Best Query Engines for Vertex AI

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Compare the Top Query Engines that integrate with Vertex AI as of October 2025

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This a list of Query Engines that integrate with Vertex AI. Use the filters on the left to add additional filters for products that have integrations with Vertex AI. View the products that work with Vertex AI in the table below.

What are Query Engines for Vertex AI?

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 for Vertex AI currently available using the table below. This list is updated regularly.

  • 1
    Google Cloud BigQuery
    BigQuery features a highly optimized query engine that can handle large-scale queries on vast datasets with remarkable speed and efficiency. Its serverless architecture allows businesses to perform high-performance queries without the need for managing infrastructure or servers. BigQuery’s SQL-based query engine is familiar to most data analysts, making it easy to get started with complex data analysis. New customers can explore the query engine with $300 in free credits, enabling them to run a variety of queries and assess how BigQuery can support their analytical needs. The platform is also designed for scalability, ensuring that query performance remains consistent even as data grows.
    1,871 Ratings
    Starting Price: Free ($300 in free credits)
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  • 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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