Azure Event Hubs and Datadog Integration

Input and output integration overview

The Azure Event Hubs Input Plugin allows Telegraf to consume data from Azure Event Hubs and Azure IoT Hub, enabling efficient data processing and monitoring of event streams from these cloud services.

The Datadog Telegraf Plugin enables the submission of metrics to the Datadog Metrics API, facilitating efficient monitoring and data analysis through a reliable metric ingestion process.

Integration details

Azure Event Hubs

This plugin serves as a consumer for Azure Event Hubs and Azure IoT Hub, allowing users to ingest data streams from these platforms efficiently. Azure Event Hubs is a highly scalable data streaming platform and event ingestion service capable of receiving and processing millions of events per second, while Azure IoT Hub enables secure device-to-cloud and cloud-to-device communication in IoT applications. The Event Hub Input Plugin interacts seamlessly with these services, providing reliable message consumption and stream processing capabilities. Key features include dynamic management of consumer groups, message tracking to prevent data loss, and customizable settings for prefetch counts, user agents, and metadata handling. This plugin is designed to support a range of use cases, including real-time telemetry data collection, IoT data processing, and integration with various data analysis and monitoring tools within the broader Azure ecosystem.

Datadog

This plugin writes to the Datadog Metrics API, enabling users to send metrics for monitoring and performance analysis. By utilizing the Datadog API key, users can configure the plugin to establish a connection with Datadog’s v1 API. The plugin supports various configuration options including connection timeouts, HTTP proxy settings, and data compression methods, ensuring adaptability to different deployment environments. The ability to transform count metrics into rates enhances the integration of Telegraf with Datadog agents, particularly beneficial for applications that rely on real-time performance metrics.

Configuration

Azure Event Hubs

[[inputs.eventhub_consumer]]
  ## The default behavior is to create a new Event Hub client from environment variables.
  ## This requires one of the following sets of environment variables to be set:
  ##
  ## 1) Expected Environment Variables:
  ##    - "EVENTHUB_CONNECTION_STRING"
  ##
  ## 2) Expected Environment Variables:
  ##    - "EVENTHUB_NAMESPACE"
  ##    - "EVENTHUB_NAME"
  ##    - "EVENTHUB_KEY_NAME"
  ##    - "EVENTHUB_KEY_VALUE"

  ## 3) Expected Environment Variables:
  ##    - "EVENTHUB_NAMESPACE"
  ##    - "EVENTHUB_NAME"
  ##    - "AZURE_TENANT_ID"
  ##    - "AZURE_CLIENT_ID"
  ##    - "AZURE_CLIENT_SECRET"

  ## Uncommenting the option below will create an Event Hub client based solely on the connection string.
  ## This can either be the associated environment variable or hard coded directly.
  ## If this option is uncommented, environment variables will be ignored.
  ## Connection string should contain EventHubName (EntityPath)
  # connection_string = ""

  ## Set persistence directory to a valid folder to use a file persister instead of an in-memory persister
  # persistence_dir = ""

  ## Change the default consumer group
  # consumer_group = ""

  ## By default the event hub receives all messages present on the broker, alternative modes can be set below.
  ## The timestamp should be in https://github.com/toml-lang/toml#offset-date-time format (RFC 3339).
  ## The 3 options below only apply if no valid persister is read from memory or file (e.g. first run).
  # from_timestamp =
  # latest = true

  ## Set a custom prefetch count for the receiver(s)
  # prefetch_count = 1000

  ## Add an epoch to the receiver(s)
  # epoch = 0

  ## Change to set a custom user agent, "telegraf" is used by default
  # user_agent = "telegraf"

  ## To consume from a specific partition, set the partition_ids option.
  ## An empty array will result in receiving from all partitions.
  # partition_ids = ["0","1"]

  ## Max undelivered messages
  ## This plugin uses tracking metrics, which ensure messages are read to
  ## outputs before acknowledging them to the original broker to ensure data
  ## is not lost. This option sets the maximum messages to read from the
  ## broker that have not been written by an output.
  ##
  ## This value needs to be picked with awareness of the agent's
  ## metric_batch_size value as well. Setting max undelivered messages too high
  ## can result in a constant stream of data batches to the output. While
  ## setting it too low may never flush the broker's messages.
  # max_undelivered_messages = 1000

  ## Set either option below to true to use a system property as timestamp.
  ## You have the choice between EnqueuedTime and IoTHubEnqueuedTime.
  ## It is recommended to use this setting when the data itself has no timestamp.
  # enqueued_time_as_ts = true
  # iot_hub_enqueued_time_as_ts = true

