Datadog is a monitoring service for IT, Dev and Ops 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.
$0
Up to 5 hosts
MongoDB
Score 8.0 out of 10
N/A
MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
$0.10
million reads
Pricing
Datadog
MongoDB
Editions & Modules
Free
$0
Up to 5 hosts
Log Management
$1.27
Per Million Log Events
Standard
$15/host
Up to 500 hosts
Infrastructure
$15.00
Per Host Per Month
APM
$31.00
Per Host Per Month
Enterprise
Custom
500+ hosts
Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
Offerings
Pricing Offerings
Datadog
MongoDB
Free Trial
Yes
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
Optional
No setup fee
Additional Details
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Fully managed, global cloud database on AWS, Azure, and GCP
MongoDB and Cassandra are both database system from the NoSQL family. MongoDB can be used in lots of use cases while Cassandra has a specific usage. There are some features that MongoDB provides efficiently while Cassandra doesn't and vice-versa. Like, you can update the data …
DataDog Is well suited to all of the Infrastructure Monitoring Solutions, DB monitoring, and other Network monitoring also. It's not well suited because it cannot give perfect Infrastructure recommendations for our use case but also For example: If we are using AWS DB to monitor performance insights then Datadog is less effective there because AWS gives very niche recommendations.
If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.
APIs, the ability to interact with the data we pull into data dog is key. We port the information over to Servicenow, so the ability to pull everything into DataDog, then Servicenow, is a key component of our success here at Wayfair.
Simple Interface - clean, useful, effective. Allows users to use DataDog for one reason, get work done.
Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.
We had a couple "integrations" that had some issues during setup, but Support addressed them very quickly
Unnecessary alerts about DataDog components...by the time I see them, they're almost always also fixed
I wish there was a DataDog mobile app that would have dedicated alerts (configurable per alert to override Do Not Disturb setting) instead of relying on emails notifications that could be overlooked in the midst of many incoming emails around the same time.
An aggregate pipeline can be a bit overwhelming as a newcomer.
There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.
I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.
NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.
The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.
Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global support.
While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
We are still trying other products, but people still like Datadog. After setting up a dashboard, it's great for monitoring instances on Datadog. Also, the DevOps team had a good time setting up Datadog. It means Datadog was way easier to set up compared to those others.
We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.
Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB
