Getstream.io — Build scalable In-App Chat, Messaging, Video and audio, and Feeds in a few hours instead of weeks. - Source: dev.to / 4 months ago

I have looked into a getstream.io integration, however it seems that the Ruby SDK is really treated as a second class citizen. There's bugs with the documented API (I'm having issues even creating users and querying users), the usage of the gem is low and there is an open issue since May that no one has even looked at, which doesn't give me hope for long term support. Source: 7 months ago

An ultralight social media app with no dependencies that can run on shared web hosting. It's an API like Getstream, so F/E is up to you. I've had a fork of it in production for 2.5 years on a subscription site that generates a small income. Source: 11 months ago

Now, I'm seeking your advice and opinions. If you have experience using Stream or MirrorFly for chat implementation, I'd greatly appreciate any insights you can provide. Here are some questions I have:. Source: 11 months ago

Think about the edge that you can have over thousands other people looking for a job like you. One of the ways to do it – tailor your (even small) experience to the company you are applying to. E.g. Let's take a company like Stream that have an open-source Swift SDK, try to contribute to their SDK, maybe close some `good-first-issue`s here and there, do some documentation improvements, enrich their example app. So... Source: about 1 year 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 / 6 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 / 20 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 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

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