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When ingesting data into Elasticsearch, it is often beneficial to enrich documents with additional information that can later be used for searching or viewing the data. Enrichment is the process of merging data from an authoritative source into documents as they are ingested into Elasticsearch. For example, enrichment can be done with the GeoIP Processor which processes documents that contain IP addresses and adds information about the geographical location associated with each IP address.

For many companies, Elastic included, wikis developed with Confluence are a critical source of content, procedures, policies, and plenty of other important info, spanning teams across the entire organization. But sometimes finding a particular nugget of information can be tricky, especially when you’re not exactly sure where that information was located. Was it in the wiki? In a Word doc? In Salesforce? A GitHub issue? Somewhere else?

As Elasticsearch users are pushing the limits of how much data they can store on an Elasticsearch node, they sometimes run out of heap memory before running out of disk space. This is a frustrating problem for these users, as fitting as much data per node as possible is often important to reduce costs. But why does Elasticsearch need heap memory to store data? Why doesn't it only need disk space?

Let’s be honest. No one wakes up in the morning thinking of reasons to contact customer support. It’s tedious, onerous, and can eat into your evening Netflix time. Thankfully, most brands realize that customer experiences drive brand loyalty and repeat purchases.

A classic problem that every backend developer has faced during their work is testing an application that uses a database. A perfectly valid solution is to use the real database for testing your application, but you would be doing an integration test, while you want a unit test. There are many ways to solve this problem. You could create the database with docker, or use an in-memory compatible one, but if you are writing unit tests that can be easily parallelized this will become quite uncomfortable.