Comparison Between Elasticsearch and Traditional Databases
| Aspect | Elasticsearch | Traditional Databases |
|---|---|---|
| Data Model | Document-oriented, JSON-based | Relational, table-based with predefined schemas |
| Querying | Uses Elasticsearch Query DSL | Uses SQL |
| Scalability | Horizontally scalable | Limited scalability, often requires complex strategies |
| Performance | Real-time search and analytics | Structured query performance may degrade with scale |
| Consistency | Eventual consistency | Strong consistency |
| Use Cases | Full-text search, real-time analytics | Transactional applications, structured data management |
How Elastic Search is Different From Traditional Databases
In the world of data management, the choice of database technology can greatly impact the efficiency and capabilities of an application. Traditional relational databases have long been the standard for storing structured data, but with the rise of big data and real-time analytics, new technologies like Elasticsearch have emerged as good alternatives.
In this article, we will learn about the key differences between Elasticsearch and traditional databases and highlight their unique features, strengths, and use cases in detail.