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Some cleanup for Manage data -> Ingest from apps (#722)
This updates the "Manage data" → "Ingest data from applications" section with a few leftovers from our initial migration: - Adds versioning tags to the files (I used `applies_to: stack ga` for these, since, to my knowledge, they haven't been tested to work in a Serverless environment). stack: ga - Removes the "What to do" comments - Removes the ECE versions of the files from the 'raw migrated files' section so that the docs can build cleanly.
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manage-data/ingest/ingesting-data-from-applications.md

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- https://www.elastic.co/guide/en/cloud/current/ec-ingest-guides.html
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- https://www.elastic.co/guide/en/cloud-enterprise/current/ece-ingest-guides.html
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# Ingesting data from applications
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% What needs to be done: Refine
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% GitHub issue: docs-projects#387
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% Scope notes: The ESS and ECE versions should be identical, but they'll still need to be combined.
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The following tutorials demonstrate how you can use the Elasticsearch language clients to ingest data from an application into a deployment in {{ech}} or {{ece}}.
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[Ingest data with Node.js](ingesting-data-from-applications/ingest-data-with-nodejs-on-elasticsearch-service.md)

manage-data/ingest/ingesting-data-from-applications/ingest-data-from-beats-to-elasticsearch-service-with-logstash-as-proxy.md

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- https://www.elastic.co/guide/en/cloud/current/ec-getting-started-search-use-cases-beats-logstash.html
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# Ingest data from Beats with Logstash as a proxy
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This guide explains how to ingest data from Filebeat and Metricbeat to {{ls}} as an intermediary, and then send that data to your {{ech}} or {{ece}} deployment. Using {{ls}} as a proxy limits your Elastic stack traffic through a single, external-facing firewall exception or rule. Consider the following features of this type of setup:
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* You can send multiple instances of Beats data through your local network’s demilitarized zone (DMZ) to {{ls}}. {{ls}} then acts as a proxy through your firewall to send the Beats data to your deployment, as shown in the following diagram:

manage-data/ingest/ingesting-data-from-applications/ingest-data-from-relational-database-into-elasticsearch-service.md

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# Ingest data from a relational database
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This guide explains how to ingest data from a relational database into {{ecloud}} through [{{ls}}](logstash://reference/index.md), using the Logstash [JDBC input plugin](logstash://reference/plugins-inputs-jdbc.md). It demonstrates how Logstash can be used to efficiently copy records and to receive updates from a relational database, and then send them into {{es}} in an {{ech}} or {{ece}} deployment.
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The code and methods presented here have been tested with MySQL. They should work with other relational databases.

manage-data/ingest/ingesting-data-from-applications/ingest-data-with-nodejs-on-elasticsearch-service.md

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# Ingest data with Node.js

manage-data/ingest/ingesting-data-from-applications/ingest-data-with-python-on-elasticsearch-service.md

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# Ingest data with Python

manage-data/ingest/ingesting-data-from-applications/ingest-logs-from-nodejs-web-application-using-filebeat.md

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# Ingest logs from a Node.js web application using Filebeat
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This guide demonstrates how to ingest logs from a Node.js web application and deliver them securely into an {{ech}} or {{ece}} deployment. You’ll set up Filebeat to monitor a JSON-structured log file that has standard Elastic Common Schema (ECS) formatted fields, and you’ll then view real-time visualizations of the log events in Kibana as requests are made to the Node.js server. While Node.js is used for this example, this approach to monitoring log output is applicable across many client types. Check the list of [available ECS logging plugins](ecs-logging://reference/intro.md#_get_started).
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*Time required: 1.5 hours*

manage-data/ingest/ingesting-data-from-applications/ingest-logs-from-python-application-using-filebeat.md

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# Ingest logs from a Python application using Filebeat
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This guide demonstrates how to ingest logs from a Python application and deliver them securely into an {{ech}} deployment. You’ll set up Filebeat to monitor a JSON-structured log file that has standard Elastic Common Schema (ECS) formatted fields, and you’ll then view real-time visualizations of the log events in {{kib}} as they occur. While Python is used for this example, this approach to monitoring log output is applicable across many client types. Check the list of [available ECS logging plugins](ecs-logging://reference/intro.md).
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*Time required: 1 hour*

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