In this article, we explore how CData drivers grant access to all of Elasticsearch, enable full SQL querying of Elasticsearch, and more.

Elasticsearch is a distributed, RESTful, full-text search engine designed to store, index, retrieve, and manage document-oriented or semi-structured data. Common uses for Elasticsearch range from building a simple search engine for a web site or document collection, to supporting auto-completion, analytics, AI, and cognitive computing workloads.

Because of the schemaless / NoSQL nature of Elasticsearch, data management is often a challenge. Through REST APIs users can access the search capabilities and features of Elasticsearch, but that requires custom development to connect it with other applications and services. The other option of using the SQL API and vendor-supplied libraries offers limited read-only SQL query capability and still only offers a small subset of tool integrations.

The CData Elasticsearch drivers offer the best of both worlds. The CData Drivers expand on the capabilities of the REST and SQL APIs, providing standards-compliant SQL-92 interfaces for data management, and the support for standards like ODBC / JDBC / ADO.NET provides near-universal seamless tool integration. The effectiveness of this methodology is best represented by our knowledge base of Elasticsearch integrations, which is only a small sample of what is possible.

In this article, we explore how CData drivers:

• Grant access to all of Elasticsearch
• Enable full SQL querying of Elasticsearch
• Support JSON structures in Elasticsearch
• Provide connectivity across major tools, platforms, and applications
• Outperform the native drivers when querying Elasticsearch

#big data #elasticsearch #drivers #cdata