Syslog and Microsoft Fabric Integration
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Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
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Input and output integration overview
The Syslog plugin enables the collection of syslog messages from various sources using standard networking protocols. This functionality is critical for environments where systems need to be monitored and logged efficiently.
The Microsoft Fabric plugin writes metrics to Real time analytics in Fabric services, enabling powerful data storage and analysis capabilities.
Integration details
Syslog
The Syslog plugin for Telegraf captures syslog messages transmitted over various protocols such as TCP, UDP, and TLS. It supports both RFC 5424 (the newer syslog protocol) and the older RFC 3164 (BSD syslog protocol). This plugin operates as a service input, effectively starting a service that listens for incoming syslog messages. Unlike traditional plugins, service inputs may not function with standard interval settings or CLI options like --once
. It includes options for setting network configurations, socket permissions, message handling, and connection handling. Furthermore, the integration with Rsyslog allows forwarding of logging messages, making it a powerful tool for collecting and relaying system logs in real-time, thus seamlessly integrating into monitoring and logging systems.
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
Syslog
[[inputs.syslog]]
## Protocol, address and port to host the syslog receiver.
## If no host is specified, then localhost is used.
## If no port is specified, 6514 is used (RFC5425#section-4.1).
## ex: server = "tcp://localhost:6514"
## server = "udp://:6514"
## server = "unix:///var/run/telegraf-syslog.sock"
## When using tcp, consider using 'tcp4' or 'tcp6' to force the usage of IPv4
## or IPV6 respectively. There are cases, where when not specified, a system
## may force an IPv4 mapped IPv6 address.
server = "tcp://127.0.0.1:6514"
## Permission for unix sockets (only available on unix sockets)
## This setting may not be respected by some platforms. To safely restrict
## permissions it is recommended to place the socket into a previously
## created directory with the desired permissions.
## ex: socket_mode = "777"
# socket_mode = ""
## Maximum number of concurrent connections (only available on stream sockets like TCP)
## Zero means unlimited.
# max_connections = 0
## Read timeout (only available on stream sockets like TCP)
## Zero means unlimited.
# read_timeout = "0s"
## Optional TLS configuration (only available on stream sockets like TCP)
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Enables client authentication if set.
# tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]
## Maximum socket buffer size (in bytes when no unit specified)
## For stream sockets, once the buffer fills up, the sender will start
## backing up. For datagram sockets, once the buffer fills up, metrics will
## start dropping. Defaults to the OS default.
# read_buffer_size = "64KiB"
## Period between keep alive probes (only applies to TCP sockets)
## Zero disables keep alive probes. Defaults to the OS configuration.
# keep_alive_period = "5m"
## Content encoding for message payloads
## Can be set to "gzip" for compressed payloads or "identity" for no encoding.
# content_encoding = "identity"
## Maximum size of decoded packet (in bytes when no unit specified)
# max_decompression_size = "500MB"
## Framing technique used for messages transport
## Available settings are:
## octet-counting -- see RFC5425#section-4.3.1 and RFC6587#section-3.4.1
## non-transparent -- see RFC6587#section-3.4.2
# framing = "octet-counting"
## The trailer to be expected in case of non-transparent framing (default = "LF").
## Must be one of "LF", or "NUL".
# trailer = "LF"
## Whether to parse in best effort mode or not (default = false).
## By default best effort parsing is off.
# best_effort = false
## The RFC standard to use for message parsing
## By default RFC5424 is used. RFC3164 only supports UDP transport (no streaming support)
## Must be one of "RFC5424", or "RFC3164".
# syslog_standard = "RFC5424"
## Character to prepend to SD-PARAMs (default = "_").
## A syslog message can contain multiple parameters and multiple identifiers within structured data section.
## Eg., [id1 name1="val1" name2="val2"][id2 name1="val1" nameA="valA"]
## For each combination a field is created.
## Its name is created concatenating identifier, sdparam_separator, and parameter name.
# sdparam_separator = "_"
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
Syslog
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Centralized Log Management: Use the Syslog plugin to aggregate log messages from multiple servers into a central logging system. This setup can help in monitoring overall system health, troubleshooting issues effectively, and maintaining audit trails by collecting syslog data from different sources.
-
Real-Time Alerting: Integrate the Syslog plugin with alerting tools to trigger real-time notifications when specific log patterns or errors are detected. For example, if a critical system error appears in the logs, an alert can be sent to the operations team, minimizing downtime and performing proactive maintenance.
-
Security Monitoring: Leverage the Syslog plugin for security monitoring by capturing logs from firewalls, intrusion detection systems, and other security devices. This logging capability enhances security visibility and helps in investigating potentially malicious activities by analyzing the captured syslog data.
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Application Performance Tracking: Utilize the Syslog plugin to monitor application performance by collecting logs from various applications. This integration helps in analyzing the application’s behavior and performance trends, thus aiding in optimizing application processes and ensuring smoother operation.
Microsoft Fabric
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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.
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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.
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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.
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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.
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
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