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Elasticsearch

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Elasticsearch Developers

Elasticsearch is a distributed, RESTful search and analytics engine at the heart of the Elastic Stack. Built on Apache Lucene, it enables full-text search, structured search, analytics, and logging at scale — powering ev…

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Key Capabilities

Why Elasticsearch?

What makes Elasticsearch the right choice for modern engineering teams.

Full-Text Search

Relevance-ranked search with BM25 scoring, synonyms, and language analysers.

Real-Time Indexing

Documents become searchable within seconds of being indexed.

Distributed Architecture

Horizontal scaling across nodes with automatic shard rebalancing.

Aggregations

Complex analytics and faceted search with bucket and metric aggregations.

Vector Search

kNN and ANN approximate nearest-neighbour search for semantic and AI-powered search.

Kibana Integration

Visualise, explore, and alert on your data with the Kibana dashboard.

Code Example

Elasticsearch in Action

elasticsearch-demoDatabase
import { Client } from '@elastic/elasticsearch';

const client = new Client({ node: process.env.ELASTICSEARCH_URL });

// Create index with custom mappings
await client.indices.create({
  index: 'products',
  mappings: {
    properties: {
      name:        { type: 'text', analyzer: 'english' },
      description: { type: 'text' },
      price:       { type: 'float' },
      category:    { type: 'keyword' },
      embedding:   { type: 'dense_vector', dims: 1536, index: true, similarity: 'cosine' },
    },
  },
});

// Semantic + keyword hybrid search
const results = await client.search({
  index: 'products',
  knn: { field: 'embedding', query_vector: queryEmbedding, k: 10, num_candidates: 100 },
  query: { match: { name: 'wireless headphones' } },
});
Our Developers

What Our Elasticsearch
Developers Know

Every Krapton developer is vetted with real production experience in Elasticsearch across multiple industry domains.

Index Design
Defining mappings, analysers, and sharding strategies for optimal search performance.
Query DSL
Writing bool, match, term, range, and nested queries using the Elasticsearch Query DSL.
Aggregations
Building faceted navigation, histograms, and date-range analytics.
Indexing Pipelines
Using ingest pipelines for data enrichment before indexing.
Vector Search
Implementing semantic search with dense_vector fields and kNN queries.
Node.js Client
Connecting to Elasticsearch from Node.js and Next.js using the official client.

More Database Technologies

Other database technologies we work with at Krapton.

Engagement Models

Three ways to hire Elasticsearch developers

Pick the engagement that matches how you actually work. No multi-year contracts — scale up or down month by month.

Dedicated Developer

Most popular

Full-time Elasticsearch engineer who reports only to you. Best for ongoing products, long-term roadmaps and teams that need a core hire without the HR overhead.

  • 40 hours / week
  • Your Jira, your repo
  • Month-to-month

Hourly / Time & Materials

Pay only for billable hours. Ideal for research spikes, code audits, or variable-load Elasticsearch work where scope is still being discovered.

  • Weekly timesheets
  • Slack-first comms
  • No minimum commit

Fixed-price Milestones

Scoped delivery with clear milestones and acceptance criteria. Best for well-defined Elasticsearch builds like an MVP, a migration or a specific module.

  • Scope locked upfront
  • Milestone acceptance
  • Predictable budget
FAQ

Hiring Elasticsearch developers — answered

Practical answers to the questions CTOs and founders ask us most often before they hire.

Hire Elasticsearch Experts

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with Elasticsearch?

Get a free 30-minute consultation with our Elasticsearch team. Clear roadmap, transparent pricing, no obligation.

Free NDA on Request
Response within 24 hours
Certified Elasticsearch developers
Flexible engagement models
US, UK, UAE & India clients served
Elasticsearch

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