Many types of business problems boil down to making recommendations, and machine learning is the special sauce that makes these problems solvable. Machine learning for recommendations is a challenging endeavor in its own right, but it is just one part of the recommendation system, which must move, store, process, and update data, in production, across several different components. In this post we show how to use Cloudera’s distribution of open source software to build a production scale recommendation system,
Self-service BI and exploratory analytics are some of the most common use cases we see our customers running on Cloudera’s analytic database solution. Over the past year, we made significant advancements to provide a more powerful user experience for SQL developers and make them more productive for their everyday self-service BI tasks and workflows. Leveraging Hue as the SQL development workbench, we continue to see usage of the platform increase and the number of analytic use cases grow –
A few weeks back, we announced the upcoming beta of Cloudera Altus Analytic DB for cloud-based data warehousing. As promised, the beta is now available and we wanted to spend some time describing the unique architecture.
Architecture of Cloudera Altus Analytic DB
Altus Analytic DB is built on the Cloudera Altus platform-as-a-service foundation, which also supports the Altus Data Engineering service. The architecture of Cloudera Altus is based around a few simple but important premises —
For the Apache Spot novice or for quick evaluation of a Cybersecurity solution on Cloudera Enterprise Data Hub (EDH) without the arduous tasks of manual installation, we’ve created a rapid deployment of Apache Spot on Amazon Web Services (AWS) using Cloudera Director.
You will immediately see how you can isolate and identify suspicious activities from the Apache Spot UI using the sample data provided in the deployment at cloud scale.
Cloudera Director 2.7 introduces support for LDAP authentication, improved Java 8 support, and instance template level normalization configuration. Continuing improvements have been made to the AWS plugin.
Cloudera Director helps you deploy, scale, and manage Cloudera clusters in AWS, Azure, or Google Cloud Platform. Its enterprise-grade features deliver a mechanism for establishing production-ready clusters in the cloud for big-data workloads and applications in a simple, reliable, automated fashion.