AWS Data Firehose and Nebius Cloud Monitoring 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.

This plugin allows users to effortlessly send aggregated metrics to Nebius Cloud Monitoring, leveraging the cloud’s monitoring solutions.

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.

Nebius Cloud Monitoring

The Nebius Cloud Monitoring plugin serves as an intermediary to send custom metrics to the Nebius Cloud Monitoring service. It is designed specifically to facilitate the monitoring of applications and services running within the Nebius ecosystem. This plugin is especially useful for users of the Nebius Cloud Platform who need to leverage cloud-based monitoring capabilities without significant configuration overhead. The plugin’s integration relies on Google Cloud metadata, allowing it to automatically fetch the necessary authentication credentials from the Compute instance it operates within. Key technical considerations include the management of reserved labels to ensure metrics are recorded correctly without conflicts.

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"

Nebius Cloud Monitoring

[[outputs.nebius_cloud_monitoring]]
  ## Timeout for HTTP writes.
  # timeout = "20s"

  ## Nebius.Cloud monitoring API endpoint. Normally should not be changed
  # endpoint = "https://monitoring.api.il.nebius.cloud/monitoring/v2/data/write"

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.

Nebius Cloud Monitoring

1. Dynamic Application Monitoring: Integrate this plugin with your application to continuously send metrics related to resource usage, such as CPU and memory utilization, to Nebius Cloud Monitoring. By doing so, you can gain insights into the performance of your application, allowing for adjustments in real-time based on the metrics received.

2. Incident Response Automation: Use the Nebius Cloud Monitoring plugin to automatically send alerts and metrics when certain thresholds are reached. For instance, if a particular service’s uptime drops below a certain percentage, the plugin can be configured to report this directly to the monitoring service, enabling quicker incident response and resolution.

3. Comparative Service Analysis: Set up the plugin to send metrics from multiple cloud instances running different versions of the same application to Nebius Cloud Monitoring. This approach allows for a comparative analysis of resource usage and performance, helping teams determine which version performs best under similar workloads.

4. Aggregated Metrics Dashboard: Use this plugin to create a centralized dashboard displaying metrics from various services across your cloud instances. By aggregating different application metrics into one interface, stakeholders can assess the overall health and performance of their cloud environment easily.

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