Open Source Rust Data Management Systems
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Rust Data Management Systems
View 4176 business solutionsBrowse free open source Rust Data Management Systems and projects below. Use the toggles on the left to filter open source Rust Data Management Systems by OS, license, language, programming language, and project status.
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Find Hidden Risks in Windows Task SchedulerWindows Task Scheduler might be hiding critical failures. Download the free JAMS diagnostic tool to uncover problems before they impact production—get a color-coded risk report with clear remediation steps in minutes.
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Zenith
Sort of like top or htop but with zoom-able charts, CPU, GPU
In terminal graphical metrics for your *nix system written in Rust. The make file provides for building fully static versions on Linux against the musl C library. It requires musl-gcc to be installed on the system. Install "musl-tools" package on debian/ubuntu derivatives, "musl-gcc" on fedora and equivalent on other distributions from their standard repos. If one needs to build with NVIDIA support in a virtual environment, then it requires some more setup since typically the VM software is unable to directly expose NVIDIA GPU. Unlike the runtime zenith script, the Makefile has been setup to detect only the presence of required NVIDIA libraries, so it is possible to build with NVIDIA support even when without NVIDIA GPU. -
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Delta
A viewer for git and diff output
Code evolves, and we all spend time studying diffs. Delta aims to make this both efficient and enjoyable: it allows you to make extensive changes to the layout and styling of diffs, as well as allowing you to stay arbitrarily close to the default git/diff output. Language syntax highlighting with color themes. Within-line highlights based on a Levenshtein edit inference algorithm. Git style strings (foreground color, background color, font attributes) are supported for >20 stylable elements. Delta provides Stylable box/line decorations to draw attention to commit, file and hunk header sections. Support for Git's color-moved feature. Code can be copied directly from the diff. n and N keybindings to move between files in large diffs, and between diffs in log -p views. Commit hashes can be formatted as terminal hyperlinks to the GitHub/GitLab/Bitbucket page. Delta acts as a pager for git's output, and delta in turn passes its own output on to a "real" pager. -
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Amadeus
Harmonious distributed data analysis in Rust
Amadeus is a high-performance, distributed data processing framework written in Rust, designed to offer an ergonomic and safe alternative to tools like Apache Spark. It provides both streaming and batch capabilities, allowing users to work with real-time and historical data at scale. Thanks to Rust’s memory safety and zero-cost abstractions, Amadeus delivers performance gains while reducing the complexity and bugs common in large-scale data pipelines. It emphasizes developer productivity through a fluent, expressive API and makes it easier to build composable and reliable data transformation pipelines without sacrificing speed or safety. -
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Cube
Universal semantic layer platform for AI, BI, spreadsheets
Cube is the semantic layer for building data applications. It helps data engineers and application developers access data from modern data stores, organize it into consistent definitions, and deliver it to every application. Cube was designed to work with all SQL-enabled data sources, including cloud data warehouses like Snowflake or Google BigQuery, query engines like Presto or Amazon Athena, and application databases like Postgres. Cube has a built-in relational caching engine to provide sub-second latency and high concurrency for API requests. -
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Discord.SortedSet
Elixir SortedSet backed by a Rust-based NIF
SortedSet NIF is a performant and reliable sorted set data structure for Elixir, implemented in Rust using the Rustler crate to take advantage of native performance while maintaining seamless integration with the BEAM ecosystem. It provides ordering and uniqueness guarantees, with all terms stored according to Elixir’s built-in sorting rules. Internally, it uses a vector of vectors layout rather than a single vector to minimize costly reallocations, allowing efficient bucket pointer copying instead of expensive term copying during growth. This design achieves a balance between performance and simplicity, and developers can customize bucket sizes for specific workloads, with a default of 500 offering solid performance across common scenarios. SortedSet extends beyond traditional set semantics by providing indexing, random access, and slice operations thanks to its deterministic ordering. -
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gping
Ping, but with a graph
Graphical Ping displays a color-coded realtime graph of continuous pings to a specified host. No warranties are provided on this program, it is completely free to use. Graph the execution time for a list of commands rather than pinging hosts. Resolve ping targets to IPv4 address. Resolve ping targets to IPv6 address. Uses dot characters instead of braille. Determine the number of seconds to display in the graph. Watch interval seconds (provide partial seconds like '0.5'). -
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Arroyo
Distributed stream processing engine in Rust
Arroyo is a distributed stream processing engine written in Rust, designed to efficiently perform stateful computations on streams of data. Unlike traditional batch processing, streaming engines can operate on both bounded and unbounded sources, emitting results as soon as they are available. -
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Broot
A new way to see and navigate directory trees
Get an overview of a directory, even a big one. That's what makes it usable where the old tree command would produce pages of output. Hit alt/enter and you're back to the terminal in the desired location. This way, you can navigate to a directory with the minimum amount of keystrokes, even if you don't exactly remember where it is. Broot is fast and doesn't block (any keystroke interrupts the current search to start the next one). Never lose track of file hierarchy while you search. Broot tries to select the most relevant file. You can still go from one match to another one using tab or arrow keys. You may also search with a regular expression. To do this, add a / before the pattern. You may also apply logical operators or combine patterns, for example searching test in all files except json ones could be !/json$/&c/test and searching carg both in file names and file contents would be carg|c/carg. -
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CocoIndex
ETL framework to index data for AI, such as RAG
CocoIndex is an open-source framework designed for building powerful, local-first semantic search systems. It lets users index and retrieve content based on meaning rather than keywords, making it ideal for modern AI-based search applications. CocoIndex leverages vector embeddings and integrates with various models and frameworks, including OpenAI and Hugging Face, to provide high-quality semantic understanding. It’s built for transparency, ease of use, and local control over your search data, distinguishing itself from closed, black-box systems. The tool is suitable for developers working on personal knowledge bases, AI search interfaces, or private LLM applications. -
Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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OctoBase
OctoBase is the open-source database behind AFFiNE
OctoBase is a local-first, peer-to-peer database designed to power collaborative apps with real-time syncing and offline-first capabilities. Developed by the team behind Anytype, OctoBase ensures that data is owned and controlled by the user, using CRDTs (Conflict-free Replicated Data Types) to resolve changes across peers without needing a central server. It supports encrypted, schema-flexible data storage and enables seamless syncing across devices while maintaining privacy. OctoBase is ideal for apps focused on privacy, collaboration, and decentralization, such as personal knowledge bases or peer-to-peer productivity tools. -
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SafeUtils
110+ developer tools as native MacOS, Linux & Windows desktop apps.
Tools: https://safeutils.com/barcode-generator https://safeutils.com/color-picker https://safeutils.com/qr-code-generator https://safeutils.com/qr-code-scanner https://safeutils.com/word-counter https://safeutils.com/base-64-decoder https://safeutils.com/diff-checker https://safeutils.com/hex-to-ascii https://safeutils.com/json-formatter https://safeutils.com/lorem-ipsum-generator https://safeutils.com/random-generator https://safeutils.com/time-converter https://safeutils.com/xml-formatter https://safeutils.com/ascii-to-binary https://safeutils.com/ascii-to-hex https://safeutils.com/base-64-encoder https://safeutils.com/binary-to-ascii https://safeutils.com/case-converter https://safeutils.com/csv-to-json https://safeutils.com/decimal-to-ascii https://safeutils.com/html-formatter https://safeutils.com/html-preview https://safeutils.com/html-to-markdown https://safeutils.com/id-generator https://safeutils.com/json-to-csv https://safeutils.com/json-to-xml -
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Scanopy
Clean network diagrams, One-time setup, zero upkeep
Scanopy is a powerful multi-modal data capture and analysis toolkit that enables users to collect, process, and visualize structured and unstructured information from a variety of sources in a flexible pipeline. It is built to handle complex scanning tasks — such as OCR, document analysis, audio transcription, network data capture, and image extraction — while providing unified APIs and workflows that make managing heterogeneous data sources seamless. Developers can compose custom pipelines that chain together transforms, filters, and exporters, enabling automation of tedious data preparation steps and accelerating insights with minimal code. The system places a premium on extensibility, allowing contributors to add new extractors or analysis modules tailored to specific industries or datasets. Its visualization tools help users explore captured information interactively, revealing patterns or anomalies that might otherwise require significant manual effort. -
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TensorBase
TensorBase is a new big data warehousing with modern efforts
TensorBase hopes the open source not become a copy game. TensorBase has a clear-cut opposition to fork communities, repeat wheels, or hack traffic for so-called reputations (like Github stars). After thoughts, we decided to temporarily leave the general data warehousing field. For people who want to learn how a database system can be built up, or how to apply modern Rust to the high-performance field, or embed a lightweight data analysis system into your own big one. You can still try, ask or contribute to TensorBase. The committers are still around the community. We will help you in all kinds of interesting things pursued in the project by us and maybe you. We still maintain the project to look forward to meeting more database geniuses in this world, although no new feature will be added in the near future. -
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Whirlwind
Ridiculously fast, fully asynchronous, sharded hashmap for Rust
Collection of thread-safe, asynchronous data structures. Whirlwind is a lightweight CSS framework focused on performance and simplicity, offering developers a minimal approach to styling web applications. -
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jlrs
Julia bindings for Rust
jlrs is a crate that provides access to most of the Julia C API, it can be used to embed Julia in Rust applications and to use functionality it provides when writing ccallable functions in Rust. Currently, this crate is only tested in combination with Julia 1.6 and 1.9, but also supports Julia 1.7, 1.8, and 1.10. Using the current stable version is highly recommended. The minimum supported Rust version is currently 1.65. Julia must be installed before jlrs can be used, jlrs is compatible with Julia 1.6 up to and including Julia 1.10. The JlrsCore package must also have been installed, if this is not the case it will automatically be added when jlrs is initialized by default. jlrs has not been tested with juliaup yet on Linux and macOS. -
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xsv
A fast CSV command line toolkit written in Rust
xsv is a command line program for indexing, slicing, analyzing, splitting and joining CSV files. Commands should be simple, fast and composable. Simple tasks should be easy. Performance trade offs should be exposed in the CLI interface. Composition should not come at the expense of performance. Let's say you're playing with some of the data from the Data Science Toolkit, which contains several CSV files. Maybe you're interested in the population counts of each city in the world. So grab the data and start examining it. The next thing you might want to do is get an overview of the kind of data that appears in each column. The stats command will do this for you. The xsv table command takes any CSV data and formats it into aligned columns using elastic tabstops. These commands are instantaneous because they run in time and memory proportional to the size of the slice (which means they will scale to arbitrarily large CSV data).
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