Slicing Pie VS Apache Flink

You could try https://slicingpie.com/, but in practice I found it burdensome. - Source: Hacker News / 6 months ago

Since we were new to one another, we used the Slicing Pie methodology and monitored one another for progress and reasonableness in their reporting of time spent. Once we realized that we were both consistently committed to the project, we did a static split of equity and were on our way. Source: 11 months ago

Take a look at slicingpie.com as others have suggested. Source: 12 months ago

- ABSOLUTELY do vesting with one year cliff as others mentioned. Do NOT commit to 49% of the get-go just in case he has a change of heart in a few months... An alternative option is the Slicing Pie approach: https://slicingpie.com/ . Slicing pie is more complicated however it is fairer and will allow you to add more people. Source: 12 months ago

The slicing pie model might work (https://slicingpie.com/) for you depending on how far you are along. I am using this for a rev share project and we all feel it covered things pretty well. Source: 12 months 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 / about 21 hours 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 / 29 days 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

Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago