Cloudera Developer Blog

Big Data best practices, how-to's, and internals from Cloudera Engineering and the community


Bringing the Best of Apache Hive 0.13 to CDH Users

More than 300 bug fixes and stable features in Apache Hive 0.13 have already been backported into CDH 5.0.0.

Last week, the Hive community voted to release Hive 0.13. We’re excited about the continued efforts and progress in the project and the latest release — congratulations to all contributors involved!

Using Apache Hadoop and Impala with MySQL for Data Analysis

Thanks to Alexander Rubin of Percona for allowing us to re-publish the post below!

Apache Hadoop is commonly used for data analysis. It is fast for data loads and scalable. In a previous post I showed how to integrate MySQL with Hadoop. In this post I will show how to export a table from  MySQL to Hadoop, load the data to Cloudera Impala (columnar format), and run reporting on top of that. For the examples below, I will use the “ontime flight performance” data from my previous post.

Meet the Engineer: Andrei Savu

In this installment of “Meet the Engineer”, our subject is Andrei Savu!

What do you do at Cloudera?

Apache Hadoop YARN: Avoiding 6 Time-Consuming "Gotchas"

Understanding some key differences between MR1 and MR2/YARN will make your migration much easier.

Here at Cloudera, we recently finished a push to get Cloudera Enterprise 5 (containing CDH 5.0.0 + Cloudera Manager 5.0.0) out the door along with more than 100 partner certifications.

Sneak Preview: "Case Studies" Track at HBaseCon 2014

The HBaseCon 2014 “Case Studies” track surfaces some of the most interesting (and diverse) use cases in the HBase ecosystem — and in the world of NoSQL overall — today.

The HBaseCon 2014 (May 5, 2014 in San Francisco) is not just about internals and best practices — it’s also a place to explore use cases that you not have even considered before.

Cloudera Live: The Instant Apache Hadoop Experience

Get started with Apache Hadoop and use-case examples online in just seconds.

Today, we announced Cloudera Live, 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!

Making Apache Spark Easier to Use in Java with Java 8

Our thanks to Prashant Sharma and Matei Zaharia of Databricks for their permission to re-publish the post below about future Java 8 support in Apache Spark. Spark is now generally available inside CDH 5.

One of Apache Spark‘s main goals is to make big data applications easier to write. Spark has always had concise APIs in Scala and Python, but its Java API was verbose due to the lack of function expressions. With the addition of lambda expressions in Java 8, we’ve updated Spark’s API to transparently support these expressions, while staying compatible with old versions of Java. This new support will be available in Spark 1.0.

A Few Examples

Meet the Data Scientist: Stuart Horsman

Meet Stuart Horsman, among the first to earn the CCP: Data Scientist distinction.

Big Data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the Big Data talent gap with the Cloudera Certified Professional (CCP) program.

How-to: Run a Simple Apache Spark App in CDH 5

Getting started with Spark (now shipping inside CDH 5) is easy using this simple example.

(Editor’s note – this post has been updated to reflect CDH 5.1/Spark 1.0)

How-to: Use cron-like Scheduling in Apache Oozie

Improved scheduling capabilities via Oozie in CDH 5 makes for far fewer headaches.

One of the best new Apache Oozie features in CDH 5, Cloudera’s software distribution, is the ability to use cron-like syntax for coordinator frequencies. Previously, the frequencies had to be at fixed intervals (every hour or every two days, for example) – making scheduling anything more complicated (such as every hour from 9am to 5pm on weekdays or the second-to-last day of every month) complex and difficult. 

Newer Posts Older Posts