Big data implementation for telecos
Data Set
|
Volume
|
Variety
|
Velocity
|
Remarks
|
Order
|
Medium
|
Medium
|
Low
|
Customer application form is Semistructured
|
CDRs
|
High
|
Low
|
High
|
Billions of records per day
|
Payments
|
Medium
|
Low
|
Medium
|
|
Network Data
|
High
|
Medium
|
High
|
Mostly structured data for call usage
– very high volumes – semistructured if web data – deep packet inspection is included |
Subscriber
|
Medium
|
Low
|
Low
|
|
Products
|
Low
|
Low
|
Low
|
Telcos moving towards simpler
products |
Current Data Management in Telecom vs Big Data
It is a fact that these solutions have come up to handle mass data processing requirements but they bring huge amount of obligations along with them.
1. The cost component which is very huge for any of these traditional appliance solutions – Both one time and AMC
2. The proprietary software’s and hardware’s which makes CSP to depend heavily on the vendor
3. Not all of these requirements can be met with one single implementation of these software
i.e for every major requirement CSP has to deploy similar or same solution which means additional cost
Big Data in Telecom
Traditionally telcos have been handling large volumes of call detail records (CDRs). And with the emergence of 3G and 4G and smarter devices (smart phone, tablets), the amount of CDRs continue to increase exponentially. Telecom service provides with an average of 50 million subscribers, it’s clear that how ‘Big’ the data has to be handled by Communication Service Provider (CSP) especially usage records, for their operational activities as well strategic decisions. In addition to this voluminous data other challenges are: _ Data gets generated from different network elements _ Data gets stored in multiple palaces in multiple application for the ease of managing and to meet specific business requirements Further it also carries valuable information about the customer and their behavior patterns which could be leveraged for improving the top line, bottom line and customer centricity
Big Data analytics classification
Big Data analytics is most appropriate for addressing strategic decisions based on very large data volume. It can handle larger volumes of data as compared to Enterprise Data Warehouses and has a capability of handling non-structured data (semi, quasi and unstructured). Currently it is not suitable for Real time analytics. Complex processing of data can be enabled using Big Data technologies.
Big Data Key players
Google added AppEngine-MapReduce to support running Hadoop 0.20 programs on Google App Engine.
Cloudera offers CDH (Cloudera’s Distribution including Apache Hadoop) and Cloudera
Enterprise
In May 2011, MapR Technologies, Inc. announced the availability of their distributed filesystem and MapReduce engine, the MapR Distribution for Apache Ha
doop.
EMC released EMC Greenplum Community Edition and EMC Greenplum HD Enterprise Edition in May 2011. The community edition, with optional for-fee technical support, consists of Hadoop, HDFS, HBase, Hive, and the ZooKeeper configuration service.
In June 2011, Yahoo! and Benchmark Capital formed Hortonworks Inc., whose focus is on making Hadoop more robust and easier to install, manage and use for enterprise users.
In Oct 2011, Oracle announced the Big Data Appliance, which integrates Hadoop, Oracle
Enterprise Linux, the R programming language, and a NoSQL database with the Exadata hardware