Media Data Lake and Big Data ComputingMedia Data Lake and Big Data Computing
$100k+
Decreased infrastructure annual cost
1000+
Hours of reduced pipeline execution time
100+
Hours saved on data issues resolution with enhanced traceability
8
Years of continuous innovation and commitment
Business Objective
Nielsen, a global leader in audience measurement, media data and analytics, collecting and processing data from millions of devices, sought to streamline information delivery to its clients.
The existing solution architecture and toolset were unable to efficiently process spikes and surges in datasets. Key information was delivered to customers in a manual, labor-intensive manner.
Description
The Transformation
Exadel architects supported Nielsen’s organizational transformation to a Media Data Lake (MDL) approach—creating a unified, clean source of truth for the organization, with reporting and machine learning built on top.
Exadel’s Big Data Engineers refactored and created hundreds of data pipelines on AWS, reducing execution time from hours to minutes, while implementing cleansing rules, transformations, and aggregations on hundreds of GBs of raw data. The team used Airflow to enable traceability, notifications, and operational control of pipelines.
Exadel also built an API layer to integrate with MDL and deliver data to consumers via APIs instead of relying on partial manual processes.
Enablers
Storage, Master Data Management, Data QA
Infrastructure, Big Data Integration
Data Connectors, APIs
BI Reporting, Custom Visualizations
Monitoring & Alerting, Availability & Recovery
Incidents & Change Requests Management
Case Studies
You may also be interested in...
Real Results
Reduced infrastructure costs by $100K+ annually and cut data pipeline execution from hours to minutes, saving 1,000+ processing hours.
