Skip to main content

Yes, mobile messaging relies on Hadoop

Mobile messaging apps are the talk of the town after acquisition of Whatsapp by Facebook for a mind boggling figure. It is pretty much in time to take a look at the data infrastructure of the messaging apps. With an aim to explore how much of Hadoop is being used in the messaging world, a few checks were done and certain interesting observations came forward.

First a look at major mobile messaging apps.
While Whatsapp is all too popular now, it has its major user base of 450 million+ users in USA, Europe, Latin America and India. Towards, the oriental half of Asia, there are other dominant players who have spread their reach across the globe. These include QQ and WeChat(from Tencent, China) with an estimated user base of 1 billion; Line(of formerly NHN Corp, Japan) with an estimated user base of 350 million; Kakao Talk (originating from Korea) with an estimated user base of 100 million. Towards the other half of the world in America, SnapChat, Kik, GroupMe, Viber and age old Skype are quite popular. And, not to forget, Facebook also has a Messenger offering.

A look at the Hadoop skilled staff on LinkedIn profiles and Apache Software foundation contributors revealed some interesting comparisons:
- Facebook, the parent company of Facebook messenger is one of the biggest proponents of Hadoop. However, WhatsApp does not seem to think the same way in terms of Hadoop adoption.
- Tencent, the Chinese parent company of QQ & WeChat has a huge concentration of Hadoop skills. Tencent though has bigger interests in internet and communication industry besides messaging.
- Even Skype (now Microsoft company) has been using Hadoop.
- Viber is a big user of NoSQL products like Couchbase and has been using HDFS and other associated ecosystem products.
- Line Corporation(formerly NHN) has for long been a user of Hadoop and was one of main patrons of Apache Hama.
- Kakao and Line have contributors on Apache Tajo project.


To understand a sample use case of Hadoop ecosystem involvement, take a look below at a slide deck telling about Line's HBase initiatives.


  1. It is pretty much in time to take a look at the data infrastructure of the messaging logging software

  2. Interesting article, yet there are cell phone spy apps which are able to track every messaging app. So where is a security?


Post a Comment

Popular articles

5 online tools in data visualization playground

While building up an analytics dashboard, one of the major decision points is regarding the type of charts and graphs that would provide better insight into the data. To avoid a lot of re-work later, it makes sense to try the various chart options during the requirement and design phase. It is probably a well known myth that existing tool options in any product can serve all the user requirements with just minor configuration changes. We all know and realize that code needs to be written to serve each customer’s individual needs. To that effect, here are 5 tools that could empower your technical and business teams to decide on visualization options during the requirement phase. Listed below are online tools for you to add data and use as playground. 1)      Many Eyes : Many Eyes is a data visualization experiment by IBM Research and the IBM Cognos software group. This tool provides option to upload data sets and create visualizations including Scatter Plot, Tree Ma

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction. Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability. From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets. Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus o

In-memory data model with Apache Gora

Open source in-memory data model and persistence for big data framework Apache Gora™ version 0.3, was released in May 2013. The 0.3 release offers significant improvements and changes to a number of modules including a number of bug fixes. However, what may be of significant interest to the DynamoDB community will be the addition of a gora-dynamodb datastore for mapping and persisting objects to Amazon's DynamoDB . Additionally the release includes various improvements to the gora-core and gora-cassandra modules as well as a new Web Services API implementation which enables users to extend Gora to any cloud storage platform of their choice. This 2-part post provides commentary on all of the above and a whole lot more, expanding to cover where Gora fits in within the NoSQL and Big Data space, the development challenges and features which have been baked into Gora 0.3 and finally what we have on the road map for the 0.4 development drive. Introducing Apache Gora Although