Category Archives: Hive

Faster Swarms of Data : Accelerating Hive Queries with Parquet Vectorization

Categories: CDH Hive Parquet Performance

Background

Apache Hive is a widely adopted data warehouse engine that runs on Apache Hadoop. Features that improve Hive performance can significantly improve the overall utilization of resources on the cluster. Hive processes data using a chain of operators within the Hive execution engine. These operators are scheduled in the various tasks (for example, MapTask, ReduceTask, or SparkTask) of the query execution plan. Traditionally, these operators are designed to process one row at a time.

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New in Cloudera Enterprise 6: Apache Hive 2.1

Categories: CDH Hive

We recently released Cloudera Enterprise 6.0 featuring significant improvements across a number of core components. In this blog post, we’re going to focus on Apache Hive 2.1.

Hive’s Approach to Rebase: Stability and Quality Most Important

Prior to the release of Cloudera Enterprise 6.0, Cloudera’s supported platform included Apache Hive 1.1 augmented with numerous features, enhancements and fixes from the later Apache Hive releases—all of which were included only after rigorous quality criteria were met.

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Using Amazon S3 with Cloudera BDR

Categories: CDH Cloud Cloudera Manager HDFS Hive

More of you are moving to public cloud services for backup and disaster recovery purposes, and Cloudera has been enhancing the capabilities of Cloudera Manager and CDH to help you do that. Specifically, Cloudera Backup and Disaster Recovery (BDR) now supports backup to and restore from Amazon S3 for Cloudera Enterprise customers.

BDR lets you replicate Apache HDFS data from your on-premise cluster to or from Amazon S3 with full fidelity (all file and directory metadata is replicated along with the data).

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Data Engineering with Cloudera Altus

Categories: Altus Cloud Hive Spark

With modern businesses dealing with an ever-increasing volume of data, and an expanding set of data sources, the data engineering process that enables analysis, visualization, and reporting only becomes more important.

When considering running data engineering workloads in the public cloud, there are capabilities which enable different operational models from on-premises deployments. The key factors here are the presence of a distinct storage layer within the cloud environment, and the ability to provision compute resources on-demand (e.g.: with Amazon’s S3 and EC2 respectively).

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