Patroni VS Apache Flink

> I think etcd is basically a k8s only project now I hate etcd with the best of them, but etcd is used in a lot more places than just kubernetes: https://github.com/apache/apisix/blob/master/docs/en/latest/FAQ.md#why-does-apache-apisix-use-etcd-for-the-configuration-center, https://github.com/zalando/patroni#patroni-a-template-for-postgresql-ha-with-zookeeper-etcd-or-consul (this one shows up on HN quite a... - Source: Hacker News / 4 days ago

We have been using stolon + consul for years without issue in our swarm environments. It may also be possible with patroni. Source: 11 months ago

One of the solutions which made it pretty simple for us to run postgresql in a ha environment (mostly in k8s, but works standalone as well) is zalandos patroni: https://github.com/zalando/patroni (docker image: https://github.com/zalando/spilo) we've also tried other operators which were easier to get started, but they failed miserably (crunchyrolls operator is... - Source: Hacker News / 11 months ago

Hello. I am currently using this docker-compose model from Zalando repository. It does not include PgBouncer in its architecture by default. I've been trying to find a containerized implementation involving Patroni, HAProxy, Etcd and PgBouncer. I didn't find anything solid so far. Source: about 1 year ago

General purpose: Patroni - Set up your own etcd + HAProxy + Patroni + Postgres components and it'll generally manage itself after that. Source: about 1 year 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 / 26 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

Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago