Search is a common requirement for applications of all varieties. Elasticsearch was built to make it easy to include search functionality in projects built in any language. From that foundation, the rest of the Elastic Stack has been built, expanding to many more use cases in the proces. In this episode Philipp Krenn describes the various pieces of the stack, how they fit together, and how you can use them in your infrastructure to store, search, and analyze your data.
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- Your host is Tobias Macey and today I’m interviewing Philipp Krenn about the Elastic Stack and the ways that you can use it in your systems
- How did you get involved in the area of data management?
- The Elasticsearch product has been around for a long time and is widely known, but can you give a brief overview of the other components that make up the Elastic Stack and how they work together?
- Beyond the common pattern of using Elasticsearch as a search engine connected to a web application, what are some of the other use cases for the various pieces of the stack?
- What are the common scaling bottlenecks that users should be aware of when they are dealing with large volumes of data?
- What do you consider to be the biggest competition to the Elastic Stack as you expand the capabilities and target usage patterns?
- What are the biggest challenges that you are tackling in the Elastic stack, technical or otherwise?
- What are the biggest challenges facing Elastic as a company in the near to medium term?
- Open source as a business model: https://www.elastic.co/blog/doubling-down-on-open
- What is the vision for Elastic and the Elastic Stack going forward and what new features or functionality can we look forward to?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Vienna – Capital of Austria
- What Is Developer Advocacy?
- Apache Lucene
- ELK Stack
- APM (Application Performance Monitoring)
- Split Brain
- Elasticsearch Ingest Nodes
- Elastic Cloud
- Kibana Canvas