
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Sleeping Well at Night During a Live Cloud Migration in a VMware Environment
This article describes the challenges of live migration to the cloud and presents key concepts and requirements that enterprises and their service providers need to understand and adopt if they want to sleep well at night when migrating on-premises VMs and data to the cloud.
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Seth James Nielson on Blockchain Technology for Data Governance
Seth James Nielson recently hosted a tutorial workshop at Data Architecture Summit 2018 Conference about Blockchain technology and its impact on data architecture and data governance.
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Columnar Databases and Vectorization
In this article, author Siddharth Teotia discusses the Dremio database which is based on Apache Arrow with vectorization capabilities.
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Q&A on the Book Software Wasteland
Almost all Enterprise Information Systems now cost vastly more to implement than they should. When you have hundreds or thousands of complex applications, you are stuck in the Application Centric Quagmire. In the book Software Wasteland Dave McComb explores what is causing application development waste and how visualizing the cost of change and becoming data-centric can help to reduce the waste.
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What Do Data Scientists and Data Engineers Need to Know about GDPR?
Andrew Burt on the implications of GDPR on data collection, storage and use for any organization dealing with customer data in the EU. Burt explains what's the minimum an org needs to pass the GDPR test, as well as how to take the opportunity to improve their overall data governance.
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Big Data Processing with Apache Spark – Part 1: Introduction
Apache Spark is an open source big data processing framework built around speed, ease of use, and sophisticated analytics. In this article, Srini Penchikala talks about how Apache Spark framework helps with big data processing and analytics with its standard API. He also discusses how Spark compares with traditional MapReduce implementation like Apache Hadoop.
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Improving Data Management with the DMM
The CMMI Institute has launched the Data Management Maturity (DMM)SM model. It can be used to improve data management, helping organizations to bridge the gap between business and IT. Using the DMM, organizations can evaluate and improve their data management practices. The model leverages the principles, structure, and proven approach of the Capability Maturity Model Integration (CMMI).
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Cindy Walker on Data Management Best Practices and Data Analytics Center of Excellence
Cindy Walker spoke at Enterprise Data World Conference about using semantic approaches to augment the data management practices. InfoQ spoke with her about the data management best practices and the data analytics center of excellence initiative.
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Interview and Book Review: NoSQL Distilled
InfoQ spoke with both authors of the book, Pramod and Martin Fowler about NoSQL database space, the emerging trends in NoSQL.