Open Source Scala Software - Page 4
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Scala Software
Browse free open source Scala Software and projects below. Use the toggles on the left to filter open source Scala Software by OS, license, language, programming language, and project status.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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The aim of this project is to create programs which turn off the computer after a certain time is exceeded within one day. The final goal is to avoid that small children spend too much time in front of the computer.
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Chimney
Scala library for boilerplate-free, type-safe data transformations
Chimney is a Scala library that facilitates boilerplate-free, type-safe data transformations between different data types. It enables developers to define mappings between source and target types, ensuring that transformations are checked at compile time, thereby reducing runtime errors and enhancing code reliability. -
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Chipyard
An Agile RISC-V SoC Design Framework with in-order cores
Chipyard is a framework and generator for constructing custom RISC‑V SoC hardware. Built at UC Berkeley, it leverages Chisel/FIRRTL to generate full-stack systems—from CPU cores to peripherals—and includes simulators, FPGA deployment tools, and integration with Rocket Chip and other RISC‑V ecosystems. -
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CoSEEEK
Context-aware Software Engineering Environment Event-driven Framework
CoSEEEK is a framework that provides automated guidance for software developers during the development lifecycle. Its unique holistic approach amalgamates quality management, process management, context management, and knowledge management and can thus provide support attuned to the current project situation and the engineer’s context. One key goal of CoSEEEK was to develop techniques so that applicable guidance can be provided to an engineer (a CoSEEEK user) in an automated way based on their current context (context-awareness); taking into account what activities they have done and are likely to do based on their process workflow (process-orientation) using a workflow engine; current quality risks and issues for the project (quality management) are taken into account; and knowledge such as guidance is provisioned (e.g., providing checklist items). -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Cosmos DB Spark
Apache Spark Connector for Azure Cosmos DB
Azure Cosmos DB Spark is the official connector for Azure CosmosDB and Apache Spark. The connector allows you to easily read to and write from Azure Cosmos DB via Apache Spark DataFrames in Python and Scala. It also allows you to easily create a lambda architecture for batch-processing, stream-processing, and a serving layer while being globally replicated and minimizing the latency involved in working with big data. -
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DBpedia has moved to GitHub: https://github.com/dbpedia/extraction-framework/wiki The mailing lists are still hosted by SourceForge. DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia and to link other datasets on the Web to Wikipedia data.
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DBpedia Spotlight
DBpedia Spotlight is a tool for automatically annotating
It is a tool for automatically annotating mentions of DBpedia resources in text, providing a solution for linking unstructured information sources to the Linked Open Data cloud through DBpedia. With a four step approach, DBpedia Spotlight performs named entity extraction, including entity detection and name resolution. It can also be used for named entity recognition, amongst other information extraction tasks. Empower the user experience reusing, interlinking and making semantic queries among high-quality open datasets, extracting meaning from unstructured data. -
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DBpedia Spotlight is a tool for annotating mentions of DBpedia resources in natural language text. The source code is now hosted on GitHub: https://github.com/dbpedia-spotlight
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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DISTOD
Distributed discovery of bidirectional order dependencies
The DISTOD data profiling algorithm is a distributed algorithm to discover bidirectional order dependencies (in set-based form) from relational data. DISTOD is based on the single-threaded FASTOD-BID algorithm [1], but DISTOD scales elastically to many machines outperforming FASTOD-BID by up to orders of magnitude. Bidirectional order dependencies (bODs) capture order relationships between lists of attributes in a relational table. They can express that, for example, sorting books by publication date in ascending order also sorts them by age in descending order. The knowledge about order relationships is useful for many data management tasks, such as query optimization, data cleaning, or consistency checking. Because the bODs of a specific dataset are usually not explicitly given, they need to be discovered. The discovery of all minimal bODs (in set-based canonical form) is a task with exponential complexity in the number of attributes. -
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DataNucleus
Java persistence using JDO, JPA or REST
DataNucleus provides Java data persistence to a range of datastores using JDO/JPA/REST APIs. *** Note that code development is no longer on SourceForge (code on SourceForge is for versions up to 3.3.5 only) *** -
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Debian Unstable/testing alpha.
Pre-Alpha Console distro for high performance Linux computing.
Basic Debian INTEL/AMD HPC console/desktop multi purpose. Linux for software devs/QA and thin laboratory clusters. MIC hardware, NUMA and live network memory and full IPv6 are supported. Final alpha release features: ======== * Usb image * General debian repos * VM optimizations * Hugepages by default * CGROUPs optimized by default * ALSA * Latest Debugging / Baremetal kernels * Full Haswell and MIC support in test * Full optimized python support * Full optimized perl support * Java compliance * IPv6 full stack with IPsets * Multipath storage * Overclocking tools * Student/Developer scripts. (git pull, builders) * i915 latest support drivers and gallium. * Kernel patching, testing featurettes * Wi-Fi and bluetooth ipv4 support * Latest GCC versions * Qt, Java, Maven, Desktop improvements Notes ==== Basic Console Pre-alpha build Test and rate for improvemen -
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Deem
Analyze time-course data with significance tests, clustering, modeling
Use statistical methods to analyze time-course data (gene expression microarray and RNA-seq data in particular, but not limited to). Apply significance tests to filter out only significant genes or time series. Cluster time series into similar groups. Generate network models, including linear or non-linear models. Variable selection and optimization routines included. Written in Scala and R. The application is a cross-platform desktop app with a simple GUI and is fully functional currently. The app was and is developed at the University of Rochester (http://cbim.urmc.rochester.edu) under the GPL 3.0 license. This is a fork of that project. -
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Deequ
Deequ is a library built on top of Apache Spark
Deequ is a library built atop Apache Spark that enables defining “unit tests for data” — that is, formal constraints or checks on datasets to ensure data quality along dimensions such as completeness, uniqueness, value ranges, correlations, etc. It can scale to large datasets (billions of rows) by translating those data checks into Spark jobs. Deequ supports advanced features like a metrics repository for storing computed statistics over time, anomaly detection of data quality metrics, and the suggestion of likely constraints automatically for new datasets. It also includes a little domain-specific language called DQDL (Data Quality Definition Language) which allows declarative specification of quality rules. Users typically run Deequ before feeding data downstream (to ML pipelines, analytics, or production systems), enabling early detection and isolation of data errors. There is also a Python wrapper, PyDeequ, for users who prefer working from Python environments. -
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Delayed Response Network
Neural network based on signal delays.
