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 / 8 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 / 22 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

> 2. ORMs do not hide SQL nastiness. This is certainly true! I mean: ORMs are now well known to "make the easy queries slightly more easy, while making intermediate queries really hard and complex queries impossible". I think the are of ORMs is over. It simply did not deliver. If a book on SQL is --say-- 100 pages, a book on Hibernate is 400 pages. So much to learn just to make the easy queries slightly easier to... - Source: Hacker News / about 1 month ago

Another strategy is to model access control declaratively and enforce it in the application layer. ZenStack (built above Prisma ORM) and Hasura are good examples of this approach. The following code shows how access policies are defined with ZenStack and how a secured CRUD API can be derived automatically. - Source: dev.to / about 2 months ago

Today, this ecosystem is going strong with new providers like Hasura, AppWrite and Supabase powering millions of projects. There are a few reasons people choose this style of hosting, especially if they are more comfortable with frontend development. BaaS lets them set up a database in a secure way, expose some business logic on top of the data, and connect via a dev-friendly SDK from their app or website code to... - Source: dev.to / 4 months ago

Hi! If you’ve ever thought about something like using GraphQL for something like this.. You might like Hasura. (Obligatory I work for Hasura) We’ve got an OpenAPI import and you can setup cron-jobs or one-off jobs and do things like load in headers from the environment variables to pass through. There isn’t currently an easy journey for chaining multiple calls together without writing any code at all, but you can... - Source: Hacker News / 3 months ago

Hasura.io — Hasura extends your existing databases wherever it is hosted and provides an instant GraphQL API that can be securely accessed for web, mobile, and data integration workloads. Free for 1GB/month of data pass-through. - Source: dev.to / 4 months ago