EditorX Make your own website with just a few clicks and drag and drop. You can also write your custom code if you want. 🔗 https://editorx.com. - Source: dev.to / almost 2 years ago

First you will need to get familiar with Editor X and Wix Velo. Don't worry, Its easier than you think. - Source: dev.to / over 2 years ago

I'm using Editor X to compare what normal code verses no-code looks like, and it really impressive how quickly things can be done. There will be situations where this will defiantly become the mainstream for creating websites in the future, when creating simple buttons takes seconds instead of minutes to make. (see full vid below). - Source: dev.to / over 2 years ago

Something like "Editor-X" https://editorx.com (but that works only for Wordpress). Source: 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 / 15 days 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 / 30 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 2 months 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 / 4 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 / 6 months ago