Compare the Top Query Engines in India as of December 2025

What are Query Engines in India?

Query engines are software tools designed to retrieve and process data from databases or large datasets in response to user queries. They efficiently interpret and execute search requests, optimizing the retrieval process to deliver accurate and relevant results quickly. Query engines can handle structured, semi-structured, and unstructured data, making them versatile for various applications such as data analytics, business intelligence, and search engines. They often support complex query languages like SQL and can integrate with multiple data sources to provide comprehensive insights. By optimizing data retrieval, query engines enhance the performance and usability of data-driven applications and decision-making processes. Compare and read user reviews of the best Query Engines in India currently available using the table below. This list is updated regularly.

  • 1
    StarTree

    StarTree

    StarTree

    StarTree, powered by Apache Pinot™, is a fully managed real-time analytics platform built for customer-facing applications that demand instant insights on the freshest data. Unlike traditional data warehouses or OLTP databases—optimized for back-office reporting or transactions—StarTree is engineered for real-time OLAP at true scale, meaning: - Data Volume: query performance sustained at petabyte scale - Ingest Rates: millions of events per second, continuously indexed for freshness - Concurrency: thousands to millions of simultaneous users served with sub-second latency With StarTree, businesses deliver always-fresh insights at interactive speed, enabling applications that personalize, monitor, and act in real time.
    Starting Price: Free
  • 2
    Apache Spark

    Apache Spark

    Apache Software Foundation

    Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.
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