Our client is a leading digital media company that is transforming mobile advertisingthrough data-driven predictive solutions.The critical need was to identify individuals across multiple devices they use, to maintaincontinuity of context while serving relevant advertisements.
- There is not just one source of data that could give all insights required for such transformation.
- Even if relevant sources and data partners are identified, defining a structuredpath from data to insights is not easy due to multiple constraints (least of which is blending the sources).
- Evaluated different graph databases for the client’s use cases considering factorssuch as query latency, big data scalability, performance tuning capability, andcost-effectiveness
- Setup graph database cluster and ingested hundreds of GBs of data
- Performed infrastructure estimation, data pre-processing, data-integrity verification, performance tuning, REST API development, and API authentication
- Deployed the graph database cluster in production using DCOS (Data CenterOperating System) with options to provide Oauth2 authentication and load balancing
- The client is able to bridge online and offline channels allowing for better-targeted ads. This allows them to command a premium from advertisers through the accurate serving of ads acrossdevice-location-time combinations at an individual level.