04.11.2025 - Henrique Canha

From SQL to Elasticsearch: a practical shift

With the exponential growth of data and the need for real-time analysis, many companies are rethinking how they store, access, and analyze information. Transitioning from traditional relational systems to distributed search engines like Elasticsearch is gaining momentum due to its flexibility, scalability, and speed.

This article explores the practical reasons for making the shift from SQL to Elasticsearch, outlines the benefits and challenges, and presents real-world use cases.

From SQL to Elasticsearch: benefits and reasons for the shift

Relational systems such as MySQL or PostgreSQL are robust and effective for structured data and complex relationships. However, they show limitations when it comes to:

High-volume, real-time queries
Hierarchical aggregations or multidimensional analysis
Full-text search capabilities
Horizontal scalability

Elasticsearch, on the other hand, is a distributed search and analytics engine based on Apache Lucene. It allows large-scale data indexing, offers a flexible schema-free structure, and near-instantaneous responses.

Key advantages:

Native full-text search with fuzziness and typo tolerance
Fast and versatile aggregations (aggregation pipelines)
Horizontal scalability via sharding and replication
Integration with tools like Kibana for visualization

According to Elastic’s 2024 report on emerging observability trends, over 50% of companies use Elasticsearch for logging, observability, and real-time security analytics.

From SQL to Elasticsearch: transforming relational data into documents

While relational systems use tables with rows and columns, Elasticsearch works with JSON documents. Therefore, the first step in any migration is to rethink the data model.

Example: The customers table with columns like id, name, address, and purchases can be converted into a single JSON document with embedded information.

Migration tools:

Logstash: powerful ETL pipeline with filters and Elasticsearch output
Beats: lightweight data shippers for logs, metrics, etc.
Ingest Nodes: enable native processing within the Elastic cluster
Custom scripts: for specific transformations using Python, Java, or Node.js

Normalization (a best practice in SQL) gives way to denormalization in Elasticsearch, which speeds up queries but requires thoughtful document structuring.

From SQL to Elasticsearch: querying and aggregations

In SQL we use SELECT, WHERE, JOIN, GROUP BY... In Elasticsearch we use Query DSL, a JSON-based query language that allows expressive and complex queries.

Example comparison:

SQL support in Elasticsearch:

SQL REST API: execute SQL queries via HTTP
Kibana: offers a visual SQL query editor
Elastic JDBC driver: enables integration with BI tools like Tableau and Power BI

For those coming from relational environments, these tools ease the learning curve and simplify the transition.

From SQL to Elasticsearch: performance and scalability

Elasticsearch is built to scale horizontally. Each index can be divided into shards that are replicated across different nodes in a cluster.

Key benefits:

Millisecond-level response times for millions of documents
Fault tolerance with automatic replication
Modular growth by adding nodes

Integration with Kibana enables real-time dashboards and alerts, making it ideal for security operations and monitoring.

From SQL to Elasticsearch: security and compliance

The Elastic Stack (Enterprise Edition) includes advanced security features:

Authentication via LDAP, SAML, Active Directory
Role-based access control (RBAC)
Encryption at rest and in transit
Audit logging

These features are essential for highly regulated sectors such as finance, healthcare, and the public sector.

How Syone supports your Elasticsearch architecture

As an open-source Competence Center and Elastic partner, Syone helps organizations design, implement and scale their Elasticsearch architecture with confidence.

We offer:

Architecture assessment and deployment best practices
Data modelling and performance optimization
Migration from relational databases to Elasticsearch
Integration with observability and security ecosystems
Managed services, monitoring and continuous support

Whether you're modernizing your data infrastructure or scaling real-time analytics, Syone delivers proven expertise to help you succeed securely and at scale.

Discover how Syone can support your Elasticsearch transformation or contact us and speak with one of our open-source experts.