Also, many tools only talk SQL. Shillelagh was developed for Apache Superset, a powerful open source business intelligence web application, and allows it to query an infinitude of new data sources without having to change a single line of code in Superset. Source: almost 2 years ago

I'm adding this to Apache Superset today! Source: almost 2 years ago

I also like to do some data analysis on the side and recently ran across Apache Superset which describes itself as a "modern data exploration and data visualization platform". Coincidentally, Superset has a lot of Python code and can be deployed in containers (nine of them at current count!). - Source: dev.to / about 2 years ago

Please don't hesitate to like and bookmark this post, write a comment, and give a star to Cube and Superset on GitHub. I hope these tools would be a part of your toolkit when you decide to build a metrics store and a business intelligence application on top of it. - Source: dev.to / over 2 years ago

Discussion by kgabryje at apache / superset “feat(native-filters): add search all filter options #14710“. - Source: dev.to / over 2 years ago

I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / about 18 hours ago

You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / 15 days ago

Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / about 1 month ago

Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago

Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 5 months ago