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 / 19 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 / about 1 month 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 2 months 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 / 3 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 / 5 months ago

In my usual NodeJS tech stack, which includes GraphQL, NestJS, SQL (predominantly PostgreSQL with MikroORM), I encountered these limitations. To overcome them, I've developed a new stack utilizing Rust, which still offers some ease of development:. - Source: dev.to / 9 months ago

Mikro-ORM is a TypeScript ORM that focuses on simplicity and efficiency. It supports various SQL databases and MongoDB. Mikro-ORM is known for its simplicity and developer-friendly APIs. It provides a concise syntax for defining data models and relationships, making it easy to use. - Source: dev.to / 9 months ago

I found MikroORM [0] to be quite reasonable if you're in the TS ecosystem already. It was also easy to do custom, raw queries, and really just felt like it wasn't in the way. [0] https://mikro-orm.io/. - Source: Hacker News / 9 months ago

It also does code generation into its own module, so good luck with hoisting in a monorepo where you want multiple independent prisma schemas. MikroORM[1] is a much better alternative to Prisma in my opinion but any ORM carries some form of baggage. [1] https://mikro-orm.io/. - Source: Hacker News / about 1 year ago

I recommend looking at https://mikro-orm.io/. Source: over 1 year ago