Datadog vs Elasticsearch

• 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.
• Lightweight agent on hosts

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• 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.

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• User interface could be improved in some areas, looking for host information and the agent install link takes a bit of time.
• Configuration of the agent is generally done via a config file which is a pro and a con. It would be nice to have some UI to configure various agent options.
• Billing isn't entirely straightforward, they could use more reports to figure out the source of your monthly costs.

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• Joining data requires duplicate de-normalized documents that make parent child relationships. It is hard and requires a lot of synchronizations
• Tracking errors in the data in the logs can be hard, and sometimes recurring errors blow up the error logs
• Schema changes require complete reindexing of an index

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• One of our critical production databases went down a couple of weeks back. Just because we had the Datadog monitoring enabled for that database, Datadog sent the alerts to PagerDuty and we were instantly notified via pager and email alert. We could act right away and bring up the database within a few mins.
• Ability to create custom screenboards have allowed us to track the critical and warning alerts in front of our eyes all the time

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• 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.

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