How-to: Backup and disaster recovery for Apache Solr (part I)

Categories: Hadoop How-to Search

Cloudera Search (that is Apache Solr integrated with the Apache Hadoop eco-system) now supports (as of C5.9) a backup and disaster recovery capability for Solr collections.

In this post we will cover the basics of the backup and disaster recovery capability in Solr and hence in Cloudera Search. In the next post we will cover the design of the Solr snapshots functionality and its integration with the Hadoop ecosystem as well as public cloud platforms (e.g.

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New in Cloudera Enterprise 5.11: Hue Data Search and Tagging

Categories: CDH Hadoop Hue

Self-service business intelligence and exploratory analytics continue to be a primary use case for Cloudera’s customers. Over the past year, we have made a number of significant advancements in Hue, the intelligent SQL editor, to provide a more powerful user experience for SQL developers and make them even more productive for those use cases.

The recent release of  Cloudera 5.11 furthers this effort with new enhancements around embedded search and tagging for faster data discovery,

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Getting Started with Cloudera Data Science Workbench

Categories: CDH Data Science

Last week, Cloudera announced the General Availability release of Cloudera Data Science Workbench. In this post, I’ll give a brief overview of its capabilities and architecture, along with a quick-start guide to connecting Cloudera Data Science Workbench to your existing CDH cluster in three simple steps.

At its core, Cloudera Data Science Workbench enables self-service data science for the enterprise. Data scientists can build, scale, and deploy data science and machine learning solutions in a fraction of the time,

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The Benefits of Migrating HPC Workloads To Apache Spark

Categories: CDH Data Science Hadoop Spark

Overview

Recently we worked with a customer that needed to run a very significant amount of models in a given day to satisfy internal and government regulated risk requirements.  Several thousand model executions would need to be supported per hour.  Total execution time was very important to this client.  In the past the customer used thousands of servers to meet the demand.  They need to run many derivations of this model with different economic factors to satisfy their requirements.   

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Hail: Scalable Genomics Analysis with Apache Spark

Categories: CDH Data Science Spark

Technology-focused discussions about genomics usually highlight the huge growth in DNA sequencing since the beginning of the century, growth that has outpaced Moore’s law and resulted in the $1000 genome. However, future growth is projected to be even more dramatic. In the paper “Big Data: Astronomical or Genomical?”, the authors say it is estimated that “between 100 million and as many as 2 billion human genomes could be sequenced by 2025”,

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