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Amazon Kinesis Data Firehose setup guideedit

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Prerequisitesedit

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  • -You have an AWS account where you can create a Firehose delivery stream. -
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  • -You have a deployment in Elastic Cloud running Elastic Stack version 7.17 or greater on AWS. -
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Limitationsedit

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    When using Elastic integrations with Firehose, only a single log type may be sent per delivery stream, e.g. VPC Flow Logs. -This is due to how Firehose records are routed into data streams in Elasticsearch.

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    It is possible to combine multiple log types in one delivery stream, but this will preclude the use of Elastic integrations (by default all Firehose logs are sent to the logs-generic-default data stream).

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Instructionsedit

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    Install the relevant integrations in Kibana.

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    In order to make the most of your data, install AWS integrations to load index templates, ingest pipelines, and dashboards into Kibana.

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    In Kibana, navigate to Management > Integrations in the sidebar.

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    Find the AWS integration by searching or browsing the catalog.

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    -Integrations catalogue with the "AWS" integration highlighted -
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    Navigate to the Settings tab and click Install AWS assets. -Confirm by clicking Install AWS in the popup.

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    -AWS integration settings page with the "Install AWS assets" button highlighted -
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    Create a delivery stream in Amazon Kinesis Data Firehose.

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    Sign into the AWS console and navigate to Amazon Kinesis. -Click Create delivery stream.

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    -Amazon Kinesis dashboard with the "Create delivery stream" button highlighted -
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    Configure the delivery stream using the following settings:

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    Choose source and destinationedit

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    Unless you are streaming data from Kinesis Data Streams, set source to Direct PUT (see Setup guide for more details on data sources).

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    Set destination to Elastic.

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    Delivery stream nameedit

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    Provide a meaningful name that will allow you to identify this delivery stream later.

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    Transform records - optionaledit

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    For advanced use cases, source records can be transformed by invoking a custom Lambda function. -When using Elastic integrations, this should not be required.

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    -Amazon Kinesis Data Firehose delivery stream settings showing 'Choose source and destination' -
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    Destination settingsedit

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    Set Elastic endpoint URL to point to your Elasticsearch cluster running in Elastic Cloud. -This endpoint can be found in the Elastic Cloud console. -An example is https://my-deployment-28u274.es.eu-west-1.aws.found.io.

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    API key should be a Base64 encoded Elastic API key, which can be created in Kibana by following the instructions under API Keys. -If you are using an API key with “Restrict privileges”, be sure to review the Indices privileges to provide at least "auto_configure" & "write" permissions for the indices you will be using with this delivery stream.

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    We recommend leaving Content encoding set to GZIP for improved network efficiency.

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    Retry duration determines how long Firehose continues retrying the request in the event of an error. -A duration of 60-300s should be suitable for most use cases.

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    Parameters:

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    The default data stream will change to logs-awsfirehose-default in January 2024. To avoid breaking changes, do not leave es_datastream_name empty. -To try the new routing functionality, set es_datastream_name to logs-awsfirehose-default.

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      You can use the es_datastream_name parameter to route documents to any data stream. -When Amazon Kinesis Data Firehose integration is installed, routing will be done automatically with es_datastream_name sets to logs-awsfirehose-default. -When using Elastic AWS integrations without the Firehose integration, you must set this parameter to specific data streams like logs-aws.vpcflow-default for ingesting VPC flow logs.

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      Elastic integrations use data streams with specific naming conventions, and Firehose records need to be routed to the relevant data stream to use preconfigured index mappings, ingest pipelines, and dashboards.

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      A separate Firehose delivery stream is required for each log type in AWS to make use of Elastic integrations.

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      The following is a list of common AWS log types and the es_datastream_name value that needs to be set to route the logs to the correct integration.

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      Cloudfront

      logs-aws.cloudfront_logs-default

      Cloudtrail

      logs-aws.cloudtrail-default

      CloudWatch

      logs-aws.cloudwatch_logs-default

      EC2 (via Cloudwatch)

      logs-aws.ec2_logs-default

      ELB

      logs-aws.elb_logs-default

      Network firewall

      logs-aws.firewall_logs-default

      Route 53 public DNS queries

      logs-aws.route53_public_logs-default

      Route 53 resolver queries

      logs-aws.route53_resolver_logs-default

      S3 server access

      logs-aws.s3access-default

      VPC Flow Logs

      logs-aws.vpcflow-default

      WAF

      logs-aws.waf-default

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      As per the data stream naming conventions, the "namespace" is a user-configurable arbitrary grouping and can be changed from default to fit your use case. For example, you may want to organize WAF Logs per environment into logs-aws.waf-production and logs-aws.waf-qa data streams for more granular control over rollover, retention, and security permissions.

