Apache Hive VS superset

Trino or Hive for SQL querying. Get Trino/Hive to talk to Nessie.

Hive, A data warehouse infrastructure that provides data summarization and ad hoc querying.

Apache Drill, Druid, Flink, Hive, Kafka, Spark

In this article, I'm showing you how to create a Spring Boot app that loads data from Apache Hive via Apache Spark to the Aerospike Database. More than that, I'm giving you a recipe for writing integration tests for such scenarios that can be run either locally or during the CI pipeline execution. The code examples are taken from this repository.

ListItem(name='Apache Hive', website='https://hive.apache.org/', category='Interactive Query', short_description='Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.'),

Apache Hive takes in a specific SQL dialect and converts it to map-reduce.

Apache Hive

ClickHouse is highly compatible with a wide range of data tools, including ETL/ELT processes and BI tools like Apache Superset. It supports virtually all common data formats, making integration seamless across diverse ecosystems.

With the transition from ETL to ELT, data warehouses have ascended to the role of data custodians, centralizing customer data collected from fragmented systems. This pivotal shift has been enabled by a suite of powerful tools: Fivetran and Airbyte streamline the extraction and loading, DBT handles the transformation, and robust warehousing solutions like Snowflake and Redshift store the data. While traditionally these technologies catered to analytical and business intelligence applications (think Looker and Superset), there's an increasing recognition of their potential for more dynamic operational analytics, delivering real-time data for actionable insights.

git clone --branch 4.0.2 --depth 1 https://github.com/apache/superset.git cd superset sudo docker compose up

Also, instead of the custom Dashboard app, a proper BI tool like Power BI, Tableau, Apache Superset, ..., etc. will be more powerful and flexible.

We are looking at moving our Power BI stuff to Apache Superset [1]. How does this compare to Superset?

[1] https://superset.apache.org/

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

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.

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.