Open Source TypeScript Information Analysis Software

Desktop application to efficiently search and analyze super-structured data. Powered by Zed. Zed is a system that makes data easier by utilizing our new super-structured data model. Brim is a desktop app to explore, query, and shape the data in your super-structured data lake. Brim is an open source desktop application for security and network specialists. Brim makes it easy to search and analyze data from packet captures, like those created by Wireshark, and structured logs, especially from the Zeek network analysis framework. Brim is especially useful to security and network operators that need to handle large packet captures, especially those that are cumbersome for Wireshark, tshark, or other packet analyzers. Inspired by the Unix-tools design pattern, everything Brim does can be run from the command line. Think of Brim's components like Lego blocks that you can easily interconnect and assemble. It’s all open source, so have a look.

Kepler.gl is a data-agnostic, high-performance web-based application for visual exploration of large-scale geolocation data sets. Built on top of Mapbox GL and deck.gl, kepler.gl can render millions of points representing thousands of trips and perform spatial aggregations on the fly. Kepler.gl is also a React component that uses Redux to manage its state and data flow. It can be embedded into other React-Redux applications and is highly customizable. For information on how to embed kepler.gl in your app take a look at this step-by-step tutorial on vis.academy. Built with Deck.gl, Kepler.gl utilizes WebGL to render large datasets quickly and efficiently. You can easily drag and drop a dataset, add filters, apply scales, and do aggregation on the fly. Built on React & Redux, Kepler.gl can be embedded inside your own mapping applications.

A set of tslint rules for static code analysis of Angular TypeScript projects. (If you are using ESLint check out the new angular-eslint repository.). You can run the static code analyzer over web apps, NativeScript, Ionic, etc. Note that by default all components are aligned with the style guide so you won't see any errors in the console. Codelyzer supports any template and style language by custom hooks. If you're using Sass for instance, you can allow codelyzer to analyze your styles by creating a file .codelyzer.js in the root of your project (where the node_modules directory is). In the configuration file can implement custom pre-processing and template resolution logic. Lint rules encode logic for syntactic & semantic checks of TypeScript, HTML, CSS and Angular expressions source code.

NAVAL-QDA is a desktop application for qualitative data analysis (QDA). Import interview transcripts, documents, and media; code them against a hierarchical node tree; classify sources by case and attribute; write analytic memos; run text and coding queries; visualize patterns; and generate reports — all from a local Electron + React app. Key features: import .txt/.docx/.pdf plus audio/video for transcription; hierarchical coding with merge/move and percent-coded tracking; cases, attributes, and a classification sheet; memos linked to sources, cases, or nodes; text search, word frequency, coding, matrix coding, and coding-comparison queries; word cloud, hierarchy treemap, and similarity-cluster visualizations; coding reports and project summaries; and optional, opt-in AI-assisted summaries/code suggestions. Local-first: project data lives in a local SQLite database — nothing leaves your machine unless you enable the AI provider. Status: early/pre-release (v0.1.0).

Continuous profiling for analysis of CPU, memory usage over time, and down-to-the-line number. Saving infrastructure costs, improving performance, and increasing reliability. A single profiler, using eBPF, automatically discovers targets from Kubernetes or systemd across the entire infrastructure with very low overhead. Supports C, C++, Rust, Go, and more. Both producing pprof formatted profiles with the eBPF-based profiler and ingesting any pprof formatted profiles allowing for wide language adoption and interoperability with existing tooling. Efficiently storing profiling data while retaining raw data and allowing slicing and dicing of data through a label-based search. Aggregate profiling data infrastructure-wide, view single profiles in time or compare on any dimension.