Elasticsearch Engineering
Search and observability infrastructure at scale. We build Elasticsearch clusters for full-text search, log analytics, APM, and real-time monitoring — with index lifecycle management, query optimization, and multi-tenant architecture.
What happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
Search and Observability Infrastructure
We design and operate Elasticsearch clusters that power sub-second search across millions of documents, ingest terabytes of logs daily, and provide real-time observability for distributed systems.
What We Build
| Capability | What We Deliver |
|---|---|
| Full-text search | custom analyzers, synonym graphs, and relevance tuning for product catalogs, knowledge bases, and document repositories serving 10K+ queries per second |
| Log analytics pipelines | Logstash and Beats ingestion from application logs, infrastructure metrics, and network flows into time-series indices with automated rollover |
| Observability platforms | APM traces, error tracking, and uptime monitoring with Kibana dashboards and alerting rules correlated across services |
| Multi-tenant search | index-per-tenant and filtered alias patterns with field-level security, cross-cluster search, and tenant-aware resource isolation |
Engineering Standards
- Index lifecycle management: hot-warm-cold architecture with automated rollover and retention policies
- Shard sizing strategy: 20-40 GB per shard, shard-per-node ratios tuned for heap and search concurrency
- Mapping design: strict schemas, keyword vs. text field selection, nested vs. flattened object trade-offs
- Query optimization: filter context over query context, composite aggregations, search-after pagination
- Monitoring: cluster health, JVM heap pressure, indexing throughput, and search latency via Prometheus + Grafana
- Disaster recovery: snapshot/restore to S3, cross-cluster replication for active-passive failover
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Full-text search, log analytics, or APM observability at scale | Elasticsearch / ELK — this page |
| Semantic search with embeddings, RAG retrieval | Vector databases — Pinecone, Weaviate for embeddings |
| SQL analytics, BI dashboards, data warehouse | Snowflake — columnar analytics, not search |
| Real-time OLAP on streaming data | Apache Druid — optimized for time-series aggregations |
| Time-series telemetry at extreme scale (metrics only) | Prometheus + VictoriaMetrics — lighter than ES for metrics |
Depth of Practice
We maintain published articles on Elasticsearch internals, ELK stack architecture, search relevance engineering, and cluster operations on the ActiveWizards blog. Our engineers operate Elasticsearch clusters handling billions of documents across e-commerce search, fintech compliance, and SaaS observability platforms.
Related articles
Graph RAG: Why Vector Search Alone Fails Multi-Hop Agent Queries
How to build Graph RAG with Neo4j for AI agent memory. Real architecture, Cypher patterns, and the failure modes vector-only pipelines hit at production
RAGRAG vs. Fine-Tuning: A CTO's Cost-Effective Guide
A refreshed CTO framework for deciding between prompt optimization, RAG, and fine-tuning based on knowledge freshness, behavior control, cost, and operating complexity.
Vector DatabasePinecone Performance Tuning for RAG: Latency, Throughput, and Read Nodes
A practical Pinecone tuning guide for RAG covering query latency, ingestion throughput, dedicated read nodes, metadata indexing, and serverless performance tradeoffs.
Discuss your Elasticsearch Engineering path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.