Me too. In fact Garden (dev tooling for the Kubernetes)[0] is a Berlin start-up with three Icelandic founders. And if I'm not mistaken, two of us worked briefly with @halldorel (above commenter) at an earlier Icelandic start-up. It's a small world (if you're Icelandic). [0] https://garden.io. - Source: Hacker News / about 1 year ago

There are dedicated tools just for that. Apart from skaffold check also tilt.dev, garden.io, devspace.sh, okteto.com. Source: about 1 year ago

Would appreciate any insights on garden.io. Thanks. Source: over 1 year ago

Alternatives:Tools like Okteto, Releasehub, Docker Compose, Tilt offer the ability to spin up environments easily, but don't allow you to mix and match connections between different environments during development/testing, so the above problems would appear to persist. Telepresence and Garden.io seem to be closer to this, but aren't complete drop-in solutions. Source: over 1 year ago

I just used garden.io for a small personal project that was more about figuring out how to use garden. I started with the examples and used the Terraform GKE example. Source: almost 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 / 5 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 / 25 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