Cisco Model-Driven Telemetry and Microsoft Fabric Integration

Input and output integration overview

The Cisco Model-Driven Telemetry (MDT) plugin facilitates the collection of telemetry data from Cisco networking platforms, utilizing gRPC and TCP transport mechanisms. This plugin is essential for users looking to implement advanced telemetry solutions for better insights and operational efficiency.

The Microsoft Fabric plugin writes metrics to Real time analytics in Fabric services, enabling powerful data storage and analysis capabilities.

Integration details

Cisco Model-Driven Telemetry

Cisco model-driven telemetry (MDT) is designed to provide a robust means of consuming telemetry data from various Cisco platforms, including IOS XR, IOS XE, and NX-OS. This plugin focuses on the efficient transport of telemetry data using either TCP or gRPC protocols, offering flexibility based on the network environment and requirements. The gRPC transport is particularly advantageous as it supports TLS for enhanced security through encryption and authentication. The plugin is compatible with a range of software versions on Cisco devices, enabling organizations to leverage telemetry capabilities across their network operations. It is especially useful for network monitoring and analytics, as it enables real-time data collection directly from Cisco devices, enhancing visibility into network performance, resource utilization, and operational metrics.

Microsoft Fabric

This plugin allows you to leverage Microsoft Fabric’s capabilities to store and analyze your Telegraf metrics. Eventhouse is a high-performance, scalable data-store designed for real-time analytics. It allows you to ingest, store and query large volumes of data with low latency. The plugin supports both events and metrics with versatile grouping options. It provides various configuration parameters including connection strings specifying details like the data source, ingestion types, and which tables to use for storage. With support for streaming ingestion and event streams, this plugin enables seamless integration and data flow into Microsoft’s analytics ecosystem, allowing for rich data querying capabilities and near-real-time processing.

Configuration

Cisco Model-Driven Telemetry

[[inputs.cisco_telemetry_mdt]]
 ## Telemetry transport can be "tcp" or "grpc".  TLS is only supported when
 ## using the grpc transport.
 transport = "grpc"

 ## Address and port to host telemetry listener
 service_address = ":57000"

 ## Grpc Maximum Message Size, default is 4MB, increase the size. This is
 ## stored as a uint32, and limited to 4294967295.
 max_msg_size = 4000000

 ## Enable TLS; grpc transport only.
 # tls_cert = "/etc/telegraf/cert.pem"
 # tls_key = "/etc/telegraf/key.pem"

 ## Enable TLS client authentication and define allowed CA certificates; grpc
 ##  transport only.
 # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]

 ## Define (for certain nested telemetry measurements with embedded tags) which fields are tags
 # embedded_tags = ["Cisco-IOS-XR-qos-ma-oper:qos/interface-table/interface/input/service-policy-names/service-policy-instance/statistics/class-stats/class-name"]

 ## Include the delete field in every telemetry message.
 # include_delete_field = false

 ## Specify custom name for incoming MDT source field.
 # source_field_name = "mdt_source"

 ## Define aliases to map telemetry encoding paths to simple measurement names
 [inputs.cisco_telemetry_mdt.aliases]
   ifstats = "ietf-interfaces:interfaces-state/interface/statistics"
 ## Define Property Xformation, please refer README and https://pubhub.devnetcloud.com/media/dme-docs-9-3-3/docs/appendix/ for Model details.
 [inputs.cisco_telemetry_mdt.dmes]
#    Global Property Xformation.
#    prop1 = "uint64 to int"
#    prop2 = "uint64 to string"
#    prop3 = "string to uint64"
#    prop4 = "string to int64"
#    prop5 = "string to float64"
#    auto-prop-xfrom = "auto-float-xfrom" #Xform any property which is string, and has float number to type float64
#    Per Path property xformation, Name is telemetry configuration under sensor-group, path configuration "WORD         Distinguished Name"
#    Per Path configuration is better as it avoid property collision issue of types.
#    dnpath = '{"Name": "show ip route summary","prop": [{"Key": "routes","Value": "string"}, {"Key": "best-paths","Value": "string"}]}'
#    dnpath2 = '{"Name": "show processes cpu","prop": [{"Key": "kernel_percent","Value": "float"}, {"Key": "idle_percent","Value": "float"}, {"Key": "process","Value": "string"}, {"Key": "user_percent","Value": "float"}, {"Key": "onesec","Value": "float"}]}'
#    dnpath3 = '{"Name": "show processes memory physical","prop": [{"Key": "processname","Value": "string"}]}'

