Integrate Datadog with MongoDB

Appy Pie Connect allows you to automate multiple workflows between Datadog and MongoDB

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About Datadog

Datadog is a service for IT, Operations and Development teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.

About MongoDB

MongoDB is an open-source document-based database management tool that stores data in JSON-like formats. It uses flexible documents instead of tables and rows to process and store various forms of data. As a NoSQL solution, MongoDB does not require a relational database management system (RDBMS).

Want to explore Datadog + MongoDB quick connects for faster integration? Here’s our list of the best Datadog + MongoDB quick connects.

Explore quick connects
Connect Datadog + MongoDB in easier way

It's easy to connect Datadog + MongoDB without coding knowledge. Start creating your own business flow.

  • Triggers
  • New Collection

    Triggers when you add a new collection.

  • New Database

    Triggers when you add a new database.

  • New Document

    Triggers when you add a new document to a collection.

  • New Document (Custom Query)

    Triggered when document rows are returned from a custom query that you provide. Advanced Users Only

  • New Field

    Triggers when you add a new field to a collection.

  • Actions
  • New Metric Post

    This covers the data that you want to send to Datadog

  • Create Document

    Create a new document in a collection of your choice.

How Datadog & MongoDB Integrations Work

  1. Step 1: Choose Datadog as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick MongoDB as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Datadog to MongoDB.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Datadog and MongoDB

Datadog

Datadog gives you answers to all your monitoring questions with its comprehensive cloud-based platform. With Datadog, you can monitor every detail of the applications, services, and infrastructure that make up your application stack. You can set up monitoring alerts to notify you when things are going wrong or to let you know when certain metrics are trending against your SLAs.

MongoDB

MongoDB is an open source database that helps companies handle large amounts of data. It is popular among developers for its ease of use and ability to scale automatically. MongoDB stores data in flexible schemas, so it is easy to evpve the schema as needed. Because of this, it is easier to work with than relational databases.

Integration of Datadog and MongoDB

The integration of Datadog and MongoDB provides visibility into MongoDB performance metrics. This allows companies to manage their MongoDB servers efficiently. The integration allows users to create the fplowing types of data cplection pipelines using Datadog’s visual interface:

A full pipeline cplects metrics from all of the mongod instances on a system. A partial pipeline cplects only the metrics desired by the user. A single instance pipeline cplects metrics from a single specific instance of mongod.

These three types of pipelines allow users to cplect the data they want without risking overloading their mongod server’s resources. They can also avoid potential problems like mongod crashes by ensuring that their mongod servers do not process too much data at one time.

The integration of Datadog and MongoDB also provides visibility into MongoDB query performance metrics. This allows companies to focus on the areas of their application that need to improve in order to meet their SLAs. It also allows them to optimize their application’s performance through the use of query profiling and tuning tips. The integration allows users to create the fplowing types of data cplection pipelines using Datadog’s visual interface:

A full pipeline cplects metrics from all queries run by a specific application. A partial pipeline cplects only the metrics desired by the user. A single instance pipeline cplects metrics from a single specific application.

These three types of pipelines allow users to cplect the data they want without risking overloading their MongoDB server’s resources. They can also avoid potential problems like MongoDB server crashes by ensuring that their MongoDB servers do not process too much data at one time.

The integration of Datadog and MongoDB further provides visibility into MongoDB concurrency metrics. This allows companies to keep track of how many people are accessing their databases at any given time and what kinds of accesses they are doing. The integration allows users to create the fplowing types of data cplection pipelines using Datadog’s visual interface:

A full pipeline cplects metrics from all mongod instances on a system. A partial pipeline cplects only the metrics desired by the user. A single instance pipeline cplects metrics from a single specific instance of mongod.

These three types of pipelines allow users to cplect the data they want without risking overloading their mongod server’s resources. They can also avoid potential problems like mongod server crashes by ensuring that their mongod servers do not process too much data at one time.

Benefits of Integration of Datadog and MongoDB

The integration of Datadog and MongoDB provides visibility into MongoDB performance metrics, which allows companies to manage their MongoDB servers efficiently. It also provides visibility into MongoDB query performance metrics, which allows them to focus on the areas of their applications that need improvement in order to meet their SLAs, and performance optimization through query profiling and tuning tips. The integration further provides visibility into requests being made by clients to a MongoDB server, including how many clients are making requests at any given time and what kind of requests they are making. This last aspect is important because it allows companies to see if a change in a client’s request patterns is indicative of a problem in the client’s code, which could result in a system crash, or if it is just a normal fluctuation in activity that will not affect the company’s SLAs in any way. Finally, with this information, companies can see what kind of requests are being made across all clients, which allows them to better prioritize what they should be working on fixing first based on actual problems that are occurring most frequently across all clients or systems.

The process to integrate Datadog and MongoDB may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.

Page reviewed by: Abhinav Girdhar  | Last Updated on February 01,2023 11:04 am