Best Query Engines for Small Business

BigQuery boasts a powerful query engine that excels at executing large-scale queries on extensive datasets with impressive speed and efficiency. Its serverless design enables organizations to conduct high-performance queries without the hassle of managing servers or infrastructure. The SQL-based query engine is accessible to most data analysts, facilitating a smooth onboarding process for intricate data analysis tasks. New users can take advantage of $300 in complimentary credits to experiment with the query engine, allowing them to perform various queries and evaluate how BigQuery can meet their analytical requirements. Additionally, the platform is engineered for scalability, ensuring that query performance remains reliable as data volumes increase.

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.

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

Snowflake is a cloud-native data platform that combines data warehousing, data lakes, and data sharing into a single solution. By offering elastic scalability and automatic scaling, Snowflake enables businesses to handle vast amounts of data while maintaining high performance at low cost. The platform's architecture allows users to separate storage and compute, offering flexibility in managing workloads. Snowflake supports real-time data sharing and integrates seamlessly with other analytics tools, enabling teams to collaborate and gain insights from their data more efficiently. Its secure, multi-cloud architecture makes it a strong choice for enterprises looking to leverage data at scale.

Amazon Athena serves as an interactive query service that simplifies the process of analyzing data stored in Amazon S3 through the use of standard SQL. As a serverless service, it eliminates the need for infrastructure management, allowing users to pay solely for the queries they execute. The user-friendly interface enables you to simply point to your data in Amazon S3, establish the schema, and begin querying with standard SQL commands, with most results returning in mere seconds. Athena negates the requirement for intricate ETL processes to prepare data for analysis, making it accessible for anyone possessing SQL skills to swiftly examine large datasets. Additionally, Athena integrates seamlessly with AWS Glue Data Catalog, which facilitates the creation of a consolidated metadata repository across multiple services. This integration allows users to crawl data sources to identify schemas, update the Catalog with new and modified table and partition definitions, and manage schema versioning effectively. Not only does this streamline data management, but it also enhances the overall efficiency of data analysis within the AWS ecosystem.

Apache Hive is a data warehousing solution that enables users to read, write, and manage extensive datasets stored across distributed systems utilizing SQL. It allows for the imposition of structure on existing stored data. Users can connect with Hive through a command line interface and a JDBC driver. As an open-source initiative, Apache Hive is maintained by dedicated volunteers at the Apache Software Foundation. Initially, it was part of the Apache® Hadoop® ecosystem but has since evolved into a standalone top-level project. We invite those interested to explore the project further and share their skills. To run SQL applications and queries on distributed datasets, traditional SQL queries need to be executed via the MapReduce Java API. However, Hive simplifies this process by offering a SQL abstraction that allows users to execute SQL-like queries known as HiveQL, without requiring the implementation of low-level Java API queries. This makes working with large datasets more accessible and efficient for users familiar with SQL.

ClickHouse is an efficient, open-source OLAP database management system designed for high-speed data processing. Its column-oriented architecture facilitates the creation of analytical reports through real-time SQL queries. In terms of performance, ClickHouse outshines similar column-oriented database systems currently on the market. It has the capability to handle hundreds of millions to over a billion rows, as well as tens of gigabytes of data, on a single server per second. By maximizing the use of available hardware, ClickHouse ensures rapid query execution. The peak processing capacity for individual queries can exceed 2 terabytes per second, considering only the utilized columns after decompression. In a distributed environment, read operations are automatically optimized across available replicas to minimize latency. Additionally, ClickHouse features multi-master asynchronous replication, enabling deployment across various data centers. Each node operates equally, effectively eliminating potential single points of failure and enhancing overall reliability. This robust architecture allows organizations to maintain high availability and performance even under heavy workloads.

Trino is a remarkably fast query engine designed to operate at exceptional speeds. It serves as a high-performance, distributed SQL query engine tailored for big data analytics, enabling users to delve into their vast data environments. Constructed for optimal efficiency, Trino excels in low-latency analytics and is extensively utilized by some of the largest enterprises globally to perform queries on exabyte-scale data lakes and enormous data warehouses. It accommodates a variety of scenarios, including interactive ad-hoc analytics, extensive batch queries spanning several hours, and high-throughput applications that require rapid sub-second query responses. Trino adheres to ANSI SQL standards, making it compatible with popular business intelligence tools like R, Tableau, Power BI, and Superset. Moreover, it allows direct querying of data from various sources such as Hadoop, S3, Cassandra, and MySQL, eliminating the need for cumbersome, time-consuming, and error-prone data copying processes. This capability empowers users to access and analyze data from multiple systems seamlessly within a single query. Such versatility makes Trino a powerful asset in today's data-driven landscape.

Tabular is an innovative open table storage solution designed by the same team behind Apache Iceberg, allowing seamless integration with various computing engines and frameworks. By leveraging this technology, users can significantly reduce both query times and storage expenses, achieving savings of up to 50%. It centralizes the enforcement of role-based access control (RBAC) policies, ensuring data security is consistently maintained. The platform is compatible with multiple query engines and frameworks, such as Athena, BigQuery, Redshift, Snowflake, Databricks, Trino, Spark, and Python, offering extensive flexibility. With features like intelligent compaction and clustering, as well as other automated data services, Tabular further enhances efficiency by minimizing storage costs and speeding up query performance. It allows for unified data access at various levels, whether at the database or table. Additionally, managing RBAC controls is straightforward, ensuring that security measures are not only consistent but also easily auditable. Tabular excels in usability, providing robust ingestion capabilities and performance, all while maintaining effective RBAC management. Ultimately, it empowers users to select from a variety of top-tier compute engines, each tailored to their specific strengths, while also enabling precise privilege assignments at the database, table, or even column level. This combination of features makes Tabular a powerful tool for modern data management.

PuppyGraph allows you to effortlessly query one or multiple data sources through a cohesive graph model. Traditional graph databases can be costly, require extensive setup time, and necessitate a specialized team to maintain. They often take hours to execute multi-hop queries and encounter difficulties when managing datasets larger than 100GB. Having a separate graph database can complicate your overall architecture due to fragile ETL processes, ultimately leading to increased total cost of ownership (TCO). With PuppyGraph, you can connect to any data source, regardless of its location, enabling cross-cloud and cross-region graph analytics without the need for intricate ETLs or data duplication. By directly linking to your data warehouses and lakes, PuppyGraph allows you to query your data as a graph without the burden of constructing and maintaining lengthy ETL pipelines typical of conventional graph database configurations. There's no longer a need to deal with delays in data access or unreliable ETL operations. Additionally, PuppyGraph resolves scalability challenges associated with graphs by decoupling computation from storage, allowing for more efficient data handling. This innovative approach not only enhances performance but also simplifies your data management strategy.

Regardless of whether your project involves a single table or numerous tables, StarRocks guarantees an impressive performance improvement of at least 300% when compared to other widely used solutions. With its comprehensive array of connectors, you can seamlessly ingest streaming data and capture information in real time, ensuring that you always have access to the latest insights. The query engine is tailored to suit your specific use cases, allowing for adaptable analytics without the need to relocate data or modify SQL queries. This provides an effortless way to scale your analytics capabilities as required. StarRocks not only facilitates a swift transition from data to actionable insights, but also stands out with its unmatched performance, offering a holistic OLAP solution that addresses the most prevalent data analytics requirements. Its advanced memory-and-disk-based caching framework is purpose-built to reduce I/O overhead associated with retrieving data from external storage, significantly enhancing query performance while maintaining efficiency. This unique combination of features ensures that users can maximize their data's potential without unnecessary delays.

Timeplus is an efficient, user-friendly stream processing platform that is both powerful and affordable. It comes packaged as a single binary, making it easy to deploy in various environments. Designed for data teams across diverse sectors, it enables the quick and intuitive processing of both streaming and historical data. With a lightweight design that requires no external dependencies, Timeplus offers comprehensive analytic capabilities for streaming and historical data. Its cost is just a fraction—1/10—of what similar open-source frameworks charge. Users can transform real-time market and transaction data into actionable insights seamlessly. The platform supports both append-only and key-value streams, making it ideal for monitoring financial information. Additionally, Timeplus allows the creation of real-time feature pipelines effortlessly. It serves as a unified solution for managing all infrastructure logs, metrics, and traces, which are essential for maintaining observability. Timeplus also accommodates a broad array of data sources through its user-friendly web console UI, while providing options to push data via REST API or to create external streams without the need to copy data into the platform. Overall, Timeplus offers a versatile and comprehensive approach to data processing for organizations looking to enhance their operational efficiency.

Starburst empowers organizations to enhance their decision-making capabilities by providing rapid access to all their data without the hassle of transferring or duplicating it. As companies accumulate vast amounts of data, their analysis teams often find themselves waiting for access to perform their evaluations. By facilitating direct access to data at its source, Starburst ensures that teams can quickly and accurately analyze larger datasets without the need for data movement. Starburst Enterprise offers a robust, enterprise-grade version of the open-source Trino (formerly known as Presto® SQL), which is fully supported and tested for production use. This solution not only boosts performance and security but also simplifies the deployment, connection, and management of a Trino environment. By enabling connections to any data source—be it on-premises, in the cloud, or within a hybrid cloud setup—Starburst allows teams to utilize their preferred analytics tools while seamlessly accessing data stored in various locations. This innovative approach significantly reduces the time taken for insights, helping businesses stay competitive in a data-driven world.

IBM Db2 Big SQL is a sophisticated hybrid SQL-on-Hadoop engine that facilitates secure and advanced data querying across a range of enterprise big data sources, such as Hadoop, object storage, and data warehouses. This enterprise-grade engine adheres to ANSI standards and provides massively parallel processing (MPP) capabilities, enhancing the efficiency of data queries. With Db2 Big SQL, users can execute a single database connection or query that spans diverse sources, including Hadoop HDFS, WebHDFS, relational databases, NoSQL databases, and object storage solutions. It offers numerous advantages, including low latency, high performance, robust data security, compatibility with SQL standards, and powerful federation features, enabling both ad hoc and complex queries. Currently, Db2 Big SQL is offered in two distinct variations: one that integrates seamlessly with Cloudera Data Platform and another as a cloud-native service on the IBM Cloud Pak® for Data platform. This versatility allows organizations to access and analyze data effectively, performing queries on both batch and real-time data across various sources, thus streamlining their data operations and decision-making processes. In essence, Db2 Big SQL provides a comprehensive solution for managing and querying extensive datasets in an increasingly complex data landscape.

SPListX for SharePoint is an advanced application that uses a rule-based query engine to facilitate the exportation of document and picture library contents along with their metadata and related list items, including file attachments, directly to the Windows File System. With SPListX, users can export an entire SharePoint site, encompassing libraries, folders, documents, list items, version histories, metadata, and permissions, to their preferred location within the Windows File System. This versatile tool is compatible with various versions of SharePoint, including 2019, 2016, 2013, 2010, 2007, 2003, as well as Office 365, making it a reliable choice for organizations utilizing different SharePoint environments. Its comprehensive support for multiple SharePoint versions ensures that users can efficiently manage and transfer their data regardless of the specific SharePoint setup they are employing.

Dynamic and engaging visualizations enable the discovery of trends within user and business processes, offering comprehensive insight into the foundational computations. A concise collection of sequential operations delivers extensive functionality and meticulous control, all achievable in fewer than ten lines of code. An adaptive query engine allows users to effortlessly balance the trade-offs between query accuracy, processing speed, and costs to suit their specific requirements. Currently, Motif employs a specialized domain-specific language known as Sequence Operations Language (SOL), which we find to be more intuitive than SQL while providing greater capabilities than a simple drag-and-drop interface. Additionally, we have developed a bespoke engine designed to enhance the efficiency of sequence queries, while strategically sacrificing unnecessary precision that does not contribute to decision-making, in favor of improving query performance. This approach not only streamlines the user experience but also maximizes the effectiveness of data analysis.

Impala delivers rapid response times and accommodates a high number of concurrent users for business intelligence and analytical queries within the Hadoop ecosystem, supporting frameworks like Iceberg, various open data formats, and numerous cloud storage solutions. It is designed to scale seamlessly, even in environments that host multiple tenants. Additionally, Impala integrates with native Hadoop security protocols and utilizes Kerberos for authentication, while the Ranger module allows for precise user and application authorization based on the data they need to access. This means you can leverage the same file formats, data structures, security measures, and resource management systems as your existing Hadoop setup, eliminating the need for redundant infrastructure or unnecessary data transformations. For those already using Apache Hive, Impala is compatible, sharing the same metadata and ODBC driver, which streamlines the transition. Just like Hive, Impala employs SQL, thereby alleviating the need to develop new implementations. With Impala, a greater number of users can engage with a wider array of data via a unified repository, ensuring that valuable insights are accessible from the source to analysis without compromising on efficiency. Ultimately, this makes Impala an essential tool for organizations looking to enhance their data interaction capabilities.

The Databricks Data Intelligence Platform empowers every member of your organization to leverage data and artificial intelligence effectively. Constructed on a lakehouse architecture, it establishes a cohesive and transparent foundation for all aspects of data management and governance, enhanced by a Data Intelligence Engine that recognizes the distinct characteristics of your data. Companies that excel across various sectors will be those that harness the power of data and AI. Covering everything from ETL processes to data warehousing and generative AI, Databricks facilitates the streamlining and acceleration of your data and AI objectives. By merging generative AI with the integrative advantages of a lakehouse, Databricks fuels a Data Intelligence Engine that comprehends the specific semantics of your data. This functionality enables the platform to optimize performance automatically and manage infrastructure in a manner tailored to your organization's needs. Additionally, the Data Intelligence Engine is designed to grasp the unique language of your enterprise, making the search and exploration of new data as straightforward as posing a question to a colleague, thus fostering collaboration and efficiency. Ultimately, this innovative approach transforms the way organizations interact with their data, driving better decision-making and insights.

A parallel query engine facilitates access to time- and symbol-indexed data efficiently. It features an enhanced SQL syntax that allows for sophisticated filtering and comprehensive aggregations. This system consolidates various types of financial information, such as quotes, trades, snapshots, and reference data, into a single repository. Users can conduct strategy backtesting utilizing high-frequency data and engage in quantitative research as well as market microstructure analysis. The platform provides detailed transaction cost analysis and allows for rollup reporting, ensuring thorough insight into trading activities. It also includes market surveillance capabilities and tools for detecting anomalies. Moreover, it can decompose non-transparent ETFs and ETNs, utilizing FAST, SBE, and proprietary protocols for improved performance. A plain text protocol ensures ease of use, while both consolidated and direct feeds are available for data ingestion. Built-in tools for monitoring latency are included, along with comprehensive end-of-day archives. The engine supports ETL processes from both institutional and retail financial data sources. It boasts a parallel SQL engine with syntax extensions, allowing advanced filtering by various criteria such as trading session and auction stage. Additionally, it offers optimized aggregate calculations for OHLCV and VWAP metrics. An interactive SQL console equipped with auto-completion enhances user experience, and an API endpoint facilitates programmatic integration. Scheduled SQL reporting is available with options for delivery via email, file, or web, along with JDBC and ODBC drivers for broader accessibility. This robust system is designed to meet the demands of modern financial analysis and trading strategies.

If you are looking to provide your clients with online access to their LIMS data and reports, AIS labPortal can help you achieve that goal seamlessly. There is no need to mail paper copies of sample analyses to customers anymore. With a unique login and secure password, clients can conveniently retrieve their data from any computer, making the process not only safer and more efficient but also environmentally sustainable. labPortal serves as a secure, cloud-based platform where clients can quickly access their sample information from their desktop, tablet, or smartphone. The user-friendly 'inbox' style interface features an advanced query engine, conditional highlighting, and the option to export data to Microsoft Excel. Additionally, the software includes a straightforward sample registration form, enabling users to pre-register samples online with ease. Eliminating the need for manual data transcription saves valuable time and reduces the potential for errors in reporting. Overall, AIS labPortal offers a modern solution to streamline data access and enhance client satisfaction.