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      For log types not listed above, review the relevant integration documentation to determine the correct es_datastream_name value. -The data stream components can be found in the example event for each integration.

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      -AWS WAF integration documentation showing a sample event with the data stream components highlighted -
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      The include_event_original field is optional and should only be used for debugging purposes. -When set to true, each log record will contain an additional field named event.original, which contains the raw (unprocessed) log message. -This parameter will increase the data volume in Elasticsearch and should be used with care.

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      Elastic requires a Buffer size of 1MiB to avoid exceeding the Elasticsearch http.max_content_length setting (typically 100MB) when the buffer is uncompressed.

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      The default Buffer interval of 60s is recommended to ensure data freshness in Elastic.

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      -Amazon Kinesis Data Firehose delivery stream settings showing 'Destination settings' section -
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    Backup settingsedit

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    It’s recommended to configure S3 backup for failed records. -It’s then possible to configure workflows to automatically re-try failed records, for example using Elastic Serverless Forwarder.

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    -Amazon Kinesis Data Firehose delivery stream settings showing 'Backup settings' section -
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    Whilst Firehose guarantees at-least-once delivery of data to the destination, if your data is highly sensitive, it’s also recommended to backup all records to S3 in case there are any ingest issues in Elasticsearch.

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    Send data to the Firehose delivery stream.

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    Consult the AWS documentation for details on how to configure a variety of log sources to send data to Firehose delivery streams.

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    Several services support writing data directly to delivery streams, including Cloudwatch logs. -In addition, there are other ways to create streaming data pipelines to Firehose, e.g. using AWS DMS.

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    An example workflow for sending VPC Flow Logs to Firehose would be:

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Amazon Kinesis Data Firehose troubleshootingedit

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Monitoringedit

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You can use the monitoring tab in the Firehose console to ensure there are incoming records and the delivery success rate is 100%.

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-Firehose monitoring page showing some charts of delivery success percentage and throughput -
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By default Firehose also logs to a Cloudwatch log group with the name /aws/kinesisfirehose/<delivery stream name>, which is automatically created when the delivery stream is created. -Two log streams, DestinationDelivery and BackupDelivery, are created in this log group.

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The backup settings in the delivery stream specify how failed delivery requests are handled. -See Backup settings for details on configuring backups to S3.

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Scalingedit

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Firehose can automatically scale to handle very high throughput. -If your Elastic deployment is not properly configured for the data volume coming from Firehose, it could cause a bottleneck, which may lead to increased ingest times or indexing failures.

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There are several facets to optimizing the underlying Elasticsearch performance, but Elastic Cloud provides several ready-to-use hardware profiles which can provide a good starting point. -Other factors which can impact performance are shard sizing, indexing configuration, and index lifecycle management (ILM).

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If you encounter further problems, please contact Elastic support by following the instructions here.

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Amazon Kinesis Data Firehose overviewedit

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Elastic Cloud users can ingest logs directly from AWS Kinesis Data Firehose. -All Elastic AWS integrations are supported without deploying agents to your AWS account. -Logs from AWS Kinesis Data Firehose can be automatically routed to the relevant Elastic integration for popular AWS services with no additional configuration.

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AWS Kinesis Data Firehose works with Elastic Stack version 7.17 or greater, running on Elastic Cloud only.

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What is Amazon Kinesis Data Firehose?edit

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Amazon Kinesis Data Firehose is an extract, transform, and load (ETL) service that reliably captures, transforms, and delivers streaming data to data lakes, data stores, and analytics services.

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Using Firehose to send your data to Elastic means you have no agents to deploy, lambdas to configure, or Beats to manage. Pricing is simple, predictable, and typically more cost-effective than other data ingest solutions. Additionally, auto-scaling capability is built in, and the service is designed to handle high-volume use cases.

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The overall architecture is shown below.

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-Diagram showing Amazon Kinesis Data Firehose connected to Elastic Cloud with examples of input data sources -
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