Open Source Scala Business Software for Windows
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Scala Business Software for Windows
View 498 business solutionsBrowse free open source Scala Business Software for Windows and projects below. Use the toggles on the left to filter open source Scala Business Software for Windows 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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Level Up Your Cyber Defense with External Threat ManagementMove beyond alerts. Gain full visibility, context, and control over your external attack surface to stay ahead of every threat.
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JobScheduler
JobScheduler workload automation to execute jobs and workflows
JS7 JobScheduler runs executables, scripts and database procedures and can be operated with e.g. MySQL, PostgreSQL, SQL Server, Oracle databases. JS7 JobScheduler provides workflow patterns such as sequential and parallel task execution, forking, synchronization, cross-platform scheduling, job load sharing, fail-over and a real REST API. JS7 includes the components: * JOC Cockpit (user interface for inventory management and control) * Controller (orchestration of Agents) * Agents: platform independent Agents for job execution Product Knowledge Base at https://kb.sos-berlin.com/display/JS7 Change Management System at https://change.sos-berlin.com Release Archive at: https://www.sos-berlin.com/jobscheduler-release-archive -
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Akka
Build concurrent, distributed, and resilient message-driven apps
Build powerful reactive, concurrent, and distributed applications more easily. Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven applications for Java and Scala. Actors and Streams let you build systems that scale up, using the resources of a server more efficiently, and out, using multiple servers. Building on the principles of The Reactive Manifesto Akka allows you to write systems that self-heal and stay responsive in the face of failures. Up to 50 million msg/sec on a single machine. Small memory footprint; ~2.5 million actors per GB of heap. Distributed systems without single points of failure. Load balancing and adaptive routing across nodes. Event Sourcing and CQRS with Cluster Sharding. Distributed Data for eventual consistency using CRDTs. Asynchronous non-blocking stream processing with backpressure. -
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Scio
A Scala API for Apache Beam and Google Cloud Dataflow
Scio is a Scala API developed by Spotify that builds on Apache Beam to enable expressive batch and streaming data pipelines, optimized for running on Google Cloud Dataflow. Inspired by Spark and Scalding, it provides scalable, type‑safe, and production-grade data processing, with built-in support for BigQuery, Pub/Sub, Cassandra, Elasticsearch, Redis, TensorFlow IO, and more. -
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Chronos
Fault tolerant job scheduler for Mesos to handle dependencies
Chronos is a replacement for cron. It is a distributed and fault-tolerant scheduler that runs on top of Apache Mesos that can be used for job orchestration. It supports custom Mesos executors as well as the default command executor. Thus by default, Chronos executes sh (on most systems bash) scripts. Chronos can be used to interact with systems such as Hadoop (incl. EMR), even if the Mesos slaves on which execution happens do not have Hadoop installed. Chronos is also natively able to schedule jobs that run inside Docker containers. Chronos has a number of advantages over regular cron. It allows you to schedule your jobs using ISO8601 repeating interval notation, which enables more flexibility in job scheduling. Chronos also supports the definition of jobs triggered by the completion of other jobs. It supports arbitrarily long dependency chains. The easiest way to use Chronos is to use DC/OS and install chronos via the universe. -
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MyCollab - Project Management Software
task management, bug tracking, kanban, project management
WE DISCONTINUE UPDATE THE PROJECT ON SOURCEFORGE SITES. FOR THE NEW RELEASE, VISIT https://community.mycollab.com. MyCollab is a free, open source collaboration platform management. It provides the comprehensive set features of Project Management, CRM, and Document Management. MyCollab is used very well in both traditional project management or Agile methodologies. It is the web-based application runs on Java platform and MySQL database. Its installation process is simple, and you do not need to edit configuration files manually as many other Java programs. MyCollab is the final choice of many organizations to find the best software for their usages. It covers all areas of project management; it's worth to try. Installation ======== * Download MyCollab binary - https://www.mycollab.com/self-hosted/ * Follow installation guideline at https://community.mycollab.com/docs/hosting-mycollab-on-your-own-server/installing-mycollab/ -
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apache spark data pipeline osDQ
osDQ dedicated to create apache spark based data pipeline using JSON
This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to run Unzip the zip file Windows : java -cp .\lib\*;osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c .\example\samplerun.json Mac UNIX java -cp ./lib/*:./osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c ./example/samplerun.json For those on windows, you need to have hadoop distribtion unzipped on local drive and HADOOP_HOME set. Also copy winutils.exe from here into HADOOP_HOME\bin -
