List of the Top 17 Free Query Engines in 2025

BigQuery boasts an exceptionally efficient query engine capable of executing large-scale queries on extensive datasets with impressive speed. Its serverless model empowers organizations to carry out high-performance queries without the burden of maintaining infrastructure or servers. The SQL-based query interface is user-friendly for most data analysts, facilitating a smooth entry into intricate data analysis tasks. New users can take advantage of $300 in complimentary credits to explore the capabilities of the query engine, allowing them to execute a range of queries and evaluate how BigQuery meets their analytical requirements. Additionally, the platform is built for scalability, ensuring that query performance stays reliable as data volumes increase.

StarTree Cloud functions as a fully-managed platform for real-time analytics, optimized for online analytical processing (OLAP) with exceptional speed and scalability tailored for user-facing applications. Leveraging the capabilities of Apache Pinot, it offers enterprise-level reliability along with advanced features such as tiered storage, scalable upserts, and a variety of additional indexes and connectors. The platform seamlessly integrates with transactional databases and event streaming technologies, enabling the ingestion of millions of events per second while indexing them for rapid query performance. Available on popular public clouds or for private SaaS deployment, StarTree Cloud caters to diverse organizational needs. Included within StarTree Cloud is the StarTree Data Manager, which facilitates the ingestion of data from both real-time sources—such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda—and batch data sources like Snowflake, Delta Lake, Google BigQuery, or object storage solutions like Amazon S3, Apache Flink, Apache Hadoop, and Apache Spark. Moreover, the system is enhanced by StarTree ThirdEye, an anomaly detection feature that monitors vital business metrics, sends alerts, and supports real-time root-cause analysis, ensuring that organizations can respond swiftly to any emerging issues. This comprehensive suite of tools not only streamlines data management but also empowers organizations to maintain optimal performance and make informed decisions based on their analytics.

You have the ability to create both flat and relational databases with an unlimited number of fields, tables, and rows, and a custom report generator is provided to facilitate this process. By connecting to compatible ODBC databases, you can craft personalized reports tailored to your needs. Additionally, you have the option to develop your own databases. Here are some key features: - Instantly filter tables for quick data retrieval - User-friendly graphic interface that is incredibly easy to navigate - Create tables and data forms with a single click - Open up to five databases at the same time - Export your data effortlessly to comma-separated files - Generate custom reports for all connected databases - Comprehensive help documentation is available for creating database reports - Print tables and queries directly from the data grid with ease - Compatibility with any SQL standard required by your ODBC-compliant databases To ensure optimal performance and an enhanced user experience, please run this database application with full administrator privileges. System requirements include: - A display resolution of 1024x768 - Compatibility with Windows 98, XP, 8, or 10, available in both 32-bit and 64-bit versions No Java or DotNet installations are necessary, making it a lightweight option for users. This software is designed with green energy in mind, taking steps to contribute positively to the environment while providing powerful database solutions.

ClickHouse is a highly efficient, open-source OLAP database management system that is specifically engineered for rapid data processing. Its unique column-oriented design allows users to generate analytical reports through real-time SQL queries with ease. In comparison to other column-oriented databases, ClickHouse demonstrates superior performance capabilities. This system can efficiently manage hundreds of millions to over a billion rows and can process tens of gigabytes of data per second on a single server. By optimizing hardware utilization, ClickHouse guarantees swift query execution. For individual queries, its maximum processing ability can surpass 2 terabytes per second, focusing solely on the relevant columns after decompression. When deployed in a distributed setup, read operations are seamlessly optimized across various replicas to reduce latency effectively. Furthermore, ClickHouse incorporates multi-master asynchronous replication, which supports deployment across multiple data centers. Each node functions independently, thus preventing any single points of failure and significantly improving overall system reliability. This robust architecture not only allows organizations to sustain high availability but also ensures consistent performance, even when faced with substantial workloads, making it an ideal choice for businesses with demanding data requirements.

Trino is an exceptionally swift query engine engineered for remarkable performance. This high-efficiency, distributed SQL query engine is specifically designed for big data analytics, allowing users to explore their extensive data landscapes. Built for peak efficiency, Trino shines in low-latency analytics and is widely adopted by some of the biggest companies worldwide to execute queries on exabyte-scale data lakes and massive data warehouses. It supports various use cases, such as interactive ad-hoc analytics, long-running batch queries that can extend for hours, and high-throughput applications that demand quick sub-second query responses. Complying with ANSI SQL standards, Trino is compatible with well-known business intelligence tools like R, Tableau, Power BI, and Superset. Additionally, it enables users to query data directly from diverse sources, including Hadoop, S3, Cassandra, and MySQL, thereby removing the burdensome, slow, and error-prone processes related to data copying. This feature allows users to efficiently access and analyze data from different systems within a single query. Consequently, Trino's flexibility and power position it as an invaluable tool in the current data-driven era, driving innovation and efficiency across industries.

