In our quest to learn Elastic Stack, we have covered good ground and have a solid footing in all of its components. We have a solid foundation of the core Elasticsearch with its search and analytics capabilities, and we have covered how to effectively use Logstash and Kibana to build a powerful platform that can deliver analytics on big data. We have also seen how X-Pack makes it easy to secure and monitor big data, generate alerts, and perform graph analysis and machine learning. 

Taking the Elastic Stack components to production requires that you are aware of some common pitfalls, patterns, and strategies that can help you run your solution smoothly in production. In this chapter, we will see some common patterns, tips, and tricks to run Elasticsearch, Logstash, Kibana, and other components in production.

We will start with Elasticsearch and then move on to other components. There are various ways to run Elasticsearch in production. There may be various factors that influence your decision on how you should deploy. We will cover the following topics to help you take your next Elastic Stack project to production: 

• Hosting Elastic Stack on a managed cloud
• Hosting Elastic Stack on your own, that is, self-hosting
• Backing up and restoring
• Setting up index aliases
• Setting up index templates
• Modeling time series data

Let's first understand how we can go about taking Elastic Stack to production with one of the managed cloud providers. This option requires a minimum amount of work to set up a production-ready cluster.