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
Elasticsearch
Score 8.4 out of 10
N/A
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
From my perspective, there is nothing currently on the marker better than Datadog, but unfortunately, that's a pricey product, Elasticsearch deliver us part of Datadog functionalities being cheaper. Fluentd as a service (provided by the company behind Fluentd) looks like a …
Search and analytics capabilities of Elasticsearch are superior to its competitors. Being open source, it is a cheaper and faster solution than other competitors. Installation is straightforward and it can be potentially deployed anywhere and everywhere! There is no need for …
I think Elasticseach works less great compared to Splunk. Mainly the way the Splunk search head works is vastly superior to the way the Elasticsearch query language works. Furthermore, the Splunk architecture is in my opinion easier to roll out and scale-up. Splunk also has a …
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.
Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
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.
As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
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.
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
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.
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
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.
As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
We have had great luck with implementing Elasticsearch for our search and analytics use cases.
While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.