Tabular is a cutting-edge open table storage solution developed by the same team that created Apache Iceberg, facilitating smooth integration with a variety of computing engines and frameworks. By utilizing this advanced technology, users can dramatically decrease both query durations and storage costs, potentially achieving reductions of up to 50%. The platform centralizes the application of role-based access control (RBAC) policies, thereby ensuring the consistent maintenance of data security. It supports multiple query engines and frameworks, including Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python, which allows for remarkable flexibility. With features such as intelligent compaction, clustering, and other automated data services, Tabular further boosts efficiency by lowering storage expenses and accelerating query performance. It facilitates unified access to data across different levels, whether at the database or table scale. Additionally, the management of RBAC controls is user-friendly, ensuring that security measures are both consistent and easily auditable. Tabular stands out for its usability, providing strong ingestion capabilities and performance, all while ensuring effective management of RBAC. Ultimately, it empowers users to choose from a range of high-performance compute engines, each optimized for their unique strengths, while also allowing for detailed privilege assignments at the database, table, or even column level. This rich combination of features establishes Tabular as a formidable asset for contemporary data management, positioning it to meet the evolving needs of businesses in an increasingly data-driven landscape.

PuppyGraph enables users to seamlessly query one or more data sources through an integrated graph model. Unlike traditional graph databases, which can be expensive, require significant setup time, and demand a specialized team for upkeep, PuppyGraph streamlines the process. Many conventional systems can take hours to run multi-hop queries and struggle with managing datasets exceeding 100GB. Utilizing a separate graph database can complicate your architecture due to fragile ETL processes, which can ultimately raise the total cost of ownership (TCO). PuppyGraph, however, allows you to connect to any data source, irrespective of its location, facilitating cross-cloud and cross-region graph analytics without the need for cumbersome ETLs or data duplication. By directly integrating with your data warehouses and lakes, PuppyGraph empowers you to query your data as a graph while eliminating the hassle of building and maintaining extensive ETL pipelines commonly associated with traditional graph configurations. You can say goodbye to the delays in data access and the unreliability of ETL operations. Furthermore, PuppyGraph addresses scalability issues linked to graphs by separating computation from storage, which enhances efficient data management. Overall, this innovative solution not only boosts performance but also simplifies your overall data strategy, making it a valuable asset for any organization.

No matter if your project consists of a single table or multiple tables, StarRocks promises a remarkable performance boost of no less than 300% when stacked against other commonly used solutions. Its extensive range of connectors allows for the smooth ingestion of streaming data, capturing information in real-time and guaranteeing that you have the most current insights at your fingertips. Designed specifically for your unique use cases, the query engine enables flexible analytics without the hassle of moving data or altering SQL queries, which simplifies the scaling of your analytics capabilities as needed. Moreover, StarRocks not only accelerates the journey from data to actionable insights but also excels with its unparalleled performance, providing a comprehensive OLAP solution that meets the most common data analytics demands. Its sophisticated caching system, leveraging both memory and disk, is specifically engineered to minimize the I/O overhead linked with data retrieval from external storage, which leads to significant enhancements in query performance while ensuring overall efficiency. Furthermore, this distinctive combination of features empowers users to fully harness the potential of their data, all while avoiding unnecessary delays in their analytic processes. Ultimately, StarRocks represents a pivotal tool for those seeking to optimize their data analysis and operational productivity.

IBM Db2 Big SQL serves as an advanced hybrid SQL-on-Hadoop engine designed to enable secure and sophisticated data queries across a variety of enterprise big data sources, including Hadoop, object storage, and data warehouses. This enterprise-level engine complies with ANSI standards and features massively parallel processing (MPP) capabilities, which significantly boost query performance. Users of Db2 Big SQL can run a single database query that connects multiple data sources, such as Hadoop HDFS, WebHDFS, relational and NoSQL databases, as well as object storage solutions. The engine boasts several benefits, including low latency, high efficiency, strong data security measures, adherence to SQL standards, and robust federation capabilities, making it suitable for both ad hoc and intricate queries. Currently, Db2 Big SQL is available in two formats: one that integrates with Cloudera Data Platform and another offered as a cloud-native service on the IBM Cloud Pak® for Data platform. This flexibility enables organizations to effectively access and analyze data, conducting queries on both batch and real-time datasets from diverse sources, thereby optimizing their data operations and enhancing decision-making. Ultimately, Db2 Big SQL stands out as a comprehensive solution for efficiently managing and querying large-scale datasets in an increasingly intricate data environment, thereby supporting organizations in navigating the complexities of their data strategy.

