Toptal helps telecom company improve data pipelines processing 100 billion records daily.
A telecom company partnered with Toptal to maintain and strengthen complex data pipelines processing approximately 100 billion records each day.
Client
A US-based telecom analytics company measuring mobile network experience from billions of daily data points.
Employees
Revenue
Industry
Communication, Media and EntertainmentDelivered Services
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The departure of a senior developer created an immediate need for experienced data engineering support. The company initially sought a temporary replacement to maintain an important pipeline while searching for a permanent employee.
Solution
Scalable Data Engineering
Toptal provided senior expertise in Scala, Spark, AWS, Python, and Airflow to develop and maintain complex data pipelines while addressing performance, processing cost, storage, and monitoring challenges.
Stronger Data Architecture
Toptal developed processes and frameworks for pipeline development, data version management, job metadata, monitoring, and testing, while introducing code-linked documentation that was automatically published to Confluence.
Outcome
Reliable Pipeline Delivery
The engineering work supported the successful delivery of data pipelines to clients while helping the system accommodate changing data quality, increasing volumes, and evolving requirements.
Faster Development and Debugging
The improved data architecture accelerated development, made pipelines easier to debug, and provided richer data to R&D and DataOps teams, reducing the time required to investigate and respond to issues.
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