Open Source Windows Big Data Tools
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Big Data Tools for Windows
View 71 business solutionsBrowse free open source Big Data tools and projects for Windows below. Use the toggles on the left to filter open source Big Data tools by OS, license, language, programming language, and project status.
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pandas
Fast, flexible and powerful Python data analysis toolkit
pandas is a Python data analysis library that provides high-performance, user friendly data structures and data analysis tools for the Python programming language. It enables you to carry out entire data analysis workflows in Python without having to switch to a more domain specific language. With pandas, performance, productivity and collaboration in doing data analysis in Python can significantly increase. pandas is continuously being developed to be a fundamental high-level building block for doing practical, real world data analysis in Python, as well as powerful and flexible open source data analysis/ manipulation tool for any language. -
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MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
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XCharts
A charting and data visualization library for Unity
A charting and data visualization library for Unity. Unity data visualization chart plugin. A UGUIpowerful, easy-to-use, parameter-configurable data visualization chart plug-in. It supports ten built-in charts. A powerful, easy-to-use, configurable charting and data visualization library for Unity. Visual configuration of parameters, real-time preview of effects, and pure code drawing without additional resources. Support ten built-in charts such as line chart, column chart, pie chart, radar chart, scatter chart, heat map, ring chart, candlestick chart, polar coordinate, parallel coordinate and so on. Supports 3D column charts, funnel charts, pyramids, dashboards, water level charts, pictographic column charts, Gantt charts, rectangular tree charts and other extended charts. Line graphs such as line graphs, curve graphs, area graphs, and stepped line graphs are supported. -
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FinMind
Open Data, more than 50 financial data
In the era of big data, data is the foundation of everything. We collect more than 50 kinds of Taiwan stock related information and provide download, online analysis, and backtesting. Regardless of the program, you can download data through the api provided by FinMind, or you can download data directly from the website. After data is available, statistical analysis, regression analysis, time series analysis, machine learning, and deep learning can be performed. For individual stocks, provide visual analysis of technical, fundamental, and chip levels. According to different strategies, back-test analysis is performed to provide performance, profit and loss, and stock selection targets of different strategy investment portfolios. -
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Apache Doris
MPP-based interactive SQL data warehousing for reporting and analysis
Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Apache Doris can meet various data analysis demands, including history data reports, real-time data analysis, interactive data analysis, and exploratory data analysis. Make your data analysis easier! Support standard SQL language, compatible with MySQL protocol. The main advantages of Doris are the simplicity (of developing, deploying and using) and meeting many data serving requirements in a single system. Doris mainly integrates the technology of Google Mesa and Apache Impala, and it is based on a column-oriented storage engine and can communicate by MySQL client. -
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Apache HBase
Get random, realtime read/write access to your Big Data
Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables, billions of rows X millions of columns, atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable. A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS. Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options. Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX. Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables. -
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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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Vespa
The open big data serving engine
Make AI-driven decisions using your data, in real-time. At any scale, with unbeatable performance. Vespa is a full-featured text search engine and supports both regular text search and fast approximate vector search (ANN). This makes it easy to create high-performing search applications at any scale, whether you want to use traditional techniques or a modern vector-based approach. You can even combine both approaches efficiently in the same query, something no other engine can do. Recommendation, personalization and targeting involves evaluating recommender models over content items to select the best ones. Vespa lets you build applications which does this online, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. This makes it possible to make recommendations specifically for each user or situation, using completely up to date information. -
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QuickRedis
QuickRedis is a free forever redis gui tool
QuickRedis is a free forever Redis Desktop manager. It supports direct connection, sentinel, and cluster mode, supports multiple languages, supports hundreds of millions of keys, and has an amazing UI. Supports both Windows, Mac OS X and Linux platform. -
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Apache Hudi
Upserts, Deletes And Incremental Processing on Big Data
Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and Incrementals. Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage). Apache Hudi is a transactional data lake platform that brings database and data warehouse capabilities to the data lake. Hudi reimagines slow old-school batch data processing with a powerful new incremental processing framework for low latency minute-level analytics. Hudi provides efficient upserts, by mapping a given hoodie key (record key + partition path) consistently to a file id, via an indexing mechanism. This mapping between record key and file group/file id, never changes once the first version of a record has been written to a file. In short, the mapped file group contains all versions of a group of records. -
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Apache Polaris
Apache Polaris, the interoperable, open source catalog
Apache Polaris is an open-source metadata catalog and data management service designed to manage Apache Iceberg tables in modern data lakehouse environments. It provides a centralized catalog that allows multiple compute engines and analytics systems to interact with the same datasets through a standardized interface. By implementing the Iceberg REST catalog API, Polaris enables distributed data platforms to access shared table metadata without tightly coupling storage systems and query engines. This design allows organizations to run queries on the same Iceberg tables using tools such as Apache Spark, Flink, Trino, and other analytics engines while maintaining consistency across platforms. Polaris also focuses on data governance, security, and interoperability within large-scale cloud data architectures. Because Iceberg tables often exist across many services in a distributed ecosystem, the catalog helps coordinate metadata, schemas, and access policies in a unified system. -
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Apache RocketMQ
Distributed messaging and streaming platform with low latency
Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability. Messaging patterns including publish/subscribe, request/reply and streaming. Financial grade transactional message. Built-in fault tolerance and high availability configuration options base on DLedger. A variety of cross language clients, such as Java, C/C++, Python, Go. Pluggable transport protocols, such as TCP, SSL, AIO. Built-in message tracing capability, also support opentracing. Versatile big-data and streaming ecosytem integration. Message retroactivity by time or offset. Reliable FIFO and strict ordered messaging in the same queue. Efficient pull and push consumption model. Million-level message accumulation capacity in a single queue. Multiple messaging protocols like JMS and OpenMessaging. Flexible distributed scale-out deployment architecture. Lightning-fast batch message exchange system. -
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ElasticJob
Distributed scheduled job framework
ElasticJob is a distributed scheduling solution consisting of two separate projects, ElasticJob-Lite and ElasticJob-Cloud. ElasticJob-Lite is a lightweight, decentralized solution that provides distributed task sharding services. ElasticJob-Cloud uses Mesos to manage and isolate resources. It uses a unified job API for each project. Developers only need code one time and can deploy at will. Support job sharding and high availability in distributed system. Scale out for throughput and efficiency improvement. Job processing capacity is flexible and scalable with the allocation of resources. Execute job on suitable time and assigned resources. Aggregation same job to same job executor. Append resources to newly assigned jobs dynamically. Using ElasticJob can make developers no longer worry about the non-functional requirements such as jobs scale out, so that they can focus more on business coding. -
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Genie
Distributed Big Data Orchestration Service
Genie is a completely open source distributed job orchestration engine developed by Netflix. Genie provides REST-ful APIs to run a variety of big data jobs like Hadoop, Pig, Hive, Spark, Presto, Sqoop and more. It also provides APIs for managing the metadata of many distributed processing clusters and the commands and applications which run on them. -
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GridDB
GridDB is a next-generation open source database
A cyber-physical systems is a system that collects a variety of data in physical space (the real world), analyzes and converts it into knowledge in cyberspace, and feeds the knowledge back to the real world to revitalize industry and solve social problems. GridDB is an open database that enables real-time processing of vast amounts of time-series data in physical space, which is necessary to realize a cyber-physical system. Multi-model architecture capable of supporting various data stores with time-series data-oriented and pluggable data stores for efficient real-time processing and management of huge amounts of time-series data at high frequency. Various architectural innovations, such as in-memory orientation with "memory as the main unit and disk as the secondary unit" and event-driven design with minimal overhead, have been incorporated to achieve processing capabilities that can handle petabyte-scale applications. -
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geometry-api-java
The Esri Geometry API for Java enables developers to write apps
The Esri Geometry API for Java can be used to enable spatial data processing in 3rd-party data-processing solutions. Developers of custom MapReduce-based applications for Hadoop can use this API for spatial processing of data in the Hadoop system. The API is also used by the Hive UDF’s and could be used by developers building geometry functions for 3rd-party applications such as Cassandra, HBase, Storm and many other Java-based “big data” applications. -
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X10
Performance and Productivity at Scale
X10 is a class-based, strongly-typed, garbage-collected, object-oriented language. To support concurrency and distribution, X10 uses the Asynchronous Partitioned Global Address Space programming model (APGAS). This model introduces two key concepts -- places and asynchronous tasks -- and a few mechanisms for coordination. With these, APGAS can express both regular and irregular parallelism, message-passing-style and active-message-style computations, fork-join and bulk-synchronous parallelism. Both its modern, type-safe sequential core and simple programming model for concurrency and distribution contribute to making X10 a high-productivity language in the HPC and Big Data spaces. User productivity is further enhanced by providing tools such as an Eclipse-based IDE (X10DT). Implementations of X10 are available for a wide variety of hardware and software platforms ranging from laptops, to commodity clusters, to supercomputers. -
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qvge
Qt Visual Graph Editor
qvge is a multiplatform graph editor written in C++/Qt. Its main goal is to make possible visually edit two-dimensional graphs in a simple and intuitive way. Please note that qvge is not a replacement for such a software like Gephi, Graphvis, Dot, yEd, Dia and so on. It is neither a tool for "big data analysis" nor a math application. It is really just a simple graph editor :) -
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Universal Java Matrix Package
sparse and dense matrix, linear algebra, visualization, big data
The Universal Java Matrix Package (UJMP) is an open source Java library which provides sparse and dense matrix classes, as well as a large number of calculations for linear algebra such as matrix multiplication or matrix inverse. Operations such as mean, correlation, standard deviation, replacement of missing values or the calculation of mutual information are supported, too. The Universal Java Matrix Package provides various visualization methods, import and export filters for a large number of file formats, and even the possibility to link to JDBC databases. Multi-dimensional matrices as well as generic matrices with a specified object type are supported and very large matrices can be handled even when they do not fit into memory. -
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FastoNoSQL
FastoNoSQL it is GUI platform for NoSQL databases.