Qubole stands out as a straightforward, accessible, and secure Data Lake Platform tailored for machine learning, streaming, and ad-hoc analysis. Our comprehensive platform streamlines the execution of Data pipelines, Streaming Analytics, and Machine Learning tasks across any cloud environment, significantly minimizing both time and effort. No other solution matches the openness and versatility in handling data workloads that Qubole provides, all while achieving a reduction in cloud data lake expenses by more than 50 percent. By enabling quicker access to extensive petabytes of secure, reliable, and trustworthy datasets, we empower users to work with both structured and unstructured data for Analytics and Machine Learning purposes. Users can efficiently perform ETL processes, analytics, and AI/ML tasks in a seamless workflow, utilizing top-tier open-source engines along with a variety of formats, libraries, and programming languages tailored to their data's volume, diversity, service level agreements (SLAs), and organizational regulations. This adaptability ensures that Qubole remains a preferred choice for organizations aiming to optimize their data management strategies while leveraging the latest technological advancements.

QuasarDB, the core of Quasar's intelligence, is an advanced, distributed, column-oriented database management system specifically engineered for high-performance timeseries data handling, enabling real-time processing for massive petascale applications. It boasts up to 20 times less disk space requirement, making it exceptionally efficient. The unmatched ingestion and compression features of QuasarDB allow for up to 10,000 times quicker feature extraction. This database can perform real-time feature extraction directly from raw data via an integrated map/reduce query engine, a sophisticated aggregation engine that utilizes SIMD capabilities of contemporary CPUs, and stochastic indexes that consume minimal disk storage. Its ultra-efficient resource utilization, ability to integrate with object storage solutions like S3, innovative compression methods, and reasonable pricing structure make it the most economical timeseries solution available. Furthermore, QuasarDB is versatile enough to operate seamlessly across various platforms, from 32-bit ARM devices to high-performance Intel servers, accommodating both Edge Computing environments and traditional cloud or on-premises deployments. Its scalability and efficiency make it an ideal choice for businesses aiming to harness the full potential of their data in real-time.

Presto serves as an open-source distributed SQL query engine designed for executing interactive analytic queries across data sources that can range in size from gigabytes to petabytes. It addresses the challenges faced by data engineers who often navigate multiple query languages and interfaces tied to isolated databases and storage systems. Presto stands out as a quick and dependable solution by offering a unified ANSI SQL interface for comprehensive data analytics and your open lakehouse. Relying on different engines for various workloads often leads to the necessity of re-platforming in the future. However, with Presto, you benefit from a singular, familiar ANSI SQL language and one engine for all your analytic needs, negating the need to transition to another lakehouse engine. Additionally, it efficiently accommodates both interactive and batch workloads, handling small to large datasets and scaling from just a few users to thousands. By providing a straightforward ANSI SQL interface for all your data residing in varied siloed systems, Presto effectively integrates your entire data ecosystem, fostering seamless collaboration and accessibility across platforms. Ultimately, this integration empowers organizations to make more informed decisions based on a comprehensive view of their data landscape.

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.

PySpark serves as the Python interface for Apache Spark, enabling the development of Spark applications through Python APIs and offering an interactive shell for data analysis in a distributed setting. In addition to facilitating Python-based development, PySpark encompasses a wide range of Spark functionalities, including Spark SQL, DataFrame support, Streaming capabilities, MLlib for machine learning, and the core features of Spark itself. Spark SQL, a dedicated module within Spark, specializes in structured data processing and introduces a programming abstraction known as DataFrame, functioning also as a distributed SQL query engine. Leveraging the capabilities of Spark, the streaming component allows for the execution of advanced interactive and analytical applications that can process both real-time and historical data, while maintaining the inherent advantages of Spark, such as user-friendliness and robust fault tolerance. Furthermore, PySpark's integration with these features empowers users to handle complex data operations efficiently across various datasets.