Elastic Enterprise Search
- Duration: 7 hours
- Exam Voucher: Yes
- Language: English
- Course Delivery : E - Learning Access
9000+ Professionals Enrolled
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Course Overview
The Elastic Enterprise Search (Elasticsearch) Training equips professionals with practical skills to implement and operate enterprise-grade search and analytics solutions. The course focuses on the full delivery lifecycle—data ingestion and indexing, query design, relevance tuning, performance optimization, and day-2 operations (monitoring, scaling, resiliency).If your organization wants “Google-like search” across documents, knowledge bases, product catalogs, logs, or internal systems—this course builds the muscle to deliver it with confidence.
Course Details
- Gain standardized knowledge of Elastic search and analytics fundamentals
- Learn practical implementation techniques for indexing, querying, filtering, and aggregations
- Improve search quality through relevance tuning and query optimization patterns
- Strengthen operational capability: scaling, monitoring, backup/recovery, and performance hygiene
- Enable faster information discovery across enterprise data sources
- Build readiness for real production environments with structured troubleshooting workflows
- Open to software engineers, data engineers, DevOps/SREs, platform teams, and analysts working with search/analytics workloads
- Recommended: basic understanding of JSON, APIs, and data modeling
- Helpful (not mandatory): familiarity with Linux and logs/metrics concepts
- No mandatory prerequisites required
- Understand Elasticsearch architecture and core building blocks (cluster, nodes, shards, indices)
- Ingest, index, and model data for search and analytics use cases
- Build effective queries and aggregations for discovery and reporting
- Improve relevance with analyzers, mappings, and tuning techniques
- Monitor and troubleshoot performance bottlenecks and operational issues
- Apply scaling and resiliency practices for production-ready deployments
- Elastic/Elasticsearch overview and enterprise search use cases
- Cluster architecture: nodes, shards, replicas, index lifecycle basics
- Data modeling: mappings, analyzers, tokenization, keyword vs text fields
- Data ingestion patterns: APIs, pipelines, bulk indexing (workflow perspective)
- Querying fundamentals: match, term, boolean queries, filters, sorting
- Aggregations for analytics: metrics, buckets, and dashboards-ready outputs
- Relevance tuning: analyzers, boosting, synonyms, scoring strategies
- Performance optimization: shard strategy, caching, query profiling, indexing throughput
- Operations: monitoring, scaling, backups/snapshots, security/access controls (high level)
- Troubleshooting workflows: slow queries, cluster health, indexing failures
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