Gui managment admin tool for: Redis Memcached SSDB LevelDB RocksDB UnQLite LMDB UpscaleDB ForestDB -
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Open Source Data Quality and Profiling
World's first open source data quality & data preparation project
This project is dedicated to open source data quality and data preparation solutions. Data Quality includes profiling, filtering, governance, similarity check, data enrichment alteration, real time alerting, basket analysis, bubble chart Warehouse validation, single customer view etc. defined by Strategy. This tool is developing high performance integrated data management platform which will seamlessly do Data Integration, Data Profiling, Data Quality, Data Preparation, Dummy Data Creation, Meta Data Discovery, Anomaly Discovery, Data Cleansing, Reporting and Analytic. It also had Hadoop ( Big data ) support to move files to/from Hadoop Grid, Create, Load and Profile Hive Tables. This project is also known as "Aggregate Profiler" Resful API for this project is getting built as (Beta Version) https://sourceforge.net/projects/restful-api-for-osdq/ apache spark based data quality is getting built at https://sourceforge.net/projects/apache-spark-osdq/ -
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BIRT Report Designer
Open Source Reporting & Data Visualization Platform
BIRT is an open source technology platform used to create data visualizations and reports that can be embedded into rich client and web applications. Developers who use BIRT Designer are able to access information from multiple data sources easily and quickly in order to create reports and applications with stunning data visualizations. Actuate now provides a free report server, BIRT iHub F-Type, to deploy BIRT content so developers don't have to build their own infrastructure. With a flexible Open Data Access framework, developers can write custom data drivers to access data from any source, including Big Data sources like Apache Hadoop, Cassandra, and MongoDB, along with all traditional relational databases, Flat Files, XML data streams, and data stored in proprietary systems. Built for embedding, BIRT includes APIs for data access, chart generation, output formats, content execution, and integration within larger applications. -
23
Old File Delete
Clean up old files with a single click.
OldFileDelete (OFD) is a lightweight and efficient utility designed for those who value minimalism and order. This application allows you to delete files PC that are older than a certain date in just two clicks. Featuring a modern flat design, the interface is intuitive: simply select a folder, specify the number of days, and the program will find and remove outdated files. No complex settings—just cleanliness and speed. -
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json-scada
A portable SCADA/IoT platform centered on the MongoDB database server.
Standard IT tools applied to SCADA/IoT (MongoDB, PostgreSQL/TimescaleDB,Node.js, C#, Golang, Grafana, etc.). MongoDB as the real-time core database, persistence layer, config store, SOE historian. Portability and interoperability over Linux, Windows, x86/64, ARM. Horizontal scalability, from a single computer to big clusters (MongoDB-sharding), Bare Metal, Docker containers, VM, cloud, or hybrid deployments. Unlimited tags, servers, and users. HTML5 Web interface. UTF-8/I18N. Protocols: IEC61850 Client, IEC60870-5-101/104 Client and Server, DNP3 Client, OPC-UA Client/Server, MQTT/Sparkplug-B, Telegraf (various data sources for monitoring like Modbus, SNMP, etc.) Github. project https://github.com/riclolsen/json-scada Requirements for Windows Installer: Windows 10/11 64 bits or Server 2016, Windows PowerShell. -
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Augustus
PMML-compliant scoring engine and analytic toolkit
Augustus development has moved to google code. The new project page is augustus.googlecode.com. New releases of the project are not currently being released to sourceforge. Augustus is designed for statistical and data mining models and produces and consumes models with 10,000s of segments. Versions of Augustus support PMML 3, 4.0.1, and 4.1.
