AWS Data Firehose and Clarify 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 Clarify plugin allows users to publish Telegraf metrics directly to Clarify, enabling enhanced analysis and monitoring 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.

Clarify

This plugin facilitates the writing of Telegraf metrics to Clarify, a platform for managing and analyzing time series data. By transforming metrics into Clarify signals, this output plugin enables seamless integration of collected telemetry data into the Clarify ecosystem. Users must obtain valid credentials, either through a credentials file or basic authentication, to configure the plugin. The configuration also provides options for fine-tuning how metrics are mapped to signals in Clarify, including the ability to specify unique identifiers using tags. Given that Clarify supports only floating point values, the plugin ensures that any unsupported types are effectively filtered out during the publishing process. This comprehensive connectivity aligns with use cases in monitoring, data analysis, and operational insights.

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"

Clarify

[[outputs.clarify]]
  ## Credentials File (Oauth 2.0 from Clarify integration)
  credentials_file = "/path/to/clarify/credentials.json"

  ## Clarify username password (Basic Auth from Clarify integration)
  username = "i-am-bob"
  password = "secret-password"

  ## Timeout for Clarify operations
  # timeout = "20s"

  ## Optional tags to be included when generating the unique ID for a signal in Clarify
  # id_tags = []
  # clarify_id_tag = 'clarify_input_id'

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.

Clarify

1. Automated Data Monitoring: By integrating the Clarify plugin with sensor data collection, organizations can automate the monitoring of environmental conditions, such as temperature and humidity. The plugin processes metrics in real-time, sending updates to Clarify where they can be analyzed for trends, alerts, and historical tracking. This use case makes it easier to maintain optimal conditions in data centers or production environments, reducing the risk of equipment failures.

2. Performance Metrics Analysis: Companies can leverage this plugin to send application performance metrics to Clarify. By transmitting key indicators such as response times and error rates, developers and operations teams can utilize Clarify’s capabilities to visualize and analyze application performance over time. This insight can drive improvements in user experience and help identify areas in need of optimization.

3. Sensor Data Aggregation: Utilizing the plugin to push data from multiple sensors to Clarify allows for a comprehensive view of physical environments. This aggregation is particularly beneficial in sectors such as agriculture, where metrics from various sensors can be correlated to decision-making about resource allocations, pest control, and crop management. The plugin ensures the data is accurately mapped and transformed for effective analysis.

4. Real-Time Alerts and Notifications: Implement the Clarify plugin to trigger real-time alerts based on predefined thresholds within the metrics being sent. For instance, if temperature readings exceed certain levels, alerts can be generated and sent to operational staff. This proactive approach allows for immediate responses to potential issues, enhancing operational reliability and safety.

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.