SPListX for SharePoint is a sophisticated application that employs a rule-based query engine to enable the export of document and picture library contents, along with their associated metadata and related list items, including file attachments, directly into the Windows File System. Users of SPListX can seamlessly export an entire SharePoint site, which includes libraries, folders, documents, list items, version histories, metadata, and permissions, to any desired location within the Windows File System. This flexible tool supports a range of SharePoint versions, such as 2019, 2016, 2013, 2010, 2007, 2003, as well as Office 365, making it a dependable option for organizations operating in various SharePoint environments. Its extensive compatibility with multiple SharePoint iterations guarantees that users can effectively manage and transfer their data, no matter which specific SharePoint configuration they utilize. Additionally, the ease of use and efficiency of SPListX makes it an essential tool for businesses looking to streamline their data handling processes.

Dynamic and captivating visual representations facilitate the identification of patterns within user interactions and business activities, providing deep insights into the core calculations involved. A succinct array of sequential tasks offers a broad range of features and detailed oversight, all accomplished in under ten lines of code. An adaptable query engine empowers users to seamlessly navigate the compromises between query precision, processing efficiency, and cost, tailoring the experience to their unique needs. Presently, Motif utilizes a custom domain-specific language called Sequence Operations Language (SOL), which we believe is more user-friendly than SQL while delivering superior functionality compared to a mere drag-and-drop interface. Furthermore, we have crafted a specialized engine aimed at boosting the efficiency of sequence queries, with a deliberate focus on sacrificing irrelevant accuracy that doesn't aid in decision-making, thereby enhancing query performance. This innovative strategy not only simplifies the user experience but also elevates the efficacy of data analysis, leading to more informed decision-making and better outcomes overall.

Impala provides swift response times and supports a large number of simultaneous users for business intelligence and analytical queries within the Hadoop framework, working seamlessly with technologies such as Iceberg, various open data formats, and numerous cloud storage options. It is engineered for effortless scalability, even in multi-tenant environments. Furthermore, Impala is compatible with Hadoop's native security protocols and employs Kerberos for secure authentication, while also utilizing the Ranger module for meticulous user and application authorization based on the specific data access requirements. This compatibility allows organizations to maintain their existing file formats, data architectures, security protocols, and resource management systems, thus avoiding redundant infrastructure and unnecessary data conversions. For users already familiar with Apache Hive, Impala's compatibility with the same metadata and ODBC driver simplifies the transition process. Similar to Hive, Impala uses SQL, which eliminates the need for new implementations. Consequently, Impala enables a greater number of users to interact with a broader range of data through a centralized repository, facilitating access to valuable insights from initial data sourcing to final analysis without sacrificing efficiency. This makes Impala a vital resource for organizations aiming to improve their data engagement and analysis capabilities, ultimately fostering better decision-making and strategic planning.

The Databricks Data Intelligence Platform empowers every individual within your organization to effectively utilize data and artificial intelligence. Built on a lakehouse architecture, it creates a unified and transparent foundation for comprehensive data management and governance, further enhanced by a Data Intelligence Engine that identifies the unique attributes of your data. Organizations that thrive across various industries will be those that effectively harness the potential of data and AI. Spanning a wide range of functions from ETL processes to data warehousing and generative AI, Databricks simplifies and accelerates the achievement of your data and AI aspirations. By integrating generative AI with the synergistic benefits of a lakehouse, Databricks energizes a Data Intelligence Engine that understands the specific semantics of your data. This capability allows the platform to automatically optimize performance and manage infrastructure in a way that is customized to the requirements of your organization. Moreover, the Data Intelligence Engine is designed to recognize the unique terminology of your business, making the search and exploration of new data as easy as asking a question to a peer, thereby enhancing collaboration and efficiency. This progressive approach not only reshapes how organizations engage with their data but also cultivates a culture of informed decision-making and deeper insights, ultimately leading to sustained competitive advantages.

