Elasticsearch Training
Generic courses teach Elasticsearch in isolation. We train your engineers on Elasticsearch as it actually runs in production — with real data, real failure modes, and real operational trade-offs. Our instructors are the same engineers who manage large-scale clusters for clients daily, so every session is grounded in practical experience rather than documentation paraphrase. Training is delivered remotely or on-site, and always tailored to your team's starting point.
Fundamentals & Core Concepts
We start with the mental model that makes everything else click: how Elasticsearch distributes data across shards, how mapping decisions compound into long-term trade-offs, and how the Query DSL translates into actual Lucene operations. Labs use real data, not toy examples.
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Cluster anatomy: master, data, coordinating, and ML node roles
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Mapping design and field type trade-offs
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Query DSL: from match queries through aggregations and nested objects
Advanced Topics & Production Operations
For engineers already comfortable with the basics, we run intensive deep-dive sessions on the topics that separate competent Elasticsearch users from genuine experts. We cover the internals that matter operationally: how Lucene segments merge, why fielddata can cause OutOfMemory errors, and when cross-cluster replication is the right answer versus a simpler alternative. Every advanced session includes a simulated incident scenario so engineers practice under realistic pressure.
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Cluster sizing, capacity planning, and hardware selection
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Cross-cluster replication (CCR) and cross-cluster search (CCS)
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Rolling upgrade procedures and version migration
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Simulated incident response — red cluster, split brain, and OOM scenarios
Custom Workshops for Your Stack
The most effective training is built around your actual situation. We review your existing indices, mappings, query patterns, and operational pain points before designing the curriculum. Lab exercises are constructed from your real data — anonymized where needed — so engineers are solving the problems they will face next week, not hypothetical ones. We have run this process for teams in e-commerce, fintech, media, and SaaS, and the curriculum adjusts substantially based on what we find in your cluster. Every workshop ends with a documented action list and a resource package — cheat sheets, runbooks, and reference architectures — that the team can use independently long after the sessions end.
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