Apache Drill
superset
Apache Drill | superset | |
---|---|---|
9 | 137 | |
1,897 | 59,071 | |
1.1% | 1.9% | |
8.1 | 9.9 | |
6 days ago | 1 day ago | |
Java | TypeScript | |
Apache License 2.0 | 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.
Apache Drill
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Git Query Language (GQL) Aggregation Functions, Groups, Alias
Also are you familiar with apache drill . The idea is to put an SQL interpreter in front of any kind of database just like you are doing for git here.
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Building a Data Lakehouse for Analyzing Elon Musk Tweets using MinIO, Apache Airflow, Apache Drill and Apache Superset
💡 You ca read more here.
- 【上海报案信息】高频关键词 (OC)
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DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
Apache Drill, Druid, Flink, Hive, Kafka, Spark
- 说的都是什么傻逼东西,一堆警察等了一小时不敢进去还他妈怪拜登不让学校有持枪警卫?哪个财团大得过控制整个共和党的NRA?
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Apache Drill: the reports of my death have been greatly exaggerated
>We’ve started talking about speeding up our release cadence to better reflect our recent activity.
There's been only one release per year in the past so you can't fault anyone to think the project is dead.
https://github.com/apache/drill/releases
- Concept: A open source alternative to big query ?
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Roapi: An API Server for Static Datasets
Looks super interesting and potentially useful. Curious how it compares with Apache Drill (https://drill.apache.org/).
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Does Java have an open source package that can execute SQL on txt/csv?
Check out Apache Drill: https://drill.apache.org/
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.
What are some alternatives?
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
streamlit - Streamlit — A faster way to build and share data apps.
Apache Calcite - Apache Calcite
jupyter-dash - OBSOLETE - Dash v2.11+ has Jupyter support built in!
Presto - The official home of the Presto distributed SQL query engine for big data
Apache Hive - Apache Hive
AranoDB - The official ArangoDB Java driver.
lightdash - Self-serve BI to 10x your data team ⚡️
QueryStream - Build JPA Criteria queries using a Stream-like API
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
spring-data-jpa-mongodb-expressions - Use the MongoDB query language to query your relational database, typically from frontend.
django-project-template - The Django project template I use, for installation with django-admin.