Best Data Management Software for Rust
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Compare the Top Data Management Software that integrates with Rust as of June 2025
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This a list of Data Management software that integrates with Rust. Use the filters on the left to add additional filters for products that have integrations with Rust. View the products that work with Rust in the table below.
What is Data Management Software for Rust?
Data management software systems are software platforms that help organize, store and analyze information. They provide a secure platform for data sharing and analysis with features such as reporting, automation, visualizations, and collaboration. Data management software can be customized to fit the needs of any organization by providing numerous user options to easily access or modify data. These systems enable organizations to keep track of their data more efficiently while reducing the risk of data loss or breaches for improved business security. Compare and read user reviews of the best Data Management software for Rust currently available using the table below. This list is updated regularly.
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1
Quary
Quary
Connect to your data warehouse, and SSO authenticates your team in seconds. Organize your business intelligence as SQL. Make changes with confidence, automated testing validates every update. Deploy models confidently and travel back in time when things go wrong. Quary connects to your sensitive data store. In order to protect it, Quary is built from the ground up with security in mind. By default, data does not leave your estate, data exchanges happen between your Quary client and your data store. Transform data together, model, test, and deploy as a team. SSO is not an extra, we include it in our base plan and even help set it up. No one should share credentials. Quary builds upon your data store's access management systems and helps you manage it. We are in the process of implementing SOC2 and have security credentials (CISSP) on the team to ensure your data stays secure.Starting Price: Free -
2
Meteomatics
Meteomatics
Meteomatics specializes in high-resolution commercial weather forecasting, power output forecasting for wind, solar and hydro, weather data gathering from the lower atmosphere using Meteodrones, and weather data delivery via the Weather API. - Unlimited accesses/day - Weather data querying via URL - Unified weather data access for historical and current weather, forecasts, climate models, and data from over 25 weather models - WMS and WFS interface - Delivery of forecasts with an average response time of 20 to 30 ms - 90 m downscaling worldwide - 1800+ parameters - Historical weather data from 1979 Climate data including climate scenarios up to the year 2100 - Secured use with HTTP and HTTPS - Integration with many formats, connectors, and programming languages available - Proprietary European Weather Model with 1 km resolution – EURO1k (Business plan)Starting Price: $0/month/user -
3
SerpApi
SerpApi
Leverage our infrastructure (IPs across the globe, full browser cluster, and CAPTCHA solving technology), and exploit our structured SERP data in the way you want. In addition, each API request runs in a full browser, and we'll even solve all CAPTCHAs. Mimicking completely what a human will do. This guarantees that you get what users truly see. SerpApi uses Google’s geolocated, encrypted params and routes your request through the proxy server nearest to your desired location to ensure accuracy. Lots of structured data is available for each result, including links, addresses, tweets, prices, thumbnails, ratings, reviews, rich snippets, and more.Starting Price: $50 per month -
4
LanceDB
LanceDB
LanceDB is a developer-friendly, open source database for AI. From hyperscalable vector search and advanced retrieval for RAG to streaming training data and interactive exploration of large-scale AI datasets, LanceDB is the best foundation for your AI application. Installs in seconds and fits seamlessly into your existing data and AI toolchain. An embedded database (think SQLite or DuckDB) with native object storage integration, LanceDB can be deployed anywhere and easily scales to zero when not in use. From rapid prototyping to hyper-scale production, LanceDB delivers blazing-fast performance for search, analytics, and training for multimodal AI data. Leading AI companies have indexed billions of vectors and petabytes of text, images, and videos, at a fraction of the cost of other vector databases. More than just embedding. Filter, select, and stream training data directly from object storage to keep GPU utilization high.Starting Price: $16.03 per month -
5
Dragonfly
DragonflyDB
