Open Source C# Big Data Tools for Windows

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.

.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer. .NET for Apache Spark runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. It also runs on all major cloud providers including Azure HDInsight Spark, Amazon EMR Spark, AWS & Azure Databricks.

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.

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.

DSTK - DataScience ToolKit is an opensource free software for statistical analysis, data visualization, text analysis, and predictive analytics. Newer version and smaller file size can be found at: https://sourceforge.net/projects/dstk3/ It is designed to be straight forward and easy to use, and familar to SPSS user. While JASP offers more statistical features, DSTK tends to be a broad solution workbench, including text analysis and predictive analytics features. Of course you may specify JASP for advanced data editing and RapidMiner for advanced prediction modeling. DSTK is written in C#, Java and Python to interface with R, NLTK, and Weka. It can be expanded with plugins using R Scripts. We have also created plugins for more statistical functions, and Big Data Analytics with Microsoft Azure HDInsights (Spark Server) with Livy. License: R, RStudio, NLTK, SciPy, SKLearn, MatPlotLib, Weka, ... each has their own licenses.

This tool will convert an Excel Spreadsheet (.xls and .xlsx files) into SQL INSERTs to one table. The first row of your excel sheet will be used as the column names so you cannot have any NULL values. Then the data underneath the column name is applied into that column with the generated insert statement. You can Save or Copy the data and then use Find and Replace if you need to tweak. Good for big data. Needed this for my work so created over the weekend, happy to share with the community. Requires .NET framework on your PC. If you're on windows you're okay.

FrincBackup means free incremental backup. It is developed for backing up a x TB NAS with storage devices in a logical volume to multiple removable storage devices, such as 500 GB USB hard drives. Files are backuped as files (not as an archive) and are readable without the need of a tool and without the need of FrincBackup itself (allthough there is a restore mode for better handling).

GnuCopy is an Open-Source tool to copy and archive all your important data. It supports all important archive typs like Zip and Tar to guaranty an easy and secure exchange between all types of operating systems. Additionally, you can create profiles to blacklist or whitelist specific file types or folders to seperate your big data stores for backups.