A telecommunications company called Cazena to learn about our Data Lake as a Service, hoping that it would help them deliver an at-risk project. It definitely would, it’s a cloud use case we know well, with a solution designed accordingly.

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When we started the Cazena journey, we commissioned a big data survey and research analysis from GigaOm. Our goal was to discover the greatest challenges for organizations adopting cloud services for analytics, data science or big data. The top challenge? Security, unsurprisingly.
 

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Cazena partner Sentier Health Informatics weighs in on their experiences navigating the complicated world of cloud security in a regulated industry. Sentier is focused on healthcare, life sciences, and insurance organizations who are quickly discovering that judicial use public cloud services is critical for competitive advantage in predictive analytics.
 

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As part of our ongoing series on Productionizing Hadoop and Spark in the cloud, we explore performance optimization, and how companies scale and tune for the best performance. We also discuss what’s required for production-grade deployments, often an underestimated part of the process.
 

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As part of our ongoing series on Productionizing Hadoop and Spark in the cloud, we explore performance optimization, and how companies scale and tune for the best performance. We also discuss what’s required for production-grade deployments, often an underestimated part of the process.

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In the first installment of our series on The Hidden Challenges of Putting Hadoop and Spark in Production, we explore the infrastructure selection process. This series was inspired after we read a recent Gartner survey, which estimates that only 14% of Hadoop deployments are in production. We’re not surprised....
 

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There is an interesting theme mentioned by the leaders of data science and advanced analytics groups: All are focused on how to make their team as productive as possible. The resources for these teams are notoriously hard to find.  So, naturally, team leaders want to ensure that these scarce, highly-skilled workers have everything they need to be efficient. Here are the most common pitfalls we hear about. Do you agree?

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Over the past few years, I have observed a deepening organizational divide in large data-driven companies. On one hand, IT and data owners have their hands full managing their current data infrastructure and platforms.

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Japanese rock gardens, or zen gardens, were first constructed centuries ago at temples as aids to meditation. Also called “dry landscapes,” zen gardens are designed as miniature models of natural landscapes. This practice of artfully modeling the world in miniature seemed like a beautiful analogy to launch our new Data Science Sandbox as a Service…

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In the past, protecting and securing enterprise data was simpler—handled mainly through the use of basic perimeter-based devices like firewalls and intrusion protection services. As more and more enterprises now look to migrate or augment their big data clusters with the cloud, the amount of access points to their data continues to exponentially increase. For the modern enterprise, perimeters are almost gone. Thorough security and compliance measures for this newly distributed data are now a top priority for CISOs and security teams, well-covered in several recent articles around the web.

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