In a complex media environment, raw data is only useful if you can connect the dots. TAG’s Data Integration & Aggregation capability is the backbone that turns probing and monitoring into strategic insight. As streams are monitored, TAG collects metrics such as latency, content matching results, A/V sync, error events, SCTE triggers, alarms, and system state. This data is centrally ingested and correlated across all managed TAG instances.
From there, it can be pushed to third-party analytics platforms (e.g., Grafana, Kibana) or data warehouses, enabling visualization of trends, anomaly detection, and advanced reporting. Integration with external systems like NMS or orchestration layers (for example, TAG & Skyline DataMiner) allows TAG’s health and media metrics to become part of unified operational dashboards.
As historic data accumulates, teams can perform deeper analysis- such as comparing performance across times of day, correlating error events with network behavior, or tuning configurations based on observed patterns. Because TAG already probes at every key point in the workflow, this unified data layer offers high-fidelity insight that drives smarter scaling, preventative maintenance, and performance optimization.
By embedding data aggregation and integration, TAG moves beyond just monitoring: you gain a data engine that supports continuous improvement, operational intelligence, and future-proof decision making.
Technical Overview
TAG’s Data Integration & Aggregation engine is built into the Realtime Media Platform, providing full access to live and historical operational data through a combination of API 5.0, Redis, Kafka, and RESTful endpoints. Every monitored stream – whether compressed, uncompressed, or OTT – generates continuous telemetry including video, audio, metadata, and system performance metrics, collected from more than 500 probing parameters across all TAG instances.
This data layer normalizes, timestamps, and correlates diverse input types such as SCTE-35/104 triggers, content matching fingerprints, SSIM quality metrics, HDR metadata, and adaptive monitoring states. Once aggregated, the data can be exported or visualized through open connectors supporting Grafana, Kibana, and DataMiner, or integrated into third-party NMS, SIEM, and orchestration environments.
To ensure scalability, TAG uses Redis-based streaming databases and Kafka message buses for real-time data transfer, allowing millions of data points per second to be processed without performance degradation. Collected data is indexed by stream ID, service type, and time window, enabling fine-grained correlation between content, probe state, and error events.
All telemetry is accessible through the open JSON API, with built-in schema definitions for automation and query integration. MCS acts as the orchestration layer, consolidating data from multiple MCM9000 nodes and aligning metrics across distributed workflows.
This architecture transforms TAG’s monitoring platform into a data-centric intelligence layer, capable of driving automated decision-making, predictive analysis, and cross-domain visualization – turning every monitored signal into actionable insight.
