Cloudera Engineering Blog · Cloud Posts
You now have more deployment options for getting hands-on with Apache Hadoop.
Launched in September 2014, Cloudera Live has become a popular choice for getting hands-on with Apache Hadoop via the cloud and Cloudera Enterprise, the world’s most deployed commercial Hadoop-based platform (CDH + Cloudera Manager, Navigator, and Director). The popularity is credited to its ease of spin up and use: With step-by-step, ramp-up tutorials, Cloudera Live helps users get up and running in just a few hours.
The recent OpenStack Kilo release adds many features to the Sahara project, which provides a simple means of provisioning an Apache Hadoop (or Spark) cluster on top of OpenStack. This how-to, from Intel Software Engineer Wei Ting Chen, explains how to use the Sahara CDH plugin with this new release.
This how-to assumes that OpenStack is already installed. If not, we recommend using Devstack to build a test OpenStack environment in a short time. (Note: Devstack is not recommended for use in a production environment. For production deployments, refer to the OpenStack Installation Guide.)
We’re pleased to announce the release of Cloudera Enterprise 5.4 (comprising CDH 5.4, Cloudera Manager 5.4, and Cloudera Navigator 2.3).
Cloudera Enterprise 5.4 (Release Notes) reflects critical investments in a production-ready customer experience through governance, security, performance and deployment flexibility in cloud environments. It also includes support for a significant number of updated open standard components–including Apache Spark 1.3, Impala 2.2, and Apache HBase 1.0 (as well as unsupported beta releases of Hive-on-Spark data processing and OpenStack deployments).
Providing Hadoop-as-a-Service to your internal users can be a major operational advantage.
Cloudera Director (free to download and use) is designed for easy, on-demand provisioning of Apache Hadoop clusters in Amazon Web Services (AWS) environments, with support for other cloud environments in the works. It allows for provisioning clusters in accordance with the Cloudera AWS Reference Architecture.
Cloudera Director 1.1 introduces new features and improvements that provide more options for creating and managing cloud deployments of Apache Hadoop. Here are details about how they work.
Cloudera Director, which was released in October of 2014, delivers production-ready, self-service interaction with Apache Hadoop clusters in cloud environments. You can find background information about Cloudera Director’s purpose and fundamental features in our earlier introductory blog post and technical overview blog post.
A new Spark tutorial and Trifacta deployment option make Cloudera Live even more useful for getting started with Apache Hadoop.
When it comes to learning Hadoop and CDH (Cloudera’s open source platform including Hadoop), there is no better place to start than Cloudera Live (cloudera.com/live). With a quick, one-button deployment option, Cloudera Live launches a four-node Cloudera cluster that you can learn and experiment in free for two-weeks. To help plan and extend the capabilities of your cluster, we also offer various partner deployments. Building on the addition of interactive tutorials and Tableau and Zoomdata integration, we have added a new tutorial on Apache Spark and a new Trifacta partner deployment.
Interested in Hive-on-Spark progress? This new AMI gives you a hands-on experience.
Nearly one year ago, the Apache Hadoop community began to embrace Apache Spark as a powerful batch-processing engine. Today, many organizations and projects are augmenting their Hadoop capabilities with Spark. As part of this shift, the Apache Hive community is working to add Spark as an execution engine for Hive. The Hive-on-Spark work is being tracked by HIVE-7292 which is one of the most popular JIRAs in the Hadoop ecosystem. Furthermore, three weeks ago, the Hive-on-Spark team offered the first demo of Hive on Spark.
With Cloudera Director, cloud deployments of Apache Hadoop are now as enterprise-ready as on-premise ones. Here’s the technology behind it.
As part of the recent Cloudera Enterprise 5.2 release, we unveiled Cloudera Director, a new product that delivers enterprise-class, self-service interaction with Hadoop clusters in cloud environments. (Cloudera Director is free to download and use, but commercial support requires a Cloudera Enterprise subscription.) It provides a centralized administrative view for cloud deployments and lets end users provision and scale clusters themselves using automated, repeatable, managed processes. To summarize, the same enterprise-grade capabilities that are available with on-premise deployments are now also available for cloud deployments. (For an overview of and motivation for Cloudera Director, please check out this blog post.)
The combination of OpenShift and Kite SDK turns out to be an effective one for developing and testing Apache Hadoop applications.
At Cloudera, our engineers develop a variety of applications on top of Hadoop to solve our own data needs (here and here). More recently, we’ve started to look at streamlining our development process by using a PaaS (Platform-as-a-Service) for some of these applications. Having single-click deployment and updates to consistent development environments lets us onboard new developers more quickly, and helps ensure that code is written and tested along patterns that will ensure high quality.
Cloudera Enterprise 5.2 contains new functionality for security, cloud deployments, and real-time architectures, and support for the latest open source component releases and partner technologies.
We’re pleased to announce the release of Cloudera Enterprise 5.2 (comprising CDH 5.2, Cloudera Manager 5.2, Cloudera Director 1.0, and Cloudera Navigator 2.1).
Our thanks to AWS Solutions Architect Rahul Bhartia for allowing us to republish his post below.
