Git vs Kafka UI
Overview
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CLI (Node.js)
Manual
Detailed Comparison
Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. | It is a simple tool that makes your data flows observable, helps find and troubleshoot issues faster and deliver optimal performance. Its lightweight dashboard makes it easy to track key metrics of your Kafka clusters - Brokers, Topics, Partitions, Production, and Consumption. |
| - | Multi-Cluster Management — monitor and manage all your clusters in one place;
Performance Monitoring with Metrics Dashboard — track key Kafka metrics with a lightweight dashboard;
View Kafka Brokers — view topic and partition assignments, controller status;
View Kafka Topics — view partition count, replication status, and custom configuration;
View Consumer Groups — view per-partition parked offsets, combined and per-partition lag;
Browse Messages — browse messages with JSON, plain text and Avro encoding;
Dynamic Topic Configuration — create and configure new topics with dynamic configuration;
Configurable Authentification — secure your installation with optional Github/Gitlab/Google OAuth 2.0 |
Statistics | |
GitHub Stars 57.1K | GitHub Stars 11.5K |
GitHub Forks 26.9K | GitHub Forks 1.3K |
Stacks 343.7K | Stacks 16 |
Followers 184.2K | Followers 19 |
Votes 6.6K | Votes 0 |
Pros & Cons | |
Pros
Cons
| No community feedback yet |
Integrations | |
| No integrations available | |
What are some alternatives to Git, Kafka UI?
Mercurial
Mercurial is dedicated to speed and efficiency with a sane user interface. It is written in Python. Mercurial's implementation and data structures are designed to be fast. You can generate diffs between revisions, or jump back in time within seconds.
SVN (Subversion)
Subversion exists to be universally recognized and adopted as an open-source, centralized version control system characterized by its reliability as a safe haven for valuable data; the simplicity of its model and usage; and its ability to support the needs of a wide variety of users and projects, from individuals to large-scale enterprise operations.
Plastic SCM
Plastic SCM is a distributed version control designed for big projects. It excels on branching and merging, graphical user interfaces, and can also deal with large files and even file-locking (great for game devs). It includes "semantic" features like refactor detection to ease diffing complex refactors.
Pijul
Pijul is a free and open source (AGPL 3) distributed version control system. Its distinctive feature is to be based on a sound theory of patches, which makes it easy to learn and use, and really distributed.
DVC
It is an open-source Version Control System for data science and machine learning projects. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
Magit
It is an interface to the version control system Git, implemented as an Emacs package. It aspires to be a complete Git porcelain. While we cannot (yet) claim that it wraps and improves upon each and every Git command, it is complete enough to allow even experienced Git users to perform almost all of their daily version control tasks directly from within Emacs. While many fine Git clients exist, only deserve to be called porcelains.
Kafka Manager
This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.
SenseOps (Qlik Sense Writeback & Version Control)
Streamline Qlik Sense with no-code DevOps, CI/CD, and powerful extensions. Build, deploy, and manage analytics faster with SenseOps.
Replicate
It lets you run machine learning models with a few lines of code, without needing to understand how machine learning works.
Kafka REST
It provides a RESTful interface to a Kafka cluster. It makes it easy to produce and consume messages, view the state of the cluster, and perform administrative actions without using the native Kafka protocol or clients. Examples of use cases include reporting data to Kafka from any frontend app built in any language, ingesting messages into a stream processing framework that doesn't yet support Kafka, and scripting administrative actions.