  ## Tags or fields to create from keys present in the application property bag.
  ## These could for example be set by message enrichments in Azure IoT Hub.
  # application_property_tags = []
  # application_property_fields = []

  ## Tag or field name to use for metadata
  ## By default all metadata is disabled
  # sequence_number_field = "SequenceNumber"
  # enqueued_time_field = "EnqueuedTime"
  # offset_field = "Offset"
  # partition_id_tag = "PartitionID"
  # partition_key_tag = "PartitionKey"
  # iot_hub_device_connection_id_tag = "IoTHubDeviceConnectionID"
  # iot_hub_auth_generation_id_tag = "IoTHubAuthGenerationID"
  # iot_hub_connection_auth_method_tag = "IoTHubConnectionAuthMethod"
  # iot_hub_connection_module_id_tag = "IoTHubConnectionModuleID"
  # iot_hub_enqueued_time_field = "IoTHubEnqueuedTime"

  ## 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"

Datadog

[[outputs.datadog]]
  ## Datadog API key
  apikey = "my-secret-key"

  ## Connection timeout.
  # timeout = "5s"

  ## Write URL override; useful for debugging.
  ## This plugin only supports the v1 API currently due to the authentication
  ## method used.
  # url = "https://app.datadoghq.com/api/v1/series"

  ## Set http_proxy
  # use_system_proxy = false
  # http_proxy_url = "http://localhost:8888"

  ## Override the default (none) compression used to send data.
  ## Supports: "zlib", "none"
  # compression = "none"

  ## When non-zero, converts count metrics submitted by inputs.statsd
  ## into rate, while dividing the metric value by this number.
  ## Note that in order for metrics to be submitted simultaenously alongside
  ## a Datadog agent, rate_interval has to match the interval used by the
  ## agent - which defaults to 10s
  # rate_interval = 0s

Input and output integration examples

Azure Event Hubs

1. Real-Time IoT Device Monitoring: Use the Azure Event Hubs Plugin to monitor telemetry data from IoT devices like sensors and actuators. By streaming device data into monitoring dashboards, organizations can gain insights into system performances, track usage patterns, and quickly respond to irregularities. This setup allows for proactive management of devices, improving operational efficiency and reducing downtime.

2. Event-Driven Data Processing Workflows: Leverage this plugin to trigger data processing workflows in response to events received from Azure Event Hubs. For instance, when a new event arrives, it can initiate data transformation, aggregation, or storage processes, allowing businesses to automate their workflows more effectively. This integration enhances responsiveness and streamlines operations across systems.

3. Integration with Analytics Platforms: Implement the plugin to funnel event data into analytics platforms like Azure Synapse or Power BI. By integrating real-time streaming data into analytics tools, organizations can perform comprehensive data analysis, drive business intelligence efforts, and create interactive visualizations that inform decision-making.

4. Cross-Platform Data Sync: Utilize the Azure Event Hubs Plugin to synchronize data streams across diverse systems or platforms. By consuming data from Azure Event Hubs and forwarding it to other systems like databases or cloud storage, organizations can maintain consistent and up-to-date information across their entire architecture, enabling cohesive data strategies.

Datadog

1. Real-Time Infrastructure Monitoring: Use the Datadog plugin to monitor server metrics in real-time by sending CPU usage and memory statistics directly to Datadog. This integration allows IT teams to visualize and analyze system performance metrics in a centralized dashboard, enabling proactive response to any emerging issues, such as resource bottlenecks or server overloads.

2. Application Performance Tracking: Leverage this plugin to submit application-specific metrics, such as request counts and error rates, to Datadog. By integrating with application monitoring tools, teams can correlate infrastructure metrics with application performance, providing insights that enable them to optimize code performance and improve user experience.

3. Anomaly Detection in Metrics: Configure the Datadog plugin to send metrics that can trigger alerts and notifications based on unusual patterns detected by Datadog’s machine learning features. This proactive monitoring helps teams swiftly react to potential outages or performance degradation before customers are impacted.

4. Integrating with Cloud Services: By utilizing the Datadog plugin to send metrics from cloud resources, IT teams can gain visibility into cloud application performance. Monitoring metrics like latency and error rates helps with ensuring service-level agreements (SLAs) are met and also assists in optimizing resource allocation across cloud environments.

Feedback

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