Neat! This seems very similar to Sheety[0], which I've used a bunch of times before (and found a few bugs...). Do you have any plans to open source? [0]https://sheety.co. - Source: Hacker News / 3 months ago

You can just use retool alone or if you still want to use bubble maybe the easiest way would be to use https://sheety.co. Source: over 1 year ago

Well there’s https://sheety.co that provides an api to write to google sheets. You just need to set up the fetch mechanism on your web page. Source: over 1 year ago

Https://sheety.co/ I found this website, where I can have the API with the needed google sheet and with the API request/response, I am getting the required details. Source: over 1 year ago

Calling a 3rd party API: There is a complete ecosystem providing "google-sheets-as-DB". I personally tested and recommend https://sheetson.com/ but there are a lot more with free tiers https://sheetsu.com/ https://sheety.co/. Source: about 2 years ago

Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 22 days 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 1 month 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 2 months 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 / 3 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 / 5 months ago