superset
nba-sql
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superset | nba-sql | |
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137 | 14 | |
58,852 | 164 | |
3.6% | - | |
9.9 | 4.5 | |
1 day ago | 17 days ago | |
TypeScript | Python | |
Apache License 2.0 | Apache License 2.0 |
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superset
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Apache Superset
Superset is absolutely phenomenal. I really hope Microsoft eventually releases all of their customizations they made to it internally to the OS community someday.
https://www.youtube.com/watch?v=RY0SSvSUkMA
https://github.com/apache/superset/discussions/20094
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A modern data stack for startups
I recently ran a little shootout between Superset, Metabase, and Lightdash. All have nontrivial weaknesses but I ended up picking Lightdash.
Superset the best of them at _data visualization_ but I honestly found it almost useless for self-serve _BI_ by business users. This issue on how to do joins in Superset (with stalebot making a mess XD) is everything difficult about Superset for BI in a nutshell. https://github.com/apache/superset/issues/8645
Metabase is pretty great and it's definitely the right choice for a startup looking to get low cost BI set up. It still has a very table centric view, but feels built for _BI_ rather than visualization alone.
Lightdash has significant warts (YAML, pivoting being done in the frontend, no symmetric aggregates) but the Looker inspiration is obvious and it makes it easy to present _groups of tables_ to business users ready to rock. I liked Looker before Google acquired it. My business users are comfortable with star and snowflake schemas (not that they know those words) and it was easy to drop Lightdash on top of our existing data warehouse.
- FLaNK Stack Weekly for 20 Nov 2023
- Hiding tokens retrieved via API from the html source?
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Yandex open sourced it's BI tool DataLens
Or like not being able to delete a user without running some SQL:
https://github.com/apache/superset/issues/13345
Almostl instantly run into this issue setting up a test instance of Superset. And the issue has been around for years.
- Apache Superset Is a Data Visualization and Data Exploration Platform
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Apache Superset: Installing locally is easy using the makefile
Are you interested in trying out Superset, but you're intimidated by the local setup process? Worry not! Superset needs some initial setup to install locally, but I've got a streamlined way to get started - using the makefile! This file contains a set of scripts to simplify the setup process.
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More public SQL-queryable databases?
Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.
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How useful is SQL for managers?
if they don't want to pay for powerbi, can try something like https://superset.apache.org/
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Real-time data analytics with Apache Superset, Redpanda, and RisingWave
In today's fast-paced data-driven world, organizations must analyze data in real-time to make timely and informed decisions. Real-time data analytics enables businesses to gain valuable insights, respond to real-time events, and stay ahead of the competition. Also, the analytics engine must be capable of running analytical queries and returning results in real-time. In this article, we will explore how you can build a real-time data analytics solution using the open-source tools Redpanda a distributed streaming platform, Apache Superset, a data visualization, and a business intelligence platform, combined with RisingWave a streaming database.
nba-sql
- Shitpost(?) From Nov to Dec, Braun was trusted with 50% more minutes per game, (when he checked in at all), and it resulted in a 50% bump in fg pct.
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nba-sql - A SQL Database for the NBA
I've searched for a good database to analyze NBA players and teams, and couldn't find one. So, I've been working on an app that builds a SQLite / Postgres / MySQL database for the NBA. You can get the Windows alpha here: https://github.com/mpope9/nba-sql/releases/tag/v0.0.6 For OSX / Linux, you need to run it from the commandline. This is very much in the alpha phase, but development is happening at a steady pace in my free time. I've been able to learn some interesting things already, there are examples in the wiki here. I have more advanced example queries that I'm still working on, and have yet to add them to the wiki if any are interested. I'd love some feedback, if anyone has experience with SQL or databases. nba-sql will build a SQLite database by default, but it also supports Postgres and MySQL. You run the application to build the database, then use something like DBeaver to query it.
- Jalen Green Shot Attempts 2021-2022 Season [OC]
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How often has a playoff team done what Dallas did yesterday?
This is what I've found - https://github.com/mpope9/nba-sql
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Steph Curry's Shot Distance Relative To Seconds Left In Game / Period
I used the nba-sql database for this. That database has a table called shot_chart_detail. The table has alot of interesting data, but there are a few columns that are especially helpful: minutes_remaining, seconds_remaining, and shot_distance. Using those columns, the following graph can be made:
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nba-sql: v0.0.4 - Alpha Windows Client Release!
Thanks! I used Postgres and Apache Superset. I wrote a small wiki on how to use it https://github.com/mpope9/nba-sql/wiki/Data-Visualization-with-Superset. Superset doesn't work with SQLite sadly.
- nba-sql: A NBA database from the 1996-97 season until now
- nba-sql: A Postgres Database for NBA Data.
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nba-sql: v0.0.2 Release
The latest Postgres dump can be found here: https://github.com/mpope9/nba-sql/releases/tag/v0.0.2. It is a compressed file, using xz. After decompressing it, you can run it using psql -U -P nba < nba.sql.
- nba-sql: A Relational NBA Database
What are some alternatives?
streamlit - Streamlit — A faster way to build and share data apps.
sports-analytics - Data collection, processing, visualization, modeling, and ideation in the space of sports analytics
jupyter-dash - OBSOLETE - Dash v2.11+ has Jupyter support built in!
espn-api - ESPN Fantasy API! (Football, Basketball)
Apache Hive - Apache Hive
nba_api - An API Client package to access the APIs for NBA.com
lightdash - Self-serve BI to 10x your data team ⚡️
pydfs-lineup-optimizer - Daily Fantasy Sports lineup optimzer for all popular daily fantasy sports sites
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
hoopR - An R package to quickly obtain clean and tidy men's basketball play by play data.
django-project-template - The Django project template I use, for installation with django-admin.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production