Best Database Software for SQL - Page 2

Tobiko is a data transformation platform that ships data faster, more efficiently, and with fewer mistakes, backward compatible with databases. Make a dev environment without rebuilding the entire DAG. Tobiko only changes what's necessary. Don't rebuild everything when you add a column. You already built your change. Tobiko promotes prod instantly without redoing your work. Avoid debugging clunky Jinja and define your models in SQL. Tobiko works at a startup and at an enterprise scale. Tobiko understands the SQL you write and improves developer productivity by finding issues at compile time. Audits and data differences provide validation and make it easy to trust the datasets you produce. Every change is analyzed and is automatically categorized as either breaking or non-breaking. When mistakes happen, seamlessly roll back to the prior version, allowing teams to reduce downtime in production.

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.

HarperDB is a distributed systems platform that combines database, caching, application, and streaming functions into a single technology. With it, you can start delivering global-scale back-end services with less effort, higher performance, and lower cost than ever before. Deploy user-programmed applications and pre-built add-ons on top of the data they depend on for a high throughput, ultra-low latency back end. Lightning-fast distributed database delivers orders of magnitude more throughput per second than popular NoSQL alternatives while providing limitless horizontal scale. Native real-time pub/sub communication and data processing via MQTT, WebSocket, and HTTP interfaces. HarperDB delivers powerful data-in-motion capabilities without layering in additional services like Kafka. Focus on features that move your business forward, not fighting complex infrastructure. You can't change the speed of light, but you can put less light between your users and their data.

Turso is a globally distributed, SQLite-compatible database service designed to provide low-latency data access across various platforms, including online, offline, and on-device environments. Built atop libSQL, an open-source fork of SQLite, Turso enables developers to deploy databases close to their users, enhancing application performance. It supports seamless integration with multiple frameworks, languages, and infrastructure providers, facilitating efficient data management for applications such as personalized large language models and AI agents. Turso offers features like unlimited databases, instant rollback with branching, and native vector search at scale, allowing for efficient parallel vector searches across users, instances, or contexts using SQL database integration. The platform emphasizes security with encryption at rest and in transit and provides an API-first approach for programmatic database management.

MySQLTuner is a lightweight, Perl-based script designed to assist database administrators in optimizing the performance and configuration of MySQL and MariaDB databases. It analyzes key metrics and parameters related to system performance, security, and resource usage, providing actionable recommendations for improvement. By examining the current workload, cache efficiency, query performance, and memory usage, MySQLTuner identifies potential bottlenecks and suggests adjustments to configuration files. It is particularly valuable for ongoing maintenance, as it highlights issues such as inefficient indexes, excessive temporary tables, or suboptimal query caching. MySQLTuner is widely used by database administrators to ensure optimal performance, reduce system overhead, and maintain a healthy, efficient database environment.

SQLPro Studio is a fully native, cross-platform database management tool. It supports a wide range of database systems and provides several features including syntax highlighting, SQL autocompletion, customizable themes, and tabbed interfaces for managing multiple queries simultaneously. It also offers cloud support for services like Amazon RDS, Microsoft Azure, and Heroku, along with SSH tunneling and master password protection for enhanced security. SQLPro Studio is optimized for performance and user experience, providing an intuitive interface without relying on Java, ensuring faster and more reliable operation. You can execute multiple queries and have the results for each displayed at the same time, including any errors and messages. SQLPro boasts an intuitive interface, where the most basic to advanced database needs are easily accessible.

Apache DataFusion is an extensible, high-performance query engine written in Rust that utilizes Apache Arrow as its in-memory format. Designed for developers building data-centric systems such as databases, data frames, machine learning, and streaming applications, DataFusion offers SQL and DataFrame APIs, a vectorized, multi-threaded, streaming execution engine, and support for partitioned data sources. It natively supports formats like CSV, Parquet, JSON, and Avro, and allows for seamless integration with object stores including AWS S3, Azure Blob Storage, and Google Cloud Storage. The engine features a comprehensive query planner, a state-of-the-art optimizer with capabilities like expression coercion and simplification, projection and filter pushdown, sort and distribution-aware optimizations, and automatic join reordering. DataFusion is highly customizable, enabling the addition of user-defined scalar, aggregate, and window functions, custom data sources, query languages, etc.

Oracle Autonomous Database is a fully automated cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. It supports a wide range of data types and models, including SQL, JSON documents, graph, geospatial, text, and vectors, enabling developers to build applications for any workload without integrating multiple specialty databases. Built-in AI and machine learning capabilities allow for natural language queries, automated data insights, and the development of AI-powered applications. It offers self-service tools for data loading, transformation, analysis, and governance, reducing the need for IT intervention. It provides flexible deployment options, including serverless and dedicated infrastructure on Oracle Cloud Infrastructure (OCI), as well as on-premises with Exadata Cloud@Customer.

Convex is an open source, reactive backend platform that enables developers to build full-stack applications entirely in TypeScript. It offers a document-relational database where queries and mutations are written in TypeScript, ensuring end-to-end type safety and seamless integration with frontend code. Convex's libraries maintain real-time synchronization between the frontend, backend, and database state without the need for manual state management, cache invalidation, or WebSockets. It includes built-in support for cloud functions, scheduling, authentication, file storage, and a variety of components that can be added with a simple npm i command. Developers can define their entire backend, including database schemas, queries, and APIs, in code, which is typechecked and autocompleted, and can be generated by AI with high accuracy. Convex's architecture ensures that all transactions are serializable, providing strong consistency guarantees and eliminating race conditions.

ChartDB is an open source, web‑based database diagramming tool that instantly visualizes your schema, whether from popular DBMSs like PostgreSQL, MySQL, SQL Server, SQLite, ClickHouse, and Oracle, or via a single query, without needing database access or complex setup. You can edit diagrams interactively using drag‑and‑drop, add annotations, or customize relationships, then export clean, dialect‑specific SQL DDL scripts or share diagrams as images. ChartDB Cloud accelerates workflows further with features like real‑time collaboration, live cursors, team avatars, and synced edits, plus auto‑saving of your work. Its AI assistant enhances productivity by detecting missing relationships, recommending foreign keys, and suggesting schema optimizations with a single click. Diagrams stay current through automated schema synchronization via a secure syncer CLI, ideal for CI/CD pipelines, preserving layout and styling even as your data model evolves.

Teable is an AI-driven, no-code database & application builder that lets users transform documents, spreadsheets, and raw data into live databases, apps, automations, and workflows with minimal effort. It supports real-time collaboration, smart permissions (team sync), and authority matrices for data governance and security. You can build multiple views of your data (Kanban, calendar, gallery, form, grid), integrate systems and tools, and manage millions of rows with no performance loss. It includes dashboard analytics, automated workflows (triggers, condition-based actions), undo/redo/history, validation, linking, sorting, filtering, formulas, etc., so users can both analyze data and act on it. It supports multiple AI models (OpenAI, Claude, DeepSeek, plus self-hosted LLMs) to enable intelligent processing (e.g., generative tasks over data), and allows integrations with SQL databases or physical database connections.

Starburst helps you make better decisions with fast access to all your data; Without the complexity of data movement and copies. Your company has more data than ever before, but your data teams are stuck waiting to analyze it. Starburst unlocks access to data where it lives, no data movement required, giving your teams fast & accurate access to more data for analysis. Starburst Enterprise is a fully supported, production-tested and enterprise-grade distribution of open source Trino (formerly Presto® SQL). It improves performance and security while making it easy to deploy, connect, and manage your Trino environment. Through connecting to any source of data – whether it’s located on-premise, in the cloud, or across a hybrid cloud environment – Starburst lets your team use the analytics tools they already know & love while accessing data that lives anywhere.

Connect your data sources, configure some data views (i.e. SQL scripts), configure a GPT Wizard, create a custom GPT, and share it with your users, employees, or clients. Expose a specific set of data (using SQL) to train GPT, and then chat with it in natural language. Data insights just got a whole lot easier, you can follow simple steps and let DataLang do the heavy lifting. Configure your connection string and give it a name. Use SQL to train GPT with your own data rows, select the data sources to train GPT with, and leverage GPT to talk to your data in real time. Create custom GPT Assistants to chat with your data. Configure a GPT to share it with your users, or customers. Your connection string credentials are securely encrypted in our system and only decrypted when necessary for data operations. We are committed to the security and confidentiality of your information. You can ask DataLang almost any question you would ask a data analyst.

Corporations need a way to ensure uninterrupted operation of their systems, high speed of data processing, and reliability of storage. The in-memory technologies have proven themselves well in solving these problems. For more than 10 years, Tarantool has been helping companies all over the world build smart caches, data marts, and golden client profiles while saving server capacity. Reduce the cost of storing credentials compared to siloed solutions and improve the service and security of client applications. Reduce data management costs of maintaining a large number of disparate systems that store customer identities. Increase sales by improving the speed and quality of customer recommendations for goods or services through the analysis of user behavior and user data. Improve mobile and web channel service by accelerating frontends to reduce user outflow. IT systems of large organizations operate in a closed loop of a local network, where data circulates unprotected.

Oracle Real Application Clusters (RAC) is a unique, scale-everything, highly available database architecture that transparently scales both reads and writes for all workloads, including OLTP, analytics, AI vectors, SaaS, JSON, batch, text, graph, IoT, and in-memory. It effortlessly scales complex applications such as SAP, Oracle Fusion Applications, and Salesforce workloads. Oracle RAC delivers the lowest latency and highest throughput for all data needs through its unique fused cache across servers, ensuring ultrafast local data access. Parallelized workloads across all CPUs guarantee maximum throughput, and the integration of Oracle’s storage design enables seamless online storage expansion. Unlike other databases that depend on public cloud infrastructures, sharding, or read replicas for scalability, Oracle RAC guarantees the lowest latency and highest throughput out of the box.

Quasar's brain is QuasarDB, a high-performance, distributed, column-oriented timeseries database management system designed from the ground up to deliver real-time on petascale use cases. Up to 20X less disk usage. Quasardb ingestion and compression capabilities are unmatched. Up to 10,000X faster feature extraction. QuasarDB can extract features in real-time from the raw data, thanks to the combination of a built-in map/reduce query engine, an aggregation engine that leverages SIMD from modern CPUs, and stochastic indexes that use virtually no disk space. The most cost-effective timeseries solution, thanks to its ultra-efficient resource usage, the capability to leverage object storage (S3), unique compression technology, and fair pricing model. Quasar runs everywhere, from 32-bit ARM devices to high-end Intel servers, from Edge Computing to the cloud or on-premises.

Processing and storing tabular datasets, e.g. from CSV or Parquet files. Large result set transfer to client. Large client/server installations for centralized enterprise data warehousing. Writing to a single database from multiple concurrent processes. DuckDB is a relational database management system (RDBMS). That means it is a system for managing data stored in relations. A relation is essentially a mathematical term for a table. Each table is a named collection of rows. Each row of a given table has the same set of named columns, and each column is of a specific data type. Tables themselves are stored inside schemas, and a collection of schemas constitutes the entire database that you can access.

Now that your data is in motion, it’s time to make sense of it. Stream processing enables you to derive instant insights from your data streams, but setting up the infrastructure to support it can be complex. That’s why Confluent developed ksqlDB, the database purpose-built for stream processing applications. Make your data immediately actionable by continuously processing streams of data generated throughout your business. ksqlDB’s intuitive syntax lets you quickly access and augment data in Kafka, enabling development teams to seamlessly create real-time innovative customer experiences and fulfill data-driven operational needs. ksqlDB offers a single solution for collecting streams of data, enriching them, and serving queries on new derived streams and tables. That means less infrastructure to deploy, maintain, scale, and secure. With less moving parts in your data architecture, you can focus on what really matters -- innovation.

Serverless computing is ideal for systems with unpredictable loads. Storage scaling, query execution, and backup layers are fully automated. The compatibility of the service API in serverless mode allows you to use the AWS SDKs for Java, JavaScript, Node.js, .NET, PHP, Python, and Ruby. YDB is hosted in three availability zones, ensuring availability even if a node or availability zone goes offline. If equipment or a data center fails, the system automatically recovers and continues working. YDB is tailored to meet high-performance requirements and can process hundreds of thousands of transactions per second with low latency. The system was designed to handle hundreds of petabytes of data.

SDF is a developer platform for data that enhances SQL comprehension across organizations, enabling data teams to unlock the full potential of their data. It provides a transformation layer to streamline query writing and management, an analytical database engine for local execution, and an accelerator for improved transformation processes. SDF also offers proactive quality and governance features, including reports, contracts, and impact analysis, to ensure data integrity and compliance. By representing business logic as code, SDF facilitates the classification and management of data types, enhancing the clarity and maintainability of data models. It integrates seamlessly with existing data workflows, supporting various SQL dialects and cloud environments, and is designed to scale with the growing needs of data teams. SDF's open-core architecture, built on Apache DataFusion, allows for customization and extension, fostering a collaborative ecosystem for data development.

SchemaFlow is a powerful tool designed to enhance AI-powered development by providing real-time access to your PostgreSQL database schema through the Model Context Protocol (MCP). It allows developers to connect their databases, visualize schema structures with interactive diagrams, and export schemas in various formats such as JSON, Markdown, SQL, and Mermaid. With native MCP support via Server-Sent Events (SSE), SchemaFlow enables seamless integration with AI-Integrated Development Environments (AI-IDEs) like Cursor, Windsurf, and VS Code, ensuring that AI assistants have up-to-date schema information for accurate code generation. It offers secure token-based authentication for MCP connections, automatic schema synchronization to keep AI assistants informed of any changes, and a schema browser for easy navigation of tables and relationships.