Category Archives: CDH

New in Cloudera Enterprise 6.0: Analytic Search

Categories: CDH Search

It has been a long and patient wait for Apache Hadoop 3.0 to mature. A major new version of the storage layer obviously impacts all our integrated components, including Apache Solr and all our integrations with the rest of the platform, commonly referred to as Cloudera Search. Since our customers’ Search deployments are so often mission critical, we’ve made sure to take time to do extensive integration testing and focus on the upgrade experience.

Now the moment has finally come to announce Solr 7.0 in Cloudera Search and available as of our new major release,

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Backup and Disaster Recovery for Cloudera Search

Categories: CDH Search

One of the worst things that can happen in mission-critical production environments is loss of data and another is downtime. For a search service that provides end users with easy access to data using natural language, downtime would mean complete halt for those parts of your organization. Even worse if the search service is fueling your online business, it interrupts your customer access and end user experience.

That is why we designed multiple options of backup and disaster recovery for your data served via Cloudera Search,

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Automated Provisioning of CDH in the Cloud with Cloudera Director and Ansible

Categories: CDH Cloud Cloudera Director Guest

This is a guest blog post from Jasper Pult, Technology Consultant at Lufthansa Industry Solutionsan international IT consultancy covering all aspects of Big Data, IoT and Cloud.  The below work was implemented using Director’s API v9 and certain API details might change in future versions.

Cloud computing is quickly replacing traditional on premises solutions in all kinds of industries. With Apache Hadoop workloads often varying in resource requirements over time,

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Production Recommendation Systems with Cloudera

Categories: CDH Data Science

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,

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