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Bouygues Telecom

CASE - PREDICTION OF ANOMALIES

Data Factory/

We're proud
of our Work!

CUSTOMER
SKILLS AND ROLES

A service centre of 9 people
1 project manager
4 developers (including 1 manager)
4 DevOps (including 1 leader)

THE TECHNICAL FRAMEWORK

Java, Spark, Tenserflow, HDFS, ElasticSearch, Kafka, Hive, Cassandra, Hortonworks, Docker, Ansible, Kubernetes.

KEY FIGURES
5
PETAOCTETS

of aggregated data

77
NODES

deployed

150k
EVENTS/SEC.

on the real time part

OUR CHALLENGE

Supporting the IT department of Bouygues Telecom to improve the QOS by detecting and predicting anomalies using big data.

 

THE CONTEXT

In order to improve the quality of service, our teams aggregate, transform and restore data from all Bouygues network equipment (internet boxes, smartphones, routers, etc.).
This data is then used in two different ways: for real-time dashboards or via BI tools and machine learning algorithms (for anomaly prediction).

 

THE PROJECT

We are responsible for all phases of the project (monitoring, design, implementation and maintenance) and make recommendations that are validated by the client.
As the infrastructure is on site, we control our infrastructure down to the last detail and install the management and automation solutions ourselves.

Our experts

Thomas Leger

Alexis Lustenberger