An artificial neural network, currently specialized to save a specific bit pattern, mainly by changing the signal propagation delays in links. More features, variables and algorithms will be added in time. -
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Diffy
Find potential bugs in your services with Diffy
Diffy is a traffic-shadowing and response-diffing tool that helps you validate a new version of a service against a trusted baseline before a full cutover. It acts as a proxy that fans out real production requests to three backends: the current “primary,” a “candidate” (new build), and a “shadow” baseline, then compares responses to detect behavioral differences. By using live traffic rather than synthetic tests, Diffy surfaces edge cases and data-dependent discrepancies that unit tests often miss. It provides mechanisms to normalize noise (timestamps, IDs, ordering) so diffs reflect meaningful regressions rather than incidental variance. Results are aggregated and visualized so teams can triage by endpoint, response field, or error type, turning release validation into a data-driven process. Built on Twitter’s Finagle stack, Diffy demonstrates a practical pattern for continuous verification and safer deployments in microservice environments. -
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Dotty
The scala 3 compiler, also known as Dotty
The exciting new version of Scala 3 brings many improvements and new features. Scala 3 is a complete overhaul of the Scala language. At its core, many aspects of the type-system have been changed to be more principled. While this also brings exciting new features along (like union types), first and foremost, it means that the type-system gets (even) less in your way and for instance type-inference and overload resolution are much improved. One underlying core concept of Scala was (and still is to some degree) to provide users with a small set of powerful features that can be combined to great (and sometimes even unforeseen) expressivity. For example, the feature of implicits has been used to model contextual abstraction, to express type-level computation, model type-classes, perform implicit coercions, encode extension methods, and many more. Learning from these use cases, Scala 3 takes a slightly different approach and focuses on intent rather than mechanism. -
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Dream Machine Preservation Project is a project with the goal to create a software emulator for the Amiga computer on the Java Virtual Machine.
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Entendre is a framework that enables syntactic and semantic analysis of inputs in relation to a reference ontology.
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Erlang/Scala IDE based on NetBeans. With syntax checking, syntax highlighting, auto-completion, pretty formatter, occurrences mark, brace matching, indentation, code folding, function navigator, go to declaration, project management, shell console.
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Escape-K is an open-source IT management platform. It is designed to be used by IT services and provides a complete set of features for both technical and user services management. Using a single user interface, Escape-K lets you manage your whole IT inf
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This project is a modification of Etherpad to enable collaborative and simultaneus editing of a DokuWiki page. It includes a script which automates the installation of both products. Dokuwiki: © Andreas Gohr Etherpad: © Google Inc
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FS2
Compositional, streaming I/O library for Scala
FS2 (“Functional Streams for Scala”) is a purely functional, effectful abstraction for stream processing on the JVM. Built on Cats Effect, it enables compositional resource-safe streaming workflows with robust error handling, back-pressure, pull/push semantics, and support for concurrent and interruptible pipelines. -
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Feathr
A scalable, unified data and AI engineering platform for enterprise
Feathr is a data and AI engineering platform that is widely used in production at LinkedIn for many years and was open sourced in 2022. It is currently a project under LF AI & Data Foundation. Define data and feature transformations based on raw data sources (batch and streaming) using Pythonic APIs. Register transformations by names and get transformed data(features) for various use cases including AI modeling, compliance, go-to-market and more. Share transformations and data(features) across team and company. Feathr is particularly useful in AI modeling where it automatically computes your feature transformations and joins them to your training data, using point-in-time-correct semantics to avoid data leakage, and supports materializing and deploying your features for use online in production. -
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FiloDB
Distributed Prometheus time series database
FiloDB is an open-source distributed, real-time, in-memory, massively scalable, multi-schema time series / event / operational database with Prometheus query support and some Spark support as well. The normal configuration for real-time ingestion is deployment as stand-alone processes in a cluster, ingesting directly from Apache Kafka. The processes form a cluster using peer-to-peer Akka Cluster technology. Designed to ingest many millions of entities, sharded across multiple processes, with distributed querying built in. Support for indexing and fast querying over flexible tags for each time series/partition, just like Prometheus. Holds a huge amount of data in-memory thanks to columnar compression techniques. Designed for highly concurrent, low-latency workloads such as dashboards and alerting. Data immediately available for querying once ingested.
