Best Free Query Engines of 2025

StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.

ClickHouse is an open-source OLAP database management software that is fast and easy to use. It is column-oriented, and can generate real-time analytical reports by using SQL queries. ClickHouse's performance is superior to comparable column-oriented database management software currently on the market. It processes hundreds of millions of rows to more than a million and tens if not thousands of gigabytes per second. ClickHouse makes use of all hardware available to process every query as quickly as possible. Peak processing speed for a single query is more than 2 Terabytes per Second (after decompression, only utilized columns). To reduce latency, reads in distributed setups are automatically balanced between healthy replicas. ClickHouse supports multimaster asynchronous replication, and can be deployed across multiple datacenters. Each node is equal, which prevents single points of failure.

ANSI SQL allows you to analyze petabytes worth of data at lightning-fast speeds with no operational overhead. Analytics at scale with 26%-34% less three-year TCO than cloud-based data warehouse alternatives. You can unleash your insights with a trusted platform that is more secure and scales with you. Multi-cloud analytics solutions that allow you to gain insights from all types of data. You can query streaming data in real-time and get the most current information about all your business processes. Machine learning is built-in and allows you to predict business outcomes quickly without having to move data. With just a few clicks, you can securely access and share the analytical insights within your organization. Easy creation of stunning dashboards and reports using popular business intelligence tools right out of the box. BigQuery's strong security, governance, and reliability controls ensure high availability and a 99.9% uptime SLA. Encrypt your data by default and with customer-managed encryption keys

Trino is an engine that runs at incredible speeds. Fast-distributed SQL engine for big data analytics. Helps you explore the data universe. Trino is an extremely parallel and distributed query-engine, which is built from scratch for efficient, low latency analytics. Trino is used by the largest organizations to query data lakes with exabytes of data and massive data warehouses. Supports a wide range of use cases including interactive ad-hoc analysis, large batch queries that take hours to complete, and high volume apps that execute sub-second queries. Trino is a ANSI SQL query engine that works with BI Tools such as R Tableau Power BI Superset and many others. You can natively search data in Hadoop S3, Cassandra MySQL and many other systems without having to use complex, slow and error-prone copying processes. Access data from multiple systems in a single query.

You can create flat or relational databases with unlimited fields, tables, and rows. A custom report builder is included. Create custom reports by connecting to compatible ODBC databases. You can create your own databases. Here are some highlights: Filter tables instantly - Ultra simple graphical-user-interface - One-click table and data form creation - You can open up to 5 databases simultaneously Export your data to comma-separated files - Create custom reports to all your databases - A complete helpfile for creating database reports - You can print tables and queries directly from your data grid - Supports any SQL standard your ODBC compatible databases require For best performance and user experience, please install and run this database app with full administrator rights. Requirements: . 1024x768 Display Size . Windows 98 / XP / Windows 8 / Windows 10 - 32bit or 64bit No Java or DotNet are required. Green Energy Software. One step at a time, saving the planet

Tabular is a table store that allows you to create an open table. It was created by the Apache Iceberg creators. Connect multiple computing frameworks and engines. Reduce query time and costs up to 50%. Centralize enforcement of RBAC policies. Connect any query engine, framework, or tool, including Athena BigQuery, Snowflake Databricks Trino Spark Python, Snowflake Redshift, Snowflake Databricks and Redshift. Smart compaction, data clustering and other automated services reduce storage costs by up to 50% and query times. Unify data access in the database or table. RBAC controls are easy to manage, enforce consistently, and audit. Centralize your security at the table. Tabular is easy-to-use and has RBAC, high-powered performance, and high ingestion under the hood. Tabular allows you to choose from multiple "best-of-breed" compute engines, based on their strengths. Assign privileges to the data warehouse database or table level.

Impala offers low latency, high concurrency, and a wide range of storage options, including Iceberg and open data formats. Impala scales linearly in multitenant environments. Impala integrates native Hadoop security, Kerberos authentication, and the Ranger module to ensure that the correct users and applications have access to 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. Impala uses the same metadata driver and ODBC driver as Apache Hive. Impala, like Hive, supports SQL. You don't need to reinvent the wheel. Impala allows more users to interact with data, whether they are using SQL queries or BI apps, through a single repository. Metadata is also stored from the source of the data until it has been analyzed.

PuppyGraph allows you to query multiple data stores in a single graph model. Graph databases can be expensive, require months of setup, and require a dedicated team. Traditional graph databases struggle to handle data beyond 100GB and can take hours to run queries with multiple hops. A separate graph database complicates architecture with fragile ETLs, and increases your total cost ownership (TCO). Connect to any data source, anywhere. Cross-cloud and cross region graph analytics. No ETLs are required, nor is data replication. PuppyGraph allows you to query data as a graph directly from your data lakes and warehouses. This eliminates the need for time-consuming ETL processes that are required with a traditional graph databases setup. No more data delays or failed ETL processes. PuppyGraph eliminates graph scaling issues by separating computation from storage.

