tuna
Apache Superset
Our great sponsors
| tuna | Apache Superset | |
|---|---|---|
| 4 | 3 | |
| 1,263 | 34,745 | |
| - | - | |
| 0.0 | 9.9 | |
| about 2 months ago | about 3 years ago | |
| Python | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tuna
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Is AWS Lambda Cold Start Still an Issue?
Every minor detail matters and adds to the total import time as part of the cold start. We need to optimize our code and imports. If you use Python, you can analyze your code with a tool like Tuna and optimize your libraries (perhaps replace slower ones) and your imports.
- Make Python Run Faster
- Scanning Function calls in a script - is there a tool?
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Creating a Python CLI with Go(lang)-comparable startup times
I started to examine the output of python -X importtime -m gefyra 2> import.log just to check the imports. There is an awesome tool to analyze the Python imports: tuna (see: https://github.com/nschloe/tuna). tuna allows analyzing the import times from the log. Run it like so tuna import.log. It opens a browser window and visualizes the import times. With that I was able to manually move all imports to the functions in which they are needed (and bring in some other optimizations). This greatly violates PEP 8 (https://peps.python.org/pep-0008/#imports) but leads to very fast startup times.
Apache Superset
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Using KeyCloak(OpenID Connect) with Apache SuperSet
The first difference is that after pull request 4565 was merged, you can no longer do:
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Open Source Analytics Stack: Bringing Control, Flexibility, and Data-Privacy to Your Analytics
Open-source BI platforms such as Metabase (website, GitHub) and Apache SuperSet (website, GitHub) are easy to deploy without IT involvement. Metabase lets you build dashboards from the data in your warehouse easily, with no SQL, or, if you have data engineering or science know-how, inside more powerful and flexible notebooks or with SQL itself. Similarly, Apache SuperSet helps businesses explore and visualize data from simple line charts to detailed geospatial charts.
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Ask HN: What low-code “dashboarding“ SaaS would you recommend in 2021?
Check out Superset. https://github.com/apache/incubator-superset
It’s modern, easy to extend. From the same author of apache airflow.
What are some alternatives?
SnakeViz - An in-browser Python profile viewer
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
Altair - Declarative statistical visualization library for Python
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
ggplot - ggplot port for python
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
seaborn - Statistical data visualization in Python
Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
vincent
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
PyQtGraph - Fast data visualization and GUI tools for scientific / engineering applications
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications