Specialized IT services provider
Collects and monitors data on transcoding and online broadcasting
DevOps engineers covering quality assurance, system design maintenance, and infrastructure support
A long time ago, the client solution made use of a DevOps system to aggregate data on transcoding processes and online broadcasts. However, it proved vulnerable to cyberattacks and was eventually hacked. Determined to enhance their security, the company intended to replace it with an advanced data monitoring and aggregation solution that would respond to their data security and resistance system requirements.
Once upon a time, the DevOps heroes received an urgent call for help from a distressed client.
Determined to restore their trust with clients, the DevOps hero embarked on a mission to revamp the system and make it more reliable and secure. The team devised a brilliant strategy:
The team began with designing an analytical monitoring system that seamlessly aggregated data from various sources into several local data centers. This allowed for centralized and efficient management of critical metrics such as bitrate, memory usage, CPU performance, state, and device status.
But the team didn’t stop there. They recognized the power of analytics in driving proactive decision-making. To achieve this, they integrated an advanced analytics system capable of analyzing current broadcasting metrics to foresee potential issues. For example, they could predict transcoding errors that might occur in a month based on early indicators such as an increasing error rate.
After carefully considering the options, we decided to go with VictoriaMetrics instead of Prometheus in Grafana. This decision was based on the following factors:
For both data ingestion and data querying on vertical and horizontal levels
To restore the data when real-time tracking may occasionally be unavailable
Proven by security certifications for Database Software Development and Software-Based Monitoring Services
SD/HD-quality channels are monitored to ensure seamless transcoding processes
Reduction in the monthly amount of big data stored within a system
Decrease in regular issue requests from end users in real time