Apache Hadoop provides a great ecosystem of tools for extracting value from data in various formats and sizes. Originally focused on large-batch processing with tools like MapReduce, Apache Pig, and Apache Hive, Hadoop now provides many tools for running interactive queries on your data, such as Impala, Drill, and Presto. This post shows you how to use Amazon Elastic MapReduce (Amazon EMR) to analyze a data set available on Amazon Simple Storage Service (Amazon S3) and then use Tableau with Impala to visualize the data.
Using this new tutorial alongside Cloudera Live is now the fastest, easiest, and most hands-on way to get started with Hadoop.
At Cloudera, developer enablement is one of our most important objectives. One only has to look at examples from history (Java or SQL, for example) to know that knowledge fuels the ecosystem. That objective is what drives initiatives such as our community forums, the Cloudera QuickStart VM, and this blog itself.
Automating the creation of short-lived clusters for testing purposes frees our support engineers to spend more time on customer issues.
The first step for any support engineer is often to replicate the customer’s environment in order to identify the problem or issue. Given the complexity of Cloudera customer environments, reproducing a specific issue is often quite difficult, as a customer’s problem might only surface in an environment with specific versions of Cloudera Enterprise (CDH + Cloudera Manager), configuration settings, certain number of nodes, or the structure of the dataset itself. Even with Cloudera Manager’s awesome setup wizards, setting up Apache Hadoop can be quite time consuming, as the software was never designed with ephemeral clusters in mind.
In this installment of “Meet the Engineer”, our subject is Andrei Savu!
What do you do at Cloudera?
Get started with Apache Hadoop and use-case examples online in just seconds.
Today, we announced the Cloudera Live Read-Only Demo, a new online service for developers and analysts (currently in public beta) that makes it easy to learn, explore, and try out CDH, Cloudera’s open source software distribution containing Apache Hadoop and related projects. No downloads, no installations, no waiting — just point-and-play!
This FAQ contains answers to the most frequently asked questions about the architecture and configuration choices involved.
In December 2013, Cloudera and Amazon Web Services (AWS) announced a partnership to support Cloudera Enterprise on AWS infrastructure. Along with this announcement, we released a Deployment Reference Architecture Whitepaper. In this post, you’ll get answers to the most frequently asked questions about the architecture and the configuration choices that have been highlighted in that whitepaper.
Developers, rejoice: Impala is now available on EMR for testing and evaluation.
Very recently, Amazon Web Services announced support for running Cloudera Impala queries on its Elastic MapReduce (EMR) service. This is very good news for EMR users — as well as for users of other platforms interested in kicking Impala’s tires in a friction-free way. It’s also yet another sign that Impala is rapidly being adopted across the ecosystem as the gold standard for interactive SQL and BI queries on Apache Hadoop.
In this new installment of our “Meet the Project Founder” series, meet Tom White, founder of Apache Whirr, PMC Member for multiple other projects (Apache Hadoop, Apache Avro, Apache Bigtop, Apache Sqoop), and author of O’Reilly Media’s best-selling book, Hadoop: The Definitive Guide.
Editor’s Note (added Feb. 25, 2015): For releases beyond 4.5, Cloudera recommends the use of Cloudera Director for deploying CDH in cloud environments.
Cloudera Manager includes a new express installation wizard for Amazon Web Services (AWS) EC2. Its goal is to enable Cloudera Manager users to provision CDH clusters and Cloudera Impala (the open source distributed query engine for Apache Hadoop) on EC2 as easily as possible (for testing and development purposes only, not supported for production workloads) - and thus is currently the fastest way to provision a Cloudera Manager-managed cluster in EC2.
This was post was originally published by U.C. Berkeley AMPLab developer (and former Clouderan) Matt Massie, on his personal blog. Matt has graciously permitted us to re-publish here for your convenience.
Note: The post below is valid for Impala version 0.6 only and is not being maintained for subsequent releases. To deploy Impala 0.7 and later using a much easier (and also free) method, use this how-to.
You may have seen the recent announcement from Skytap about the availability of pre-configured CDH4 templates in the Skytap Cloud public template library. So for anyone who wants to try out a Cloudera Hadoop cluster—from small to large—it can now be easily accomplished in Skytap Cloud. The how-to below from Skytap’s Matt Sousely explains how.
The goal of this how-to will be to spin up a 10-node Cloudera Hadoop cluster in Skytap Cloud. To begin, let’s talk about the two new Cloudera Hadoop cluster templates. The first is Cloudera CDH4 Hadoop cluster: a 2-node Hadoop cluster template. It includes 2 nodes and a management node/server. The second is the Cloudera CDH4 Hadoop host template. This second template is not intended to run by itself in a configuration—rather, it contains a host VM that is ready to become another Hadoop node in the Cloudera CDH4 Hadoop cluster template-based configuration.
Note (added July 8, 2013): The information below is deprecated; we suggest that you refer to this post for current instructions.
Today we bring you one user’s experience using Apache Whirr to spin up a CDH cluster in the cloud. This post was originally published here by George London (@rogueleaderr) based on his personal experiences; he has graciously allowed us to bring it to you here as well in a condensed form. (Note: the configuration described here is intended for learning/testing purposes only.)
Apache Whirr release 0.7.0 is now available. It includes changes covering over 50 issues, four of which were considered blockers. Whirr is a tool for quickly starting and managing clusters running on cloud services like Amazon EC2. This is the first Whirr release as a top level Apache project (previously releases were under the auspices of the Incubator). In addition to improving overall stability some of the highlights are described below: