AWS Elastic Beanstalk vs Elasticsearch

• AWS Elastic Beanstalk was easy to implement, we only had to insert the application code to have it up and running in a few minutes. This helps us to launch company campaigns for different areas such as marketing, commercial and human resources.
• It has a fast learning curve, since the training material of the platform is public on the manufacturer's portal, furthermore it is easy to interact and intuitive for developers.
• The productivity of the equipment is optimized since they are not worrying about preparing the platform or infrastructure, they only have to attend to what is required by the application and its operation and this maintenance.
• The indicators and metrics that it has, ensure that the application cannot be saturated and have a performance problem, with AWS Elastic Beanstalk the application can scale resources when needed automatically.

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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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• Limited to the frameworks and configurations that AWS supports. There is no native way to use Elastic Beanstalk to deploy a Go application behind Nginx, for example.
• It's not always clear what's changed on an underlying system when AWS updates an EB stack; the new version is announced, but AWS does not say what specifically changed in the underlying configuration. This can have unintended consequences and result in additional work in order to figure out what changes were made.

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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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• Greatly reduced time in setting up and deploying test environments has saved a lot of hours and money.
• Near-zero maintenance means that we don't spend any time tending to servers, the app containers just work.
• The initial learning curve is a little steep to get services hosted in Elastic Beanstalk talking to external services like Redis, MongoDB, etc; but once one understands the pattern, development is a breeze.
• The command line interface has enabled us to build many utility scripts that simplify our development workflow, thereby saving 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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