StarRocks offers at least 300% more performance than other popular solutions, whether you're using a single or multiple tables. With a rich set connectors, you can ingest real-time data into StarRocks for the latest insights. A query engine that adapts your use cases. StarRocks allows you to scale your analytics easily without moving your data or rewriting SQL. StarRocks allows a rapid journey between data and insight. StarRocks is unmatched in performance and offers a unified OLAP system that covers the most common data analytics scenarios. StarRocks offers at least 300% faster performance than other popular solutions, whether you are working with one table or many. StarRocks' built-in memory-and-disk-based caching framework is specifically designed to minimize the I/O overhead of fetching data from external storage to accelerate query performance.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

Parallel query engine with symbol- and time-indexed data access. Extended SQL syntax with advanced filtering, aggregations and more. Consolidate all quotes, trades and snapshots in one place. Strategy backtesting using high-frequency data. Quantitative and market microstructure analysis. Granular transaction cost analysis and rollup report. Market surveillance and anomaly detection. Non-transparent ETF/ETN decomposition. FAST, SBE and proprietary protocols. Plain text protocol. Consolidated and direct feeds. Built-in latency monitoring tools. End-of-day archives. ETL from retail and institutional financial data platforms. Parallel SQL engine with syntax extensions. Advanced filtering via trading session, auction stage, and index composition. Optimized aggregates to OHLCV and VWAP calculations. Interactive SQL console with auto completion. API endpoint for programmatic integrtion. Scheduled SQL reporting via email, file, or web delivery. JDBC and ODBC drivers.

Qubole is an open, secure, and simple Data Lake Platform that enables machine learning, streaming, or ad-hoc analysis. Our platform offers end-to-end services to reduce the time and effort needed to run Data pipelines and Streaming Analytics workloads on any cloud. Qubole is the only platform that offers more flexibility and openness for data workloads, while also lowering cloud data lake costs up to 50%. Qubole provides faster access to trusted, secure and reliable datasets of structured and unstructured data. This is useful for Machine Learning and Analytics. Users can efficiently perform ETL, analytics, or AI/ML workloads in an end-to-end fashion using best-of-breed engines, multiple formats and libraries, as well as languages that are adapted to data volume and variety, SLAs, and organizational policies.

Don't let game, app, or device crashes stop you from having a great experience. Backtrace automates cross-platform exception management and cross-platform crash management so that you can focus on shipping. Cross-platform callstack, event aggregation, and monitoring. A single system can process errors from panics and core dumps, minidumps, as well as during runtime across your stack. Backtrace generates searchable, structured error reports from your data. Automated analysis reduces time to resolution by surfacing important signals which lead engineers to the crash root cause. Rich integrations into dashboards and notification systems mean that you don't have to worry about missing a detail. Backtrace's rich queries engine will help you answer the questions that are most important to you. A high-level overview of errors, prioritization and trends across all projects can be viewed. You can search through key data points as well as your own custom data for all errors.

A hybrid SQL-onHadoop engine that delivers advanced, security-rich data queries across enterprise big data sources including Hadoop object storage and data warehouses. IBM Db2 Big SQL, an enterprise-grade, hybrid ANSI compliant SQL-on-Hadoop engine that delivers massively parallel processing and advanced data query, is available. Db2 Big SQL allows you to connect to multiple sources, such as Hadoop HDFS and WebHDFS. RDMS, NoSQL database, object stores, and RDMS. You can benefit from low latency, high speed, data security, SQL compatibility and federation capabilities to perform complex and ad-hoc queries. Db2 Big SQL now comes in two versions. It can be integrated with Cloudera Data Platform or accessed as a cloud native service on the IBM Cloud Pak®. for Data platform. Access, analyze, and perform queries on real-time and batch data from multiple sources, including Hadoop, object stores, and data warehouses.

SPListX for SharePoint allows you to export picture library contents, metadata, list items and associated file attachments to Windows File System. SharePoint site, libraries and folders can be exported to Windows File System. SPListX supports SharePoint 2019 / SharePoint 2016, SharePoint 2013 / SharePoint 2010, SharePoint 2007 / SharePoint 2003 & Office 365.

Rich interactive visualizations to identify patterns in user and company flows with full visibility of computation. In less than 10 lines of code, a small set of sequence operators can provide full expressivity and finely-grained control. A query engine that allows you to trade between query speed, precision and cost according your needs. Motif currently uses a custom-built DSL, called Sequence Operations Language. We believe it is more natural than SQL and more powerful that a drag-and drop interface. We built a custom algorithm to optimize sequence queries. We also trade off precision for query speed, which is not used in decision-making.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.