CyberChef is a simple, intuitive web app for carrying out all manner of "cyber" operations within a web browser. These operations include simple encoding like XOR or Base64, more complex encryption like AES, DES and Blowfish, creating binary and hexdumps, compression and decompression of data, calculating hashes and checksums, IPv6 and X.509 parsing, changing character encodings, and much more.
 
The tool is designed to enable both technical and non-technical analysts to manipulate data in complex ways without having to deal with complex tools or algorithms. It was conceived, designed, built and incrementally improved by an analyst in their 10% innovation time over several years.

CyberChef is a simple, intuitive web app for carrying out all manner of "cyber" operations within a web browser. These operations include simple encoding like XOR or Base64, more complex encryption like AES, DES and Blowfish, creating binary and hexdumps, compression and decompression of data, calculating hashes and checksums, IPv6 and X.509 parsing, changing character encodings, and much more.

CyberChef - The Cyber Swiss Army Knife for Developers (DevToolbox)

OpenRefine (previously Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.



OpenRefine - Powerful tool for working with messy data

Elementary was built out of the need to effortlessly and immediately gain visibility into the data stack, starting with tracing the actual upstream & downstream dependencies in the data warehouse, without any implementation efforts, security risks or compromises on accuracy.
 
Features:
<ul><li>Lineage visualization: Visual map of data flow and dependencies in the data warehouse.&nbsp;</li><li>Dataset status: Present data about freshness and volume on the lineage graph.</li><li>Accuracy: Reflects the actual state in the DWH based on logs.</li><li>Plug-and-play: No need for code changes.</li><li>Graph filters: Filter the graph by dataset, dates, direction and depth.</li></ul>

Elementary was built out of the need to effortlessly and immediately gain visibility into the data stack, starting with tracing the actual upstream & downstream dependencies in the data warehouse, without any implementation efforts, security risks or compromises on accuracy.

Elementary - Data observability platform for modern data teams that is open and transparent

Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science. Build an app in a few lines of code with magically simple API. Adding a widget is the same as declaring a variable. No need to write a backend, define routes etc. Effortlessly share, manage, and collaborate on your apps directly from Streamlit. Apps are deployed directly from Github repo.

Streamlit - The fastest way to build data apps in Python

Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts. It easily integrates your data, using either our simple no-code viz builder or state of the art SQL IDE. Superset can query data from any SQL-speaking datastore or data engine (e.g. Presto or Athena) that has a Python DB-API driver and a SQLAlchemy dialect.
 
Superset provides: 
<ul>
<li>An intuitive interface for visualizing datasets and
crafting interactive dashboards</li>
<li>A wide array of beautiful visualizations to showcase your data</li>
<li>Code-free visualization builder to extract and present datasets</li>
<li>A world-class SQL IDE for preparing data for visualization, including a rich metadata browser</li>
<li>A lightweight semantic layer which empowers data analysts to quickly define custom dimensions and metrics</li>
<li>Out-of-the-box support for most SQL-speaking databases</li>
<li>Seamless, in-memory asynchronous caching and queries</li>
<li>An extensible security model that allows configuration of very intricate rules on
on who can access which product features and datasets.</li>
<li>Integration with major
authentication backends (database, OpenID, LDAP, OAuth, REMOTE_USER, etc)</li>
<li>The ability to add custom visualization plugins</li>
<li>An API for programmatic customization</li>
<li>A cloud-native architecture designed from the ground up for scale</li>
</ul>

Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts. It easily integrates your data, using either our simple no-code viz builder or state of the art SQL IDE. Superset can query data from any SQL-speaking datastore or data engine (e.g. Presto or Athena) that has a Python DB-API driver and a SQLAlchemy dialect.

Apache Superset is a Data Visualization and Data Exploration Platform 

Kapacitor is a open source framework for processing, monitoring, and alerting on time series data. Kapacitor imports (stream or batch) time series data, and then transform, analyze, and act on the data. It uses Telegraf to collect system metrics on your local machine and store them in InfluxDB.
 
Kapacitor’s alerting system follows a publish-subscribe design pattern. Alerts are published to topics and handlers subscribe to a topic. This pub/sub model and the ability for these to call User Defined Functions make Kapacitor very flexible to act as the control plane in your environment, performing tasks like auto-scaling, stock reordering, and IoT device control.

Kapacitor is a open source framework for processing, monitoring, and alerting on time series data. Kapacitor imports (stream or batch) time series data, and then transform, analyze, and act on the data. It uses Telegraf to collect system metrics on your local machine and store them in InfluxDB.

Kapacitor - Open source framework for processing, monitoring, and alerting on time series data

NFStream is a Python package providing fast, flexible, and expressive data structures designed to make working with online or offline network data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world network data analysis in Python. Additionally, it has the broader goal of becoming a common network data processing framework for researchers providing data reproducibility across experiments. NFStream extracts +90 flow features and can convert it directly to a pandas Dataframe or a CSV file.


NFStream - A Flexible Network Data Analysis Framework

Metabase is the easy, open source way for everyone in your company to ask questions and learn from data. Get a real-time glimpse into what your company is learning about your data. Activity helps people in your company find an answer, jump start their own exploration, or improve existing questions. 

Metabase - The simplest, fastest way to get business intelligence and analytics  to everyone in your company

Pilosa is an open source, distributed bitmap index that dramatically accelerates continuous analysis across multiple, massive data sets.  Pilosa collapses the speed and batch layer by abstracting the index from data storage, optimizing it for massive scale, and making your data instantly queryable and continuously analyzable. 

Pilosa - Distributed bitmap index that dramatically accelerates queries across multiple, massive data sets

Educate and Empower software professionals in open source tools and technologies.

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