AWS Data Firehose and Microsoft Fabric Integration

Input and output integration overview

This plugin listens for metrics sent via HTTP from AWS Data Firehose in supported data formats, providing real-time data ingestion capabilities.

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

Integration details

AWS Data Firehose

The AWS Data Firehose Telegraf plugin is designed to receive metrics from AWS Data Firehose via HTTP. This plugin listens for incoming data in various formats and processes it according to the request-response schema outlined in the official AWS documentation. Unlike standard input plugins that operate on a fixed interval, this service plugin initializes a listener that remains active, waiting for incoming metrics. This allows for real-time data ingestion from AWS Data Firehose, making it suitable for scenarios where immediate data processing is required. Key features include the ability to specify service addresses, paths, and support for TLS connections for secure data transmission. Additionally, the plugin accommodates optional authentication keys and custom tags, enhancing its flexibility in various use cases involving data streaming and processing.

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

AWS Data Firehose

[[inputs.firehose]]
  ## Address and port to host HTTP listener on
  service_address = ":8080"

  ## Paths to listen to.
  # paths = ["/telegraf"]

  ## maximum duration before timing out read of the request
  # read_timeout = "5s"
  ## maximum duration before timing out write of the response
  # write_timeout = "5s"

  ## Set one or more allowed client CA certificate file names to
  ## enable mutually authenticated TLS connections
  # tls_allowed_cacerts = ["/etc/telegraf/clientca.pem"]

  ## Add service certificate and key
  # tls_cert = "/etc/telegraf/cert.pem"
  # tls_key = "/etc/telegraf/key.pem"

  ## Minimal TLS version accepted by the server
  # tls_min_version = "TLS12"

  ## Optional access key to accept for authentication.
  ## AWS Data Firehose uses "x-amz-firehose-access-key" header to set the access key.
  ## If no access_key is provided (default), authentication is completely disabled and
  ## this plugin will accept all request ignoring the provided access-key in the request!
  # access_key = "foobar"

  ## Optional setting to add parameters as tags
  ## If the http header "x-amz-firehose-common-attributes" is not present on the
  ## request, no corresponding tag will be added. The header value should be a
  ## json and should follow the schema as describe in the official documentation:
  ## https://docs.aws.amazon.com/firehose/latest/dev/httpdeliveryrequestresponse.html#requestformat
  # parameter_tags = ["env"]

  ## Data format to consume.
  ## Each data format has its own unique set of configuration options, read
  ## more about them here:
  ## https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_INPUT.md
  # data_format = "influx"

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

AWS Data Firehose

1. Real-Time Data Analytics: Using the AWS Data Firehose plugin, organizations can stream data in real-time from various sources, such as application logs or IoT devices, directly into analytics platforms. This allows data teams to analyze incoming data as it is generated, enabling rapid insights and operational adjustments based on fresh metrics.

2. Profile Access Patterns for Optimization: By collecting data about how clients interact with applications through AWS Data Firehose, businesses can gain valuable insights into user behavior. This can drive content personalization strategies or optimize server architecture for better performance based on traffic patterns.

3. Automated Alerting Mechanism: Integrating AWS Data Firehose with alerting systems via this plugin allows teams to set up automated alerts based on specific metrics collected. For example, if a particular threshold is reached in the input data, alerts can trigger operations teams to investigate potential issues before they escalate.

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.

Feedback

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