As far as keeping track of domain changes you can store DDL files in version control like you mention or use tools like Flyway (https://flywaydb.org) or Liquidbase (https://liquibase.org) which takes care of database migrations. Source: about 2 years ago

I just use SQL directly (or something like JOOQ). For database migrations I use Liquibase. Source: about 2 years ago

Regarding the migrations, there are tools such as https://liquibase.org/ or FlyAway that handle this. Heck, you can even use an ORM that has a migration baked-in but that defeats the purpose of having the migrations in a separate project. Source: about 2 years ago

I've trialled schemachange and liquibase which are change script based tools. I've ruled out a whole load of other tools that are either change script based tools or don't support Snowflake, including the following:. Source: over 2 years ago

Nowadays I prefer to automate database updates and deployment, using Liquibase and its relational database vendor agnostic syntax for that. Especially on production systems. But on local dev environments, I can still use the occasional SQL in a pinch. Source: over 2 years ago

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 / 1 day 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 / 21 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 / about 1 month 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 / 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