Google Cloud Memorystore VS Apache Flink

I imagine that would work. I'd probably default to a redis https://cloud.google.com/memorystore because it feels more boring to me. Source: 6 months ago

I suggest you to use realtime database. It is cheaper than Memorystore (if you use in Google Cloud) and realtime database has a free tier. Source: 11 months ago

Memorystore is Google-hosted Redis/Memcached. You could set up a virtual machine and install Redis/Memcached yourself, but Memorystore eliminates that extra work and provides you with a well-working cache out of the box. Source: about 1 year ago

Memorystore is the managed cache service on GCP. https://cloud.google.com/memorystore. Source: over 1 year ago

Memorystore, the GCP managed service for cache, is not a service by itself, you need to choice the backend behind with Redis or memcached. These two kinds of configurations for Memorystore do not have the same model pricing. Memorystore for memcached is mostly based on Compute Engine model with pricing based on the number of nodes and vCPU + RAM per node. Even if the model pricing is nearly the same, the... - Source: dev.to / almost 2 years 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 / 11 days 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 / about 1 month 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