Dragonfly is a drop-in Redis replacement that cuts costs and boosts performance. Designed to fully utilize the power of modern cloud hardware and deliver on the data demands of modern applications, Dragonfly frees developers from the limits of traditional in-memory data stores. The power of modern cloud hardware can never be realized with legacy software. Dragonfly is optimized for modern cloud computing, delivering 25x more throughput and 12x lower snapshotting latency when compared to legacy in-memory data stores like Redis, making it easy to deliver the real-time experience your customers expect. Scaling Redis workloads is expensive due to their inefficient, single-threaded model. Dragonfly is far more compute and memory efficient, resulting in up to 80% lower infrastructure costs. Dragonfly scales vertically first, only requiring clustering at an extremely high scale. This results in a far simpler operational model and a more reliable system.Starting Price: Free -
6
txtai
NeuML
txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.Starting Price: Free -
7
Apache DataFusion
Apache Software Foundation
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.Starting Price: Free -
8
Convex
Convex
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.Starting Price: $25 per month -
9
Chalk
Chalk
Powerful data engineering workflows, without the infrastructure headaches. Complex streaming, scheduling, and data backfill pipelines, are all defined in simple, composable Python. Make ETL a thing of the past, fetch all of your data in real-time, no matter how complex. Incorporate deep learning and LLMs into decisions alongside structured business data. Make better predictions with fresher data, don’t pay vendors to pre-fetch data you don’t use, and query data just in time for online predictions. Experiment in Jupyter, then deploy to production. Prevent train-serve skew and create new data workflows in milliseconds. Instantly monitor all of your data workflows in real-time; track usage, and data quality effortlessly. Know everything you computed and data replay anything. Integrate with the tools you already use and deploy to your own infrastructure. Decide and enforce withdrawal limits with custom hold times.Starting Price: Free -
10
Pathway
Pathway
Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a scalable Rust engine based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with Docker and Kubernetes. -
11
Polars
Polars
Knowing of data wrangling habits, Polars exposes a complete Python API, including the full set of features to manipulate DataFrames using an expression language that will empower you to create readable and performant code. Polars is written in Rust, uncompromising in its choices to provide a feature-complete DataFrame API to the Rust ecosystem. Use it as a DataFrame library or as a query engine backend for your data models. -
12
Ditto
Ditto
Ditto is the only mobile database with built-in edge device connectivity and resiliency, enabling apps to synchronize without relying on a central server or constant cloud connectivity. Through the use of CRDTs and P2P mesh replication, Ditto allows you to build collaborative, resilient applications where data is always available and up-to-date for every user. This allows you to keep mission-critical systems online when it matters most. Ditto uses an edge-native architecture, meaning your app remains fully functional even offline. Devices running Ditto apps can discover and communicate with each other directly, forming an ad-hoc mesh network rather than routing everything through a cloud server. The platform automatically handles the complexity of discovery and connectivity using whatever channels are available – for example, Bluetooth, peer-to-peer Wi-Fi, or local LAN – to find nearby devices and sync data with them. -
13
Arroyo
Arroyo
Scale from zero to millions of events per second. Arroyo ships as a single, compact binary. Run locally on MacOS or Linux for development, and deploy to production with Docker or Kubernetes. Arroyo is a new kind of stream processing engine, built from the ground up to make real-time easier than batch. Arroyo was designed from the start so that anyone with SQL experience can build reliable, efficient, and correct streaming pipelines. Data scientists and engineers can build end-to-end real-time applications, models, and dashboards, without a separate team of streaming experts. Transform, filter, aggregate, and join data streams by writing SQL, with sub-second results. Your streaming pipelines shouldn't page someone just because Kubernetes decided to reschedule your pods. Arroyo is built to run in modern, elastic cloud environments, from simple container runtimes like Fargate to large, distributed deployments on the Kubernetes logo Kubernetes. -
14
SDF
SDF
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. -
15
Daft
Daft
Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. When your local machine is no longer sufficient, it scales seamlessly to run out-of-core on a distributed cluster. Daft can handle User-Defined Functions (UDFs) in columns, allowing you to apply complex expressions and operations to Python objects with the full flexibility required for ML/AI. Daft runs locally with a lightweight multithreaded backend. When your local machine is no longer sufficient, it scales seamlessly to run out-of-core on a distributed cluster.
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