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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Gen AI apps are built with MongoDB AtlasMongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
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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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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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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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marimo
A reactive notebook for Python
marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots, make working with data feel refreshingly fast, futuristic, and intuitive. Version with git, run as Python scripts, import symbols from a notebook into other notebooks or Python files, and lint or format with your favorite tools. You'll always be able to reproduce your collaborators' results. Notebooks are executed in a deterministic order, with no hidden state, delete a cell and marimo deletes its variables while updating affected cells. -
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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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MyCAT
Active, high-performance open source database middleware
MyCAT is an Open-Source software, “a large database cluster” oriented to enterprises. MyCAT is an enforced database which is a replacement for MySQL and supports transaction and ACID. Regarded as MySQL cluster of enterprise database, MyCAT can take the place of expensive Oracle cluster. MyCAT is also a new type of database, which seems like a SQL Server integrated with the memory cache technology, NoSQL technology and HDFS big data. And as a new modern enterprise database product, MyCAT is combined with the traditional database and new distributed data warehouse. In a word, MyCAT is a fresh new middleware of database. MyCAT ’s objective is to smoothly migrate the current stand-alone database and applications to cloud side with low cost and to solve the bottleneck problem caused by the rapid growth of data storage and business scale. -
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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 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. -
Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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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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Modin
Scale your Pandas workflows by changing a single line of code
Scale your pandas workflow by changing a single line of code. Modin uses Ray, Dask or Unidist to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code. Even using the DataFrame constructor is identical. It is not necessary to know in advance the available hardware resources in order to use Modin. Additionally, it is not necessary to specify how to distribute or place data. Modin acts as a drop-in replacement for pandas, which means that you can continue using your previous pandas notebooks, unchanged, while experiencing a considerable speedup thanks to Modin, even on a single machine. Once you’ve changed your import statement, you’re ready to use Modin just like you would pandas. -
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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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Blue Whale Configuration Platform
Blue Whale smart cloud configuration platform
Has accumulated experience in supporting hundreds of Tencent businesses, compatible with various complex system architectures, born in operation and maintenance, and proficient in operation and maintenance. From configuration management to job execution, task scheduling and monitoring self-healing, and then through operation and maintenance big data analysis to assist operational decision-making, it covers the full-cycle assurance management of business operations in a comprehensive manner. The open PaaS has a powerful development framework and scheduling engine, as well as a complete operation and maintenance development training system, which helps the rapid transformation and upgrading of operation and maintenance. Through the Blue Whale intelligent cloud system, it can help enterprises quickly realize the automation of basic operation and maintenance services, thereby accelerating the transformation of DevOps, realizing a tool culture, and maximizing operational efficiency. -
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Fluid
Fluid, elastic data abstraction and acceleration for BigData/AI apps
Fluid, elastic data abstraction and acceleration for BigData/AI applications in the cloud. Provide DataSet abstraction for underlying heterogeneous data sources with multidimensional management in a cloud environment. Enable dataset warmup and acceleration for data-intensive applications by using a distributed cache in Kubernetes with observability, portability, and scalability. Taking characteristics of application and data into consideration for cloud application/dataset scheduling to improve the performance. -
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ODD Platform
First open-source data discovery and observability platform
Unlock the power of big data with OpenDataDiscovery Platform. Experience seamless end-to-end insights, powered by unprecedented observability and trust - from ingestion to production - while building your ideal tech stack! Democratize data and accelerate insights. Find data that fits your use case and discover hints left by your peers to leverage existing knowledge. Explore tags, ownership details, links to other sources and other information to shorten and simplify data discovery phase. Forget unnerved stakeholders and wasting too much time on digging the root cause of data issues when it fails. With ODD’s automatic company-wide ingestion-to-product lineage you’ll have answers in just seconds and stakeholders won’t need to wait. Sleep well, knowing all your data is in check. Forget manual testing, days of debugging, and weeks of worrying. Know the impact of each code change with automatic testing. Enjoy lineage and alerts powered with data quality information. -
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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. -
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HPCC Systems
End-to-end big data in a massively scalable supercomputing platform.
HPCC Systems® (www.hpccsystems.com) from LexisNexis® Risk Solutions is a proven, open source solution for Big Data insights that can be implemented by businesses of all sizes. With HPCC Systems, developers can design applications with Big Data at their core, enabling businesses to better analyze and understand data at scale, improving business time to results and decisions. HPCC Systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. Read our blog (http://hpccsystems.com/blog ), or connect with us on Twitter (@hpccsystems), Facebook (https://www.facebook.com/hpccsystems ) and LinkedIn (http://www.linkedin.com/company/hpcc-systems) HPCC Systems is available on AWS & can be configured through the Instant Cloud Solution.">
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FastoRedis
Cross-platform open source Redis DB management tool
FastoRedis (fork of FastoNoSQL) — is a cross-platform open source Redis management tool (i.e. Admin GUI). It put the same engine that powers Redis's redis-cli shell. Everything you can write in redis-cli shell — you can write in FastoRedis! Our program works on the most amount of Linux systems, also on Windows, Mac OS X, FreeBSD and Android platforms, on desktops and embedded devices. -
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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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PANDA
A comprehensive and flexible quantification tool for proteomics data
PANDA is a comprehensive and flexib tool for quantitative proteomics data analysis, which is developed based on our solid foundations in quantitative proteomics for years. Several novelties have been implemented in it. First, we implement the advantage algorithms of LFQuant (Proteomics 2012, 12, (23-24), 3475-84) and SILVER (Bioinformatics 2014, 30, (4), 586-7) into PANDA. Second, we consider the state-of-art concept of quantification reliability in this quantitative workflow. On the levels of spectra, peptides and proteins, PANDA works out a few quantitative filters and new scores for quantification confidence. Third, PANDA is designed for processing proteomics big data in parallel. -
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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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Parkiet
Parquet format file GUI editor
Parquet file viewer and editor written in Java and SWT. It uses Apache Avro library for reading and writing edited parquet files. Only Parquet files with simple data type columns are supported. -
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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size.