Kron Telemetry Pipeline offers ready-to-use functions such as filtering, deduplication, sampling, and aggregation to reduce log volume before logs reach destinations like observability and SIEM tools. By selectively filtering incoming data based on predefined criteria, the pipeline can discard unnecessary or low-value data early on, thus reducing the processing overhead and storage costs associated with storing irrelevant information.
Discard low-value log while it is in flight
Centrally manage your IT and security logs and eliminate low-value data to take control of your cost
Convert Logs to Metric and Aggregate Metrics
Extract metrics and eliminate noise in the log data without ingesting to analytical platforms
Utilize Storage Tiering
Send your data to the most cost-effective destinations, optimize costs on licensing and infrastructure. Adopt data management strategy which utilizes a number of data storage techniques which are fit for purpose
Condense log events into single event
Kron Telemetry Pipeline can condense multiple log events into a single, comprehensive event, effectively aggregating various related entries into a unified format. This feature simplifies the analysis and monitoring processes by reducing the number of events that need to be processed and reviewed, thereby enhancing efficiency and clarity in data handling and visualization.
Implementing functions like throttling, filtering, and reduction in a telemetry pipeline can significantly contribute to cost control measures. Throttling regulates the data flow by limiting the rate at which data is processed or transmitted, ensuring that resources are used efficiently and preventing the overloading of downstream systems. By selectively filtering incoming data based on predefined criteria, unnecessary or low-value data can be discarded early in the pipeline, thus reducing processing overhead and storage costs associated with storing irrelevant information.
Moreover, employing reduction functions facilitates the aggregation and summarization of data, condensing large volumes of raw telemetry into more manageable and meaningful insights. By reducing the granularity of data while preserving essential metrics, organizations can optimize storage utilization and reduce the computational burden on analytics systems, thereby minimizing infrastructure costs. Together, these functions enable organizations to strike a balance between capturing valuable telemetry data and managing operational expenses effectively, ultimately enhancing cost control efforts within the telemetry pipeline.