Optimizing Storage Efficiency in Elasticsearch
Organizations are using the Elastic Stack for a variety of use cases. Many of these use cases, such as centralized logging and security analytics, deal with indexing, storing, and analyzing large volumes of logs and metrics into Elasticsearch. At these large volumes, cluster architectures and data tiering strategies are important to ensure that your access and speed needs are balanced with the storage costs.
In this webinar, we will look at how hot-warm-cold cluster architectures allow you to achieve that balance, and the various considerations that go into deciding the data tiering policy. We will also discuss the factors that influence how much data can be put on each node, and how you can think about optimizing that.
- Overview of hot-warm-cold architecture and node characteristics
- Impact of use-case characteristics on node storage density
- How to optimize your data for storage efficiency
- How different lucene data structures can affect storage density
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Christian Dahlqvist is a Product Marketing Engineer at Elastic, responsible for creating demos and other types of technical content. This includes working closely with field and engineering teams, developing recommendations and best practices around deployment architecture, sizing, benchmarking, and performance.