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Creating CloudWatch metric streams is an integral part of monitoring and logging in AWS, especially for those preparing for the AWS Certified DevOps Engineer – Professional exam. CloudWatch Metric Streams is a feature that allows you to continuously stream CloudWatch metrics to Amazon S3, Amazon Kinesis Data Firehose, or other destinations.
To set up CloudWatch metric streams, follow the steps outlined below.
Navigate to the Amazon CloudWatch console in your AWS Management Console.
Choose the output format for your metrics (for example, OpenTelemetry 0.7).
Review your settings and create the metric stream.
{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: “s3:PutObject”,
“Resource”: “arn:aws:s3:::your-bucket-name/*”
}
]
}
After selecting and configuring your delivery stream, review your settings and create the metric stream.
{
“Version”: “2012-10-17”,
“Statement”: [
{
“Effect”: “Allow”,
“Action”: “firehose:PutRecordBatch”,
“Resource”: “arn:aws:firehose:region:account-id:deliverystream/your-deliverystream-name”
}
]
}
Feature | Amazon S3 | Amazon Kinesis Data Firehose |
---|---|---|
Storage | Used for long-term storage of metric data. | Typically used to enable real-time processing of metric data before storage. |
Processing | Metrics are stored as-is without further processing. | Provides options to transform, batch, compress, and encrypt data before loading. |
Integration | Ideal for use with AWS Athena for querying large volumes of data. | Can be used with Amazon Redshift, Amazon Elasticsearch, and other AWS services for real-time analytics. |
Durability | Provides 99.999999999% (11 9’s) of data durability. | Transient storage before delivery to the final destination. |
Latency | Higher latency, not suitable for real-time monitoring. | Lower latency suitable for near real-time use cases. |
Metric streams are a powerful tool for DevOps engineers to continuously collect and route metrics, which enables detailed observability and operational excellence. Whether using Amazon S3 for durable long-term storage or Amazon Kinesis Data Firehose for real-time data processing, AWS provides versatile options for managing metrics at scale. Understanding these services and how to use them effectively is instrumental to success in the AWS Certified DevOps Engineer – Professional exam.
Answer: B) False
Explanation: CloudWatch metric streams can be configured to deliver metrics to several destinations, including Amazon S3, Amazon Kinesis Data Firehose, and more.
Answer: A) 1 second
Explanation: Metric streams can stream metrics with a frequency as high as once a second.
Answer: D) All of the above
Explanation: When setting up a metric stream, you can filter by namespaces, dimensions, and metric names to refine the data you want to include.
Answer: D) None, Kinesis Data Firehose is a direct destination option
Explanation: Amazon Kinesis Data Firehose can be directly used as a destination without needing an intermediate service when setting up CloudWatch metric streams.
Answer: B) False
Explanation: Metric streams only include real-time data after the stream is created; historical data is not streamed.
Answer: B) cloudwatch:PutMetricStream
Explanation: The permission required to create a CloudWatch metric stream is cloudwatch:PutMetricStream.
Answer: B) False
Explanation: CloudWatch metric stream filters can be updated after creation to change the included or excluded metrics.
Answer: A) Amazon Kinesis Data Analytics
Explanation: CloudWatch metric streams can directly target Amazon Kinesis Data Firehose and Amazon S3, but not Amazon Kinesis Data Analytics or Amazon Redshift.
Answer: A) True
Explanation: When CloudWatch metric stream data is sent to Amazon S3, there is an option to enable data compression to save on storage costs.
Answer: A) JSON
Explanation: Metric streams data is delivered in a JSON format to the destination.
Answer: A) True
Explanation: Before modifying the destination of a CloudWatch metric stream, you must first stop the stream.
Answer: A) Amazon Elasticsearch Service
Explanation: Kinesis Data Firehose can deliver data to services like Amazon Elasticsearch Service, Amazon S3, Amazon Redshift, and Splunk. It cannot directly deliver data to an EC2 instance, a physical server, or Amazon DynamoDB as final destinations.
AWS CloudWatch Metric Streams are a feature that allows you to continuously stream CloudWatch metrics to other services like Amazon S3, Amazon Kinesis Data Firehose, or any other HTTP endpoint. Unlike standard CloudWatch metrics, which are pulled from AWS services on demand, Metric Streams provide a near real-time feed of metrics, which is useful for scalable and efficient metric analysis and storage.
CloudWatch Metric Streams offer several benefits over traditional metric polling mechanisms. First, they reduce the latency in metric data availability, offering near real-time delivery of metrics. Second, they reduce the overhead on both the service sending the metrics and the receiving service due to the continuous, automated delivery of metric data. Third, they simplify the architecture for large-scale metric analysis and monitoring, as they decouple metric ingestion from metric analysis.
Common use cases for integrating CloudWatch Metric Streams with Amazon S3 or Kinesis Data Firehose include long-term metric data storage, detailed analytics and insight generation using big data tools or custom analysis, real-time alerting and event-driven architectures, and feeding metric data into third-party monitoring and analysis solutions.
When creating or updating a CloudWatch Metric Stream, you can specify a filter that includes or excludes specific metrics or namespaces by using the “IncludeFilter” or “ExcludeFilter” options. You can define these filters based on metric namespaces, metric names, or other dimensions to refine which metrics are streamed.
Security considerations include ensuring that the IAM role assigned to the stream has the least privilege necessary to perform its function, securing the data in transit using encryption (like AWS KMS), and implementing access control to the destination (S3 bucket or Kinesis Data Firehose stream) using resource-based policies, bucket policies, or stream-level permissions to prevent unauthorized access.
To ensure minimal data loss, you should set up dead-letter queues (DLQs) for the Kinesis Data Firehose delivery stream, enable Kinesis Data Firehose backup in Amazon S3, implement retries for failed data processing or batch handling, and leverage alarm notifications or other mechanisms to alert for failure conditions so that they can be addressed promptly.
AWS Lambda can be integrated with Metric Streams by attaching a Lambda function to a Kinesis Data Firehose delivery stream as a data transformer. The Lambda function can parse, filter, or transform the streaming metric data in real-time before the data is sent to the final destination, such as Amazon S3, for further processing or storage.
CloudWatch Metric Streams support multiple formats for output: OpenTelemetry and JSON. Your choice of format will affect downstream processing, as different systems and tools might prefer or directly support a particular format. JSON is widely supported and might be easier to integrate with many services, whereas OpenTelemetry is a newer standard that provides a high-fidelity format for observability data, which might be preferred for certain modern monitoring tools.
Backfilling in CloudWatch Metric Streams refers to the process of including historical data from before the stream’s creation in the stream’s output. This can be useful when you want to perform retrospective analysis or have a complete dataset from a certain point in time. When creating a stream, you can specify a “start time” for the Metric Stream to begin backfilling data from.
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