Category Archives: Data Engineering

Announcing Workload Analytics for Cloudera Altus

Categories: Cloud Data Engineering

When we announced Cloudera Altus, we called out three guiding principles that led us to reimagine running big data workloads in the cloud: simplicity, cost effectiveness, and maintaining the integrity of Cloudera’s trusted, enterprise-grade platform at the core. We decided early on that enabling customers to migrate data engineering workloads (which benefit most from cloud…

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Simplifying Big Data in the Cloud

Categories: Cloud Data Engineering Product Spark

In recent years, as public cloud adoption has accelerated and customers have started looking towards cloud for large-scale data workloads, we sought to reimagine how to most effectively offer Cloudera capabilities in the cloud. Our customers wanted to understand how to leverage the agility, scale, and ease-of-use offered by the cloud to efficiently and cost-effectively…

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What To Consider When You’re Considering Cloud

Categories: Analytic Database Cloud Data Engineering Data Science Operational Database Spark

In a blog posted earlier this week, my esteemed colleague Sean Anderson laid out a powerful argument for machine learning (ML) as a way to fuel recommendation engines, churn reduction engines, and IoT workflows. Leveraging components like Apache Spark, and its machine-learning libraries, data scientists are able to design and train complex models using troves…

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