Telecoms network consists of hundreds of network elements that generate call detail records or event data for each phone call, text message, internet activity, or even a simple walk from one location to another. The number of records per day varies between millions and billions. In many cases, the network elements are from different vendors. The typical interface to access these records is file-based. Communication services providers want to analyze these records to run campaigns, improve user experience, improve network or call center, or to detect fraud. They need a system that unifies source data, runs near real-time analytics, and scales with increasing network traffic. Join our upcoming webinar to discover how we power telecom analytics using the Elastic Stack, an open source, scalable & distributed, reliably and securely take data from any source, in any format, then search, analyze, and visualize it in real time. Elastic Stack is a group of open source products from Elastic designed to help users take data from any type of source and in any format and search, analyze, and visualize that data in real time. The product group was formerly known as ELK Stack, in which the letters in the name stood for the products in the group: Elasticsearch, Logstash and Kibana. A fourth product, Beats, was subsequently added to the stack, rendering the potential acronym unpronounceable. Elastic Stack can be deployed on premises or made available as Software as a Service (SaaS).

1. Yassine LASRI - Product Manager
Septembre 26, 2019
Introducing Elastic Stack for
Telecom Analytics

2. 2
Yassine LASRI
Product Manager - Analytics
Synapticiel (www.synapticiel.co)
Telecoms Engineer
+12, Senior Architect BSS/OSS, Analytics
Revenue Assurance & Fraud Management
@ylasri
@lasri

3. 3
Housekeeping & Logistics
Attendees are automatically muted when joining Zoom
Ask questions for us in the Zoom chat during the webinar
Slides & Recording will be available after the webinar
Q/A will be at the end of the webinar

4. 4
Agenda
1) Telecom Data Challenges.
2) Quick Elastic Stack Overview.
3) High Level Architecture Example.
4) Demo
5) Q/A

5. 5
TelecomData Challenges
Network Operations
Subscribers Behavior
Complex & Diverse
Events Data Sources
Valuable
Business insights
Major Challenges
Network Optimizing
QoS & SLA, CEM
Fraud (Assurance)
Smart Marketing
Product Innovation

6. 6
NoSQL is better at
“scaling out” than RDBMS
Issue #2 : Capex & Opex Cost
Example : Oracle's Enterprise Edition Plus Business
Intelligence Suite lists at $221,2500 per processor How do you build
Distributed
H. Scalable,
Search/Analytics
platform ?
Issue #1 : RDBMS fails to support Big Data
Scaling "out" with RDBMS is not possible
Open Source NoSQL

7. Elastic Stack Overview
7

8. 8
ELASTICSEARCH
High Level Architecture

9. 9
Anatomy of Logstash Pipeline
Multiples sources (S&U)
Network Logs
Mediation Files
Queues
Beats
RDBMS
….
Multiples Transformations
Normalization
Enrichment
Corrélations
Aggregation
Pseudonymization
….
Multiples Outputs
Files Distribution
Elasticsearch Ingest
….

10. 10
Elastic Stack {Demo}
Demo Use Case/Context :
Generated full 24H prepaid voice charging events (Ercicsson CCN node)
Simulate medium Operator
Stages:
High Level example of logstash pipeline
Kibana data discovery, visualization & dashboarding
Elasticsearch data frames & entity-centric index (Starting version 7.3.0)

11. Yassine LASRI - Product Manager
Septembre 26, 2019
Webinar : Introducing Elastic
Stack for Telecom Analytics
(Q&A Session)

12. 12
Encrypted communications (inside/outside cluster)
Built-in authentication realms & IP-based authorization
Role-based access control (Index, Document & Field level access)
Security is free, starting in
versions 6.8.0 and 7.1.0

13. Role-based access control (RBAC)
Access can be restricted to resources :
Indices
Aliases
Documents
Fields
Users
Cluster
13

14. 14
X-Pack Open Code

15. 15
Elastic Stack Features

16. 16
Elasticsearch Scaling
16

17. 17
Elasticsearch Resiliency/HA
PUT /cortex_ocs_data
{
"settings" : {
"index.default_pipeline": "cortex_data_ingest",
"index" : {
"number_of_shards" : 5,
"number_of_replicas" : 1,
"refresh_interval" : "10s"
}
},
"mappings": {
"properties": {
"@timestamp": {"type": "date"},
"event_id": {"type": "keyword"},
"event_type": {"type": "keyword"},
"caller_party_number": {"type": "keyword"},
"called_party_number": {"type": "keyword"},
"duration": {"type": "long"}
}
}
}

18. 18
Elasticsearch CCR/DR

19. 19
Elasticsearch CCR/DR

20. 20
Elasticsearch CCR/DR

21. 21
Elasticsearch CCR/DR

22. 22
Analytics for The Network
Dominate Telco Deployments
In order to understand where vendors are gaining traction with
products which included Advanced analytics, AI and Automation
(A3), it is useful to look beyond the vendor PowerPoint ☺ - at
published case studies and press releases.
Main Findings
45% of the press releases/case studies related to the network
36% specifically to network management and assurance
The relative maturity of using ML in campaign management and the
reduction in hype around consumer chatbots meant that there was less
case study materials seen in this 2018/19 analysis period than might be
expected.
Increased activity was seen in field service workforce management, the
contact center and digital channels.
Source : https://charlottepatrick.uk/most-popular-a3-projects-in-2018-19/

23. 23
Beats (Logs & Security Analytics)
Audit data Auditbeat
Log files Filebeat
Cloud data Functionbeat
Availability Heartbeat
Systemd Linux journals Journalbeat
Metrics Metricbeat
Network traffic Packetbeat
Windows event logs Winlogbeat

24. 24
Logstash Input

25. 25
Logstash Filter

26. 26
Logstash Output

27. 27
Logstash Pipeline