 ## Additional GRPC connection settings.
 [inputs.cisco_telemetry_mdt.grpc_enforcement_policy]
  ## GRPC permit keepalives without calls, set to true if your clients are
  ## sending pings without calls in-flight. This can sometimes happen on IOS-XE
  ## devices where the GRPC connection is left open but subscriptions have been
  ## removed, and adding subsequent subscriptions does not keep a stable session.
  # permit_keepalive_without_calls = false

  ## GRPC minimum timeout between successive pings, decreasing this value may
  ## help if this plugin is closing connections with ENHANCE_YOUR_CALM (too_many_pings).
  # keepalive_minimum_time = "5m"

Microsoft Fabric

[[outputs.microsoft_fabric]]
  ## The URI property of the resource on Azure
  connection_string = "https://trd-abcd.xx.kusto.fabric.microsoft.com;Database=kusto_eh;Table Name=telegraf_dump;Key=value"

  ## Client timeout
  # timeout = "30s"

Input and output integration examples

Cisco Model-Driven Telemetry

1. Real-Time Network Monitoring: Utilize the Cisco MDT plugin to collect network performance metrics from Cisco routers and switches. By feeding telemetry data into a visualization tool, network operators can observe traffic trends, bandwidth usage, and error rates in real-time. This proactive monitoring allows teams to swiftly address issues before they affect network performance, resulting in a more reliable service.

2. Automated Anomaly Detection: Integrate Cisco MDT with machine learning algorithms to create an automated anomaly detection system. By continuously analyzing telemetry data, the system can identify deviations from typical operational patterns, providing alerts for unusual conditions that may signify network problems or security threats, which can aid in maintaining operational integrity.

3. Dynamic Configuration Management: Leveraging the telemetry data collected from Cisco devices, organizations can implement dynamic configuration management solutions that automatically adjust network settings based on current performance indicators. For instance, if the telemetry indicates high utilization on certain links, the system could dynamically route traffic to underutilized paths, optimizing resource usage.

4. Enhanced Reporting and Analytics: Use the Cisco MDT plugin to feed detailed telemetry data into analytics platforms, enabling comprehensive reporting on network health and performance. Historical and real-time analysis can guide decision-making and strategic planning, helping organizations to allocate resources more effectively and understand their network’s operational landscape better.

Microsoft Fabric

1. Real-time Monitoring Dashboards: Utilize the Microsoft Fabric plugin to feed live metrics from your applications into a real-time dashboard on Microsoft Fabric. This allows teams to visualize key performance indicators instantly, enabling quick decision-making and timely responses to performance issues.

2. Automated Data Ingestion from IoT Devices: Use this plugin in scenarios where metrics from IoT devices need to be ingested into Azure for analysis. Using the plugin’s capabilities, data can be streamed continuously, facilitating real-time analytics and reporting without complex coding efforts.

3. Cross-Platform Data Aggregation: Leverage the plugin to consolidate metrics from multiple systems and applications into a single Azure Data Explorer table. This use case enables easier data management and analysis by centralizing disparate data sources within a unified analytics framework.

4. Enhanced Event Transformation Workflows: Integrate the plugin with Eventstreams to facilitate real-time event ingestion and transformation. By configuring different metrics and partition keys, users can manipulate the flow of data as it enters the system, allowing for advanced processing before the data reaches its final destination.

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