Category Archives: Altus

Introducing S3Guard: S3 Consistency for Apache Hadoop

Categories: Altus CDH Cloud Hadoop

Synopsis

This article introduces a new Apache Hadoop feature called S3Guard. S3Guard addresses one of the major challenges with running Hadoop on Amazon’s Simple Storage Service (S3), eventual consistency. We outline the problem of S3’s eventual consistency, how it affects Hadoop workloads, and explain how S3Guard works.

Problem

Although Apache Hadoop has support for using Amazon Simple Storage Service (S3) as a Hadoop filesystem, S3 behaves different than HDFS.  One of the key differences is in the level of consistency provided by the underlying filesystem.

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Cloudera Enterprise 5.12 is Now Available

Categories: Altus CDH Cloud Cloudera Manager Cloudera Navigator Data Science Hue Impala Kafka Kudu

Cloudera is pleased to announce that Cloudera Enterprise 5.12 is now generally available (GA). The release includes enhancements for running in cloud environments (with broader ADLS support and improved AWS Spot Instance support), usability and productivity improvements for both data science and analytic workloads, as well as performance gains and self-service performance management across a range of workloads.

As usual, there are also a number of quality enhancements, bug fixes, and other improvements across the stack.

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Talend on Why their Partnership with Cloudera Altus is a No Brainer

Categories: Altus Cloud

The following article by Ciaran Dynes was reposted from the Talend blog with their permission. 

As you may have read, Talend recently announced its support for Cloudera / TalendCloudera Altus, a newly released Platform-as-a-Service (PaaS) offering that simplifies running large-scale data processing applications in the public cloud. For us, supporting Altus at launch was the absolute easiest decision given that so many of our customers are looking to realize the cost,

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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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