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.

Comments

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

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

    ReplyDelete

Post a Comment

Popular posts from this blog

Beyond NSA, the intelligence community has a big technology footprint

While all through the past few days the focus has been on NSA activities, the discussion has often veered around the technologies and products used by NSA. At the same time, a side discussion topic has been the larger technical ecosystem of intelligence units. CIA has been one of the more prolific users of Information Technology by its own admission. To that extent, CIA spinned off a venture capital firm In-Q-Tel in 1999 to invest in focused sector companies. Per Helen Coster of Fortune Magazine, In-Q-Tel (IQT) has been named “after the gadget-toting James Bond character Q”.
In-Q-Tel states on its website that “We design our strategic investments to accelerate product development and delivery for this ready-soon innovation, and specifically to help companies add capabilities needed by our customers in the Intelligence Community”. To that effect, it has made over 200 investments in early stage companies for propping up products. Being a not-for-profit group, unlike Private Venture capi…

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 on how MapReduce and…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…