Best Query Engines for Salesforce Data Cloud
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Compare the Top Query Engines that integrate with Salesforce Data Cloud as of June 2025
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This a list of Query Engines that integrate with Salesforce Data Cloud. Use the filters on the left to add additional filters for products that have integrations with Salesforce Data Cloud. View the products that work with Salesforce Data Cloud in the table below.
What are Query Engines for Salesforce Data Cloud?
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 Salesforce Data Cloud currently available using the table below. This list is updated regularly.
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1
Google Cloud BigQuery
Google
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.Starting Price: Free ($300 in free credits) -
2
Snowflake
Snowflake
Snowflake is a comprehensive AI Data Cloud platform designed to eliminate data silos and simplify data architectures, enabling organizations to get more value from their data. The platform offers interoperable storage that provides near-infinite scale and access to diverse data sources, both inside and outside Snowflake. Its elastic compute engine delivers high performance for any number of users, workloads, and data volumes with seamless scalability. Snowflake’s Cortex AI accelerates enterprise AI by providing secure access to leading large language models (LLMs) and data chat services. The platform’s cloud services automate complex resource management, ensuring reliability and cost efficiency. Trusted by over 11,000 global customers across industries, Snowflake helps businesses collaborate on data, build data applications, and maintain a competitive edge.Starting Price: $2 compute/month -
3
Amazon Athena
Amazon
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run. Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL. Most results are delivered within seconds. With Athena, there’s no need for complex ETL jobs to prepare your data for analysis. This makes it easy for anyone with SQL skills to quickly analyze large-scale datasets. Athena is out-of-the-box integrated with AWS Glue Data Catalog, allowing you to create a unified metadata repository across various services, crawl data sources to discover schemas and populate your Catalog with new and modified table and partition definitions, and maintain schema versioning. -
4
Apache Hive
Apache Software Foundation
The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive. Apache Hive is an open source project run by volunteers at the Apache Software Foundation. Previously it was a subproject of Apache® Hadoop®, but has now graduated to become a top-level project of its own. We encourage you to learn about the project and contribute your expertise. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data. Hive provides the necessary SQL abstraction to integrate SQL-like queries (HiveQL) into the underlying Java without the need to implement queries in the low-level Java API. -
5
Apache Impala
Apache
Impala provides low latency and high concurrency for BI/analytic queries on the Hadoop ecosystem, including Iceberg, open data formats, and most cloud storage options. Impala also scales linearly, even in multitenant environments. Impala is integrated with native Hadoop security and Kerberos for authentication, and via the Ranger module, you can ensure that the right users and applications are authorized for the right data. Utilize the same file and data formats and metadata, security, and resource management frameworks as your Hadoop deployment, with no redundant infrastructure or data conversion/duplication. For Apache Hive users, Impala utilizes the same metadata and ODBC driver. Like Hive, Impala supports SQL, so you don't have to worry about reinventing the implementation wheel. With Impala, more users, whether using SQL queries or BI applications, can interact with more data through a single repository and metadata stored from source through analysis.Starting Price: Free -
6
Databricks Data Intelligence Platform
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.
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