An advanced parallel query engine enables efficient access to both time- and symbol-indexed data. It incorporates an upgraded SQL syntax that facilitates complex filtering and extensive aggregations. This innovative system merges diverse financial data types, including market quotes, trade transactions, snapshots, and reference information, into a unified database. Users can perform strategy backtesting with high-frequency datasets, engage in quantitative research, and analyze market microstructure dynamics. The platform offers in-depth transaction cost analysis alongside rollup reporting, which ensures a comprehensive understanding of trading activities. With integrated market surveillance features and anomaly detection tools, it enhances overall monitoring capabilities. It also has the capacity to break down opaque ETFs and ETNs while employing FAST, SBE, and proprietary protocols to boost performance. A straightforward text protocol simplifies usage, and both consolidated and direct data feeds are provided for seamless data ingestion. Additionally, built-in latency monitoring tools and extensive end-of-day data archives are part of the offering. The engine supports ETL processes from both institutional and retail financial data sources, and its parallel SQL engine comes with syntax extensions that allow for advanced filtering based on various parameters, such as trading sessions and auction stages. It further provides optimized calculations for OHLCV and VWAP metrics, enhancing analytical precision. An interactive SQL console with auto-completion features improves user interaction, while an API endpoint supports programmatic integration. Scheduled SQL reports can be generated with delivery options via email, file, or web, complemented by JDBC and ODBC drivers for wider accessibility.

Qubole distinguishes itself as a user-friendly, accessible, and secure Data Lake Platform specifically designed for machine learning, streaming, and on-the-fly analysis. Our all-encompassing platform facilitates the efficient execution of Data pipelines, Streaming Analytics, and Machine Learning operations across any cloud infrastructure, significantly cutting down both time and effort involved in these processes. No other solution offers the same level of openness and flexibility for managing data workloads as Qubole, while achieving over a 50 percent reduction in expenses associated with cloud data lakes. By allowing faster access to vast amounts of secure, dependable, and credible datasets, we empower users to engage with both structured and unstructured data for a variety of analytics and machine learning tasks. Users can seamlessly conduct ETL processes, analytics, and AI/ML functions in a streamlined workflow, leveraging high-quality open-source engines along with diverse formats, libraries, and programming languages customized to meet their data complexities, service level agreements (SLAs), and organizational policies. This level of adaptability not only enhances operational efficiency but also ensures that Qubole remains the go-to choice for organizations looking to refine their data management strategies while staying at the forefront of technological innovation. Ultimately, Qubole’s commitment to continuous improvement and user satisfaction solidifies its position in the competitive landscape of data solutions.

Ensure that crashes of games, applications, or devices don't hinder your enjoyable experience. Backtrace streamlines the management of exceptions and crashes across various platforms, allowing you to concentrate on delivering your product. It provides a unified call stack, event aggregation, and comprehensive monitoring solutions. This single system efficiently handles errors from panics, core dumps, minidumps, and runtime issues across your entire stack. Backtrace creates structured and searchable error reports from your collected data. Its automated analysis significantly shortens the resolution time by highlighting critical signals that guide engineers to the root cause of crashes. With seamless integrations into various dashboards and notification systems, you can rest assured that no detail will slip through the cracks. The advanced queries engine offered by Backtrace empowers you to address your most pressing questions. A broad overview of errors, along with prioritization and trends spanning all your projects, is readily accessible. Furthermore, you can sift through essential data points and your customized information for every error, enhancing your overall troubleshooting process. This comprehensive approach ultimately leads to a more efficient workflow and improved product reliability.

Apache Spark™ is a powerful analytics platform crafted for large-scale data processing endeavors. It excels in both batch and streaming tasks by employing an advanced Directed Acyclic Graph (DAG) scheduler, a highly effective query optimizer, and a streamlined physical execution engine. With more than 80 high-level operators at its disposal, Spark greatly facilitates the creation of parallel applications. Users can engage with the framework through a variety of shells, including Scala, Python, R, and SQL. Spark also boasts a rich ecosystem of libraries—such as SQL and DataFrames, MLlib for machine learning, GraphX for graph analysis, and Spark Streaming for processing real-time data—which can be effortlessly woven together in a single application. This platform's versatility allows it to operate across different environments, including Hadoop, Apache Mesos, Kubernetes, standalone systems, or cloud platforms. Additionally, it can interface with numerous data sources, granting access to information stored in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and many other systems, thereby offering the flexibility to accommodate a wide range of data processing requirements. Such a comprehensive array of functionalities makes Spark a vital resource for both data engineers and analysts, who rely on it for efficient data management and analysis. The combination of its capabilities ensures that users can tackle complex data challenges with greater ease and speed.