Cloudera Engineering Blog · Hadoop Posts
It was good to see Jay Kreps (@jaykreps), the LinkedIn engineer who is the tech lead for that company’s online data infrastructure, visit Cloudera Engineering yesterday to spread the good word about Apache Kafka.
Kafka, of course, was originally developed inside LinkedIn and entered the Apache Incubator in 2011. Today, it is being widely adopted as a pub/sub framework that works at massive scale (and which is commonly used to write to Apache Hadoop clusters, and even data warehouses).
There’s an important new addition coming to the Apache Hadoop book ecosystem. It’s now in early release!
We are very happy to announce that the new Apache Hadoop book we have been writing for O’Reilly Media, Hadoop Application Architectures, is now available as an early release! It contains the first two chapters and can be found in O’Reilly’s Catalog and via Safari.
Pretty busy for early Summer:
Google’s Jeff Dean — among the original architects of MapReduce, Bigtable, and Spanner — revealed some fascinating facts about Google’s internal environment at Cloudera HQ recently.
Earlier this week, we were pleased to welcome Google Senior Fellow Jeff Dean to Cloudera’s Palo Alto HQ to give an overview of some of his group’s current research. Jeff has a peerless pedigree in distributed computing circles, having been deeply involved in the design and implementation of Google’s original advertising serving system, MapReduce, Bigtable, Spanner, and a host of other projects.
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).
More good news!
Using an appropriate network representation and the right tool set are the key factors in successfully merging structured and time-series data for analysis.
In Part 1 of this series, you took your first steps for using Apache Giraph, the highly scalable graph-processing system, alongside Apache Hadoop. In this installment, you’ll explore a general use case for analyzing time-dependent, Big Data graphs using data from multiple sources. You’ll learn how to generate random large graphs and small-world networks using Giraph – as well as play with several parameters to probe the limits of your cluster.
In its relatively short lifetime (co-founded by Twitter and Cloudera in July 2013), Parquet has already become the de facto standard for columnar storage of Apache Hadoop data — with native support in Impala, Apache Hive, Apache Pig, Apache Spark, MapReduce, Apache Tajo, Apache Drill, Apache Crunch, and Cascading (and forthcoming in Presto and Shark). Parquet adoption is also broad-based, with employees of the following companies (partial list) actively contributing:
Learn how to convert your data to the Parquet columnar format to get big performance gains.
Using a columnar storage format for your data offers significant performance advantages for a large subset of real-world queries. (Click here for a great introduction.)