Data Warehouse

for Telecom company

Overview

Our telecom client manages its own fiber optic infrastructure both from an operational and business standpoint. The company aspires to be a data-driven organization, so it is crucial to ensure consistency and high quality of data as well as the ability to conduct various analyses.

Overview

Goal

The business objective is to provide the client with a single cloud platform that enables the storage and transformation of data from various sources in a consistent and organized manner. It is also important to generate easy and convenient reports for various teams, such as production or sales.

Solution

The scope of work included analyzing what the client’s infrastructure looked like, proposing a solution, implementing and delivering it.

01

The company did not have a single system or process that organized and maintained data systematically. Additionally, the data came from many different sources, which were handled differently by each team. As a result, there were discrepancies, and the company could not use it for prediction.

02

Our solution included the creation of a data warehouse and a platform for technical and sales analysis.

Before we started building a data warehouse, we designed the data model. Once our data model was ready we needed to extract data from various sources and load it into a data warehouse. Airflow was used to schedule and automate data extraction and loading tasks, while we leveraged BigQuery as the storage engine.

03

The next step was to build ETL processes. We used Airflow to build and manage data pipelines that automate the process of extracting, loading, and transforming data. The goal was to feed the model so that it could create visualizations and predictions based on the data.

During the project we also supported our client in the process of migrating their solution from on-premises to the cloud, leveraging GCP. Such a move unlocked the possibilities of our client’s scalability.

02

Our solution included the creation of a data warehouse and a platform for technical and sales analysis.

Before we started building a data warehouse, we designed the data model. Once our data model was ready we needed to extract data from various sources and load it into a data warehouse. Airflow was used to schedule and automate data extraction and loading tasks, while we leveraged BigQuery as the storage engine.

Results

A huge benefit that this project brought to our client is improved risk management. By analyzing data, the company can proactively identify and address potential risks to its business. By analyzing network performance data, the company can find areas of high demand and potential network issues. Our client can therefore plan network expansions, upgrades, and maintenance activities more effectively.

Also data-insights helps spot inefficiencies and bottlenecks in its processes. With this information, the company can optimize its operations, reduce costs, and improve service quality.