Cloudera Developer Blog
Big Data best practices, how-to's, and internals from Cloudera Engineering and the community
Extended attributes in HDFS will facilitate at-rest encryption for Project Rhino, but they have many other uses, too.
Many mainstream Linux filesystems implement extended attributes, which let you associate metadata with a file or directory beyond common “fixed” attributes like filesize, permissions, modification dates, and so on. Extended attributes are key/value pairs in which the values are optional; generally, the key and value sizes are limited to some implementation-specific limit. A filesystem that implements extended attributes also provides system calls and shell commands to get, list, set, and remove attributes (and values) to/from a file or directory.
Find Cloudera tech talks in Texas, Oregon, Washington DC, Illinois, Georgia, Japan, and across the SF Bay Area during the next calendar quarter.
Below please find our regularly scheduled quarterly update about where to find tech talks by Cloudera employees – this time, for the third calendar quarter of 2014 (July through September; traditionally, the least active quarter of the year). Note that this list will be continually curated during the period; complete logistical information may not be available yet. And remember, many of these talks are in “free” venues (no cost of entry).
Prefer IntelliJ IDEA over Eclipse? We’ve got you covered: learn how to get ready to contribute to Apache Hadoop via an IntelliJ project.
It’s generally useful to have an IDE at your disposal when you’re developing and debugging code. When I first started working on HDFS, I used Eclipse, but I’ve recently switched to JetBrains’ IntelliJ IDEA (specifically, version 13.1 Community Edition).
It’s been a while since we provided a how-to for this purpose. Thanks, Daan Debie (@DaanDebie), for allowing us to re-publish the instructions below (for CDH 5)!
I recently started as a Big Data Engineer at The New Motion. While researching our best options for running an Apache Hadoop cluster, I wanted to try out some of the features available in the newest version of Cloudera’s Hadoop distribution: CDH 5. Of course I could’ve downloaded the QuickStart VM, but I rather wanted to run a virtual cluster, making use of the 16GB of RAM my shiny new 15″ Retina Macbook Pro has ;)
What is your definition of a “data scientist”?
An update on community efforts to bring at-rest encryption to HDFS — a major theme of Project Rhino.
Encryption is a key requirement for many privacy and security-sensitive industries, including healthcare (HIPAA regulations), card payments (PCI DSS regulations), and the US government (FISMA regulations).
Unique across all options, Cloudera Manager makes it easy to do what would otherwise be a disruptive operation for operators and users.
For the increasing number of customers that rely on enterprise data hubs (EDHs) for business-critical applications, it is imperative to minimize or eliminate downtime — thus, Cloudera has focused intently on making software upgrades a routine, non-disruptive operation for EDH administrators and users.
More good news!
Thanks to Bill Podell, VP Big Data and BI Practice, MBI Solutions, for the guest post below.
Capacity planning has long been a critical component of successful implementations for production systems. Today, Big Data calls for a particularly deep understanding of capacity management – because resource utilization explodes as business users, analysts, and data scientists jump onboard to analyze and use newly found data. The resource impact can escalate very quickly, causing poor loading and or response times. The result is throwing more hardware at the issue without any understanding of what impact the new hardware will have on the current issue. Better yet, be proactive and know about the problem before the problem even occurs!
Organizing your data inside Hadoop doesn’t have to be hard — Kite SDK helps you try out new data configurations quickly in either HDFS or HBase.
Kite SDK is a Cloudera-sponsored open source project that makes it easier for you to build applications on top of Apache Hadoop. Its premise is that you shouldn’t need to know how Hadoop works to build your application on it, even though that’s an unfortunately common requirement today (because the Hadoop APIs are low-level; all you get is a filesystem and whatever else you can dream up — well, code up).