Data Engineering

Staffing your big data team

Building the right team is as important as assembling the right IT infrastructure – and the needs differ just as dramatically. A traditional BI and analytics organization consists of three main groups:   Analysts that develop reports often using sample…
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Announcing Workload Analytics for Cloudera Altus

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…
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New Capabilities for Apache Spark Users

  In September 2015, Cloudera launched the One Platform Initiative to make Apache Spark the default engine for Cloudera’s modern data platform. At the time, we had about 150 customers using Spark, many of them for simple ETL and data…
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Simplifying Big Data in the Cloud

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…
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An Enterprise Data Hub, The Next Gen Platform

Cloudera is proud to host Hired Brains analyst and data systems expert Neil Raden in a series of papers aimed at helping IT professionals understand the opportunities they have to modernize and ultimately optimize their data environments.  Increasingly users are…
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What To Consider When You’re Considering Cloud

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…
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