You can use my SQL spreadsheet app: https://superintendent.app I had a similar problem at work where I needed to do some formula on a 5 GB CSV file. Excel can't handle more than 1M rows. Database is too clunky. Eventually I built a GUI wrapper on SQLite, and it grew into Superintendent.app. - Source: Hacker News / 21 days ago

Desktop first made me think of Superintendant[0] which I’ve enjoyed using 0: https://superintendent.app/. - Source: Hacker News / 10 months ago

Project 3: Electron with typescripts. 15 paying users. Source: 12 months ago

That is exactly https://superintendent.app (disclaimer: I'm the creator). - Source: Hacker News / over 1 year ago

Well, shameless plug. https://superintendent.app (paid with free trial) enables you to load a bunch of CSVs and write SQL on those CSV files. It's a much faster to work with if you know SQL well. It can also handle millions of rows easily (e.g. Loading 1GB CSV file takes 10s on Macbook Pro). I initially built it because I had to identify the mismatched transactions between 2 giant CSVs using "full outer join". - Source: Hacker News / over 1 year 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 / 4 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 / 24 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 / about 1 month 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 / 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