Open Source Go Data Visualization Software for Mac

Neuroglancer is a WebGL-based visualization tool designed for exploring large-scale volumetric and neuroimaging datasets directly in the browser. It allows users to interactively view arbitrary 2D and 3D cross-sections of volumetric data alongside 3D meshes and skeleton models, enabling precise examination of neural structures and biological imaging results. Its multi-pane interface synchronizes multiple orthogonal views with a central 3D viewport, making it ideal for analyzing complex brain imaging data such as connectomics datasets. Neuroglancer operates entirely client-side, fetching data over HTTP in a variety of supported formats including Neuroglancer precomputed, N5, Zarr, and NIfTI, among others. The viewer is built with a multi-threaded architecture, separating rendering and data processing to ensure smooth performance even with massive datasets. Extensively used in neuroscience research, Neuroglancer supports integration with tools.

Aquatone is a tool for visual inspection of websites across a large amount of hosts and is convenient for quickly gaining an overview of HTTP-based attack surface. Aquatone is designed to be as easy to use as possible and to integrate with your existing toolset with no or minimal glue. Aquatone is started by piping output of a command into the tool. It doesn't really care how the piped data looks as URLs, domains, and IP addresses will be extracted with regular expression pattern matching. This means that you can pretty much give it output of any tool you use for host discovery. Aquatone is now completely focused on screenshotting and reporting. I know a lot of people used Aquatone for its DNS enumeration capabilities and it was definitely very good at that when it was released. Now other tools are doing a much better job of this, so I decided to leave it out of the new Aquatone, and instead make it easy to use it with your tool of choice.

ClickVisual is a lightweight browser-based logs analytics and logs search platform for ClickHouse.

Karpor is Intelligence for Kubernetes. It brings advanced Search, Insight and AI to Kubernetes. It is essentially a Kubernetes Visualization Tool. With Karpor, you can gain crucial visibility into your Kubernetes clusters across any clouds.

An all-in-one observability solution that aims to combine the advantages of Prometheus and Grafana. It manages alert rules and visualizes metrics, logs, and traces in a beautiful web UI.

Promviz is a visualization tool for Prometheus, enabling users to create custom dashboards and analyze Prometheus metrics interactively.

Sampler is a real-time monitoring dashboard that allows developers to track various metrics, system information, and custom data sources via a terminal interface.

Understand your application quickly by seeing it in a real-time interactive display. Pick open-source or cloud-hosted options. Weave Scope automatically detects processes, containers, hosts. No kernel modules, no agents, no special libraries, no coding. Seamless integration with Docker, Kubernetes, DCOS and AWS ECS. See your Docker hosts, containers and services in real-time. Easily identify and correct issues to ensure the stability and performance of your containerized applications. View metrics, tags and metadata within the context of a process, container, service or host. Effortlessly navigate from processes inside your container to Docker hosts. Stop, start, pause and restart containers. View logs. Launch a command line. All without leaving Scope. Choose an overview of your container infrastructure, or focus on a specific microservice. Easily identify and correct issues to ensure the stability and performance of your containerized applications.

Visualize Go module dependencies in Graphviz DOT format. Run graphdot in the directory of any project using Go modules with a go.mod file to print out a dependency graph in Graphviz DOT format. The output can be piped directly into dot to generate a PNG image file. For large graphs with many nodes of dependencies, you may want to generate an SVG file to allow you to zoom in with high fidelity and save disk space instead. If you like a more UML'ish style, you can use the provided graph properties from uml.gprops.

pprof is a profiling visualization and analysis tool that ingests profiles in the profile.proto format and generates human-readable and graph-based reports. It supports multiple profile types (CPU, heap, allocations, contention, etc.) and can present data as text tables, call graphs (via Graphviz/dot), flame graphs, and interactive web UIs. The tool helps developers find hot paths, quantify resource usage, and compare profiles across runs to validate performance changes. It is widely used in Go but also has bindings and exporters for other ecosystems, and the repository includes a Go package for reading and writing profiles programmatically. The pprof command can operate on local files or fetch from targets exposing profiling endpoints, supporting iterative, production-oriented workflows. Its design and proto schema make it a lingua franca for profilers that want to interoperate across languages and runtimes.