Showcase Your Elasticsearch Skills with a GitHub README Badge

Use this markdown in your README:

![Elasticsearch](https://img.shields.io/badge/Elasticsearch-005571?style=for-the-badge&logo=elasticsearch&logoColor=white)

The #005571 is Elasticsearch's official teal — a dark, professional tone from the Elastic brand palette. The elasticsearch logo identifier renders Elasticsearch's logo from Simple Icons. This teal badge pairs naturally with Kibana, Logstash, and other Elastic Stack component badges in a data engineering or observability-focused profile.

Elasticsearch experience varies from basic search to complex distributed cluster management. Specify your depth:

• Search: Full-text queries (match, multi_match, bool), fuzzy search, boosting, custom analyzers
• Aggregations: Metric aggregations (avg, sum, max), bucket aggregations for faceted search
• Index management: Mapping design, index lifecycle management (ILM), aliases for zero-downtime reindexing
• Ingestion: Bulk API for high-throughput indexing, Logstash pipelines, Beats agents
• Cluster management: Shard allocation, replica configuration, cluster health monitoring
• Observability: Log analysis, APM (Application Performance Monitoring) integration, Kibana dashboards

Designing efficient index mappings — knowing when to use keyword vs. text, when to disable _source, and how to set appropriate shard counts — separates Elasticsearch users from Elasticsearch engineers.

Elasticsearch integrates across several technology stacks:

• ELK stack: Elasticsearch + Logstash + Kibana
• Modern observability: Elasticsearch + Filebeat + APM + Kibana
• Search application: Elasticsearch + Python/Node.js + Redis + PostgreSQL
• Data pipeline: Elasticsearch + Kafka + Python + Grafana

Elasticsearch and PostgreSQL are complementary tools — PostgreSQL for transactional data with ACID guarantees, Elasticsearch for full-text search and aggregation queries. Architectures that use both tools effectively (write to Postgres, sync to Elasticsearch for search) demonstrate sophisticated data architecture understanding.

Elasticsearch integration code is written in Python, JavaScript, Java, or Go depending on your application. The Elasticsearch client libraries handle API communication, so your language stats reflect your application stack. JSON query DSL files in your repositories tell an experienced developer what kinds of search problems you have solved.

For pinned repositories, a project that implements a non-trivial search feature — autocomplete with edge n-grams, faceted search with aggregations, multilingual search with language-specific analyzers — is more compelling than a 'basic Elasticsearch setup' project. Include sample query DSL in your README to show what your implementation actually does.