Staying in the MapR land, the question of comparing MapR to Cloudera is answered by people from all sides (MapR, Cloudera and Hortonworks). My summary: “cool proprietary technology addressing some of the current limitations of the Hadoop, but also missing some of the features the Hadoop community has come up with”.

Original title and link: How Does MapR Compare to Cloudera? (NoSQL database©myNoSQL)

via: http://www.quora.com/How-does-MapR-plan-to-compete-with-Cloudera

Announced 2 hours ago, by Twitter’s analytics infrastructure engineer Dmitriy Ryaboy, here comes Parquet:

We created Parquet to make the advantages of compressed, efficient columnar data representation available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model, or programming language.

The Parquet format page describes the details of the Apache Thrift metadata encoding, supported types, Thrift definitions, etc.

Original title and link: Parquet - Columnar Storage Format for Hadooop by Twitter and Cloudera (NoSQL database©myNoSQL)

via: http://parquet.github.com

Charles Zedlewki takes position for Cloudera to the recent attacks to Hadoop and Impala:

I’m reminded of our open source strategy this week not only because of the further validation of Hadoop’s popularity but also because of the entry of a new round of proprietary imitators. At one point there were six distinct vendors all promoting proprietary filesystems as alternatives to HDFS, many of which included breathless claims of how they could make Apache Hadoop faster and “more powerful.” This year we get to see history repeat itself, this time with SQL engines. The marketing is nearly identical to that of the proprietary filesystem era: damning open source with faint praise, pointing out its limitations and extolling the virtues of some feature(s) proprietary to that particular vendor.

Proprietary SQL vendors will pull a page from the proprietary storage playbook: damn open source Impala with faint praise and point out its limitations, both real and contrived. They will be equally ineffective. We will continue to bet on an open, integrated, and highly flexible big data platform. Saying you are “all in on Hadoop” while simultaneously promoting a proprietary platform means you are missing the point.

Neither Cloudera, nor other companies that invested a lot and everything in the Hadoop ecosystem are at the size not to care about large corporations attacking their bets. Every corporation is trying to emulate the Microsoft strategy: wait for a new technology to be confirmed, then jump at the opportunity with all your forces. But I really hope open source will prevail.

Original title and link: Cloudera Pissed Off (NoSQL database©myNoSQL)

via: http://blog.cloudera.com/blog/2013/02/open-source-flattery-and-the-platform-for-big-data/

Nong Li about Cloudera’s Impala implementation:

Cloudera Impala, the open-source real-time query engine for Apache Hadoop, uses many tools and techniques to get the best query performance. This blog post will discuss how we use runtime code generation to significantly improve our CPU efficiency and overall query execution time. We’ll explain the types of inefficiency that code-generation eliminates and go over in more detail one of the queries in the TPCH workload where code generation improves overall query speeds by close to 3x.

This reminded me of the days I was working on Java AOP frameworks whose implementation was based on bytecode generation for the same purpose of optimization. Everything worked perfectly well as long as the underlying assumptions remained the same.

Original title and link: Inside Cloudera Impala: Runtime Code Generation (NoSQL database©myNoSQL)

via: http://blog.cloudera.com/blog/2013/02/inside-cloudera-impala-runtime-code-generation/

Shaun Connolly summarizing a recent webinar about where Hortonwork’s work on Hadoop will focus in 2013:

[…] Interactive Query, Business Continuity (DR, Snapshots, etc.), Secure Access, as well as ongoing investments in Data Integration, Management (i.e. Ambari), and Online Data (i.e. HBase).
[…] Rather than abandon the Apache Hive community, Hortonworks is focused on working in the community to optimize Hive’s ability to serve big data exploration and interactive query in support of important BI use cases. Moreover, we are focused on enabling Hive to take advantage of YARN in Apache Hadoop 2.0, which will help ensure fast query workloads don’t compete for resources with the other jobs running in the cluster. Enabling Hadoop to predictably support enterprise workloads that span Batch, Interactive, and Online use cases is an important area of focus for us.

Basically this says that Hortonworks sees YARN and Hive as the answer to online or real-time interactive querying of Hadoop data. Cloudera’s take on this is different.

Original title and link: Hadoop in 2013: What Hortonworks Will Focus On (NoSQL database©myNoSQL)

via: http://hortonworks.com/blog/the-road-ahead-for-hortonworks-and-hadoop/

Hortonworks, a leading contributor to Apache Hadoop, today announced it has joined the OpenStack Foundation, which promotes the development, distribution and adoption of the OpenStack cloud operating system. By contributing to the OpenStack ecosystem, Hortonworks is supporting the open source community and facilitating adoption of 100-percent open source Apache Hadoop-based solutions in the cloud. Now customers will be able to access an enterprise-ready Hortonworks Data Platform built for the cloud that alleviates the time and complexities of manually deploying a big data solution.

What took this so long? Cloudera has been part of OpenStack since 2010.

Original title and link: Hortonworks Joins OpenStack Foundation (NoSQL database©myNoSQL)

via: http://hortonworks.com/about-us/news/hortonworks-joins-openstack-foundation/

As I was hoping and expecting, Datameer updated the chart visualizing Hadoop’s business side ecosystem:

It shouldn’t be a surprise to anyone that the top most connected companies in the Hadoop space are Cloudera and Hortonworks. They outrank the IT industry mammoths: IBM, HP, Microsoft, Oracle, SAP, etc.

Original title and link: Hadoop Business Ecosystem as of January 2013 (NoSQL database©myNoSQL)

via: http://www.datameer.com/blog/perspectives/hadoop-ecosystem-as-of-january-2013-now-an-app.html