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Cassandra Spark Connector
Apache Spark to Apache Cassandra connector
The Apache Cassandra Spark Connector allows Spark jobs (RDDs or DataFrames/Datasets) to read from and write to Cassandra tables. Compatible with Apache Cassandra (v2.1+), Spark 1.0–3.5, and Scala 2.11–2.13, it supports mapping Cassandra rows to Scala case classes, saving results back to Cassandra, and executing arbitrary CQL within Spark applications. -
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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. -
The All-in-One Commerce Platform for Businesses - ShopifyShopify is a leading all-in-one commerce platform that enables businesses to start, build, and grow their online and physical stores. It offers tools to create customized websites, manage inventory, process payments, and sell across multiple channels including online, in-person, wholesale, and global markets. The platform includes integrated marketing tools, analytics, and customer engagement features to help merchants reach and retain customers. Shopify supports thousands of third-party apps and offers developer-friendly APIs for custom solutions. With world-class checkout technology, Shopify powers over 150 million high-intent shoppers worldwide. Its reliable, scalable infrastructure ensures fast performance and seamless operations at any business size.
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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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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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Inhaler
speed reading tool
Inhaler is a speed reading tool programmed in scala using swing. It features variable reading speed and font size. It is licensed under GPL. -
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OBP-API (Open Bank Project API)
An open source RESTful API platform for banks
OBP-API is an open-source banking platform that exposes a comprehensive REST/JSON API for accounts, transactions, payments, KYC, and related banking resources. It’s designed to help banks and fintechs implement secure, permissioned access to financial data while aligning with open-banking and PSD2-style workflows. The platform includes fine-grained roles, consents, and entitlements so institutions can control who can see or act on specific resources, down to individual endpoints and fields. A pluggable connector layer lets the API sit in front of different core banking systems or sandboxes, making it possible to prototype quickly and later connect to production cores. OBP-API emphasizes strong auditing and metadata, supporting features like tags, comments, and views on top of transactional data to enable collaboration and compliance. -
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SZT-bigdata
SZT‑bigdata is an open source project
SZT‑bigdata is an open-source project analyzing real Shenzhen metro (subway) card usage data using big‑data frameworks like Spark, Hadoop, Hive, Kafka, Flink, ClickHouse, HBase, and Elasticsearch. Aimed at exploring transit passenger flow patterns and system optimization using a variety of Scala-based technologies. -
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SnappyData
Memory optimized analytics database, based on Apache Spark
SnappyData (aka TIBCO ComputeDB) is a distributed, in-memory optimized analytics database. SnappyData delivers high throughput, low latency, and high concurrency for a unified analytics workload. By fusing an in-memory hybrid database inside Apache Spark, it provides analytic query processing, mutability/transactions, access to virtually all big data sources and stream processing all in one unified cluster. One common use case for SnappyData is to provide analytics at interactive speeds over large volumes of data with minimal or no pre-processing of the dataset. For instance, there is no need to often pre-aggregate/reduce or generate cubes over your large data sets for ad-hoc visual analytics. This is made possible by smartly managing data in memory, dynamically generating code using vectorization optimizations, and maximizing the potential of modern multi-core CPUs. SnappyData enables complex processing on large data sets in sub-second timeframes. -
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Synapse Machine Learning
Simple and distributed Machine Learning
SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit, and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of data sources. SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. For production-grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster. -
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Uniscala is a set of Scala APIs for business objects that supports a framework, called Granite, for business-oriented web applications based on Scala, Wicket and DB4O. Uniscala is strongly influenced by Zope.
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Waterloo
Java-based scientific graphics
Java-based scientific graphics with support for Java, Groovy, MATLAB, Python, the R statistical environment, Scala and SciLab. -
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Java OpenCL Process Virtual Machine. Spring IoC based framework for complex data analysis with OpenCL computing.
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