During the last year I refined my RSS collection about big-data, data science and analytics. I usually check it everyday in order to discover a ton of new cool technologies and have fun. Here is the updated list.


News about emerging technologies, scalability and data

Data companies, social networks and search engines

Companies supporting e distributing big-data processing products

Recently I discovered the awesome data science list that contains a list of interesting blogger I haven’t time to check yet. You can surely find something more in it. I’ll try to publish an update when I’ll check it.

[UPDATE 2014-09-22 11:35]

Thanks to @onurakpolat for correcting my link to awesome data science list. Previous link was to his fork, the original repo is by @okulbilisim

The big-data environment at the moment is really “collaborative”. Each project is ready to run on almost every available platform and this is good. However, recently two factions are forming: people who use the Hadoop 2.0 Stack and people who use the BDAS.

Hadoop 2.0 Stack


The most important difference between Hadoop 2.0 and previous versions is YARN, the new cluster resource manager and next generation MapReduce. It can run almost every kind of big-data project:

  • Traditional Map Reduce (new version is backward compatible) with Hive or Pig or Cascading for query.
  • Interactive near real-time Map Reduce (using Tez)
  • HBase and Accumulo
  • Storm and S4 for stream processing
  • Giraph for graph processing
  • OpenMPI for message passing
  • Spark as In-memory Map Reduce
  • HDFS as distributed filesystem
  • more…

Most interesting companies here are IntelCloudera, MapR and Hortonworks.

BDAS (Berkely Data Analytics Stack)


On the BDAS everything is built around Mesos: the cluster resource manager. It’a relative new project is already widely used. Traditional HDFS is accelerated by Tachyon (in-memory file system). The main integration is around Spark which is the base for:

Mesos can also run traditional Hadoop environment and other projects (such as Storm and OpenMPI). You can also run traditional applications (also Rails apps) using Marathon.

The most interesting companies here are Databricks and Mesosphere.

Who will win? 😀


According to WikipediaKlout is “a website and mobile app that uses social media analytics to rank its users according to online social influence via the “Klout Score“, which is a numerical value between 1 and 100“.

This is not so different from what I try to do everyday. They get signals from social networks, process them in order to extract relevant data and show some diagrams and a synthetic index of user influence. It’s really interesting for me observe how their data is stored and processed.

At Hadoop Summit 2012, Dave Mariani (by Klout) and Denny Lee (by Microsoft) presented the Klout architecture and shown the following diagram:


It shows many different technologies, a great example of polyglot persistence 🙂

Klout uses a lot of Hadoop. It’s used to collect signals coming from different Signal Collectors (one for each social network i suppose). Procedure to enhance data are written using Pig and Hive used also for data warehouse.

Currently MySQL is used only to collect user registrations, ingested into the data warehouse system. In the past they use it as bridge between the data warehouse and their “Cube“, a Microsoft SQL Server Analysis Services (SSAS). They use it for Business Intelligence with Excel and other custom apps. On 2011 data were migrated using Sqoop. Now they can leverage on Microsoft’s Hive ODBC driver and MySQL isn’t used anymore.

Website and mobile app are based on the Klout API. Data is collected from the data warehouse and stored into HBase (users profile and score) and MongoDB (interaction between users). ElasticSearch is used as search index.

Most of custom components are written in Scala. The only exception is the website, written in Javascript/Node.js.

In the end Klout is probably the biggest company working both using open source tools coming from the Hadoop ecosystem and Microsoft tools. The Hadoop version for Windows Azure, developed in pair with Hortonworks, is probably the first product of this collaboration.