Apache Derby VS Apache Flink

Implementing a database is out of scope for this guide, however, several guides are available online for simple embedded open source software solutions like SQLite, HyperSQL, Derby, and many more. - Source: dev.to / over 1 year ago

And finally, here comes test scoped artefacts. Apache Spark ones are included as testImplementation. Because integration tests will start the local Spark node. So, they are required during the runtime. The slf4j-api is also the runtime dependency. Testcontainers will be used to run the Aerospike instance. The janino is required by Apache Spark during the job execution. And we need Apache Derby to tune Apache Hive... - Source: dev.to / about 2 years ago

It most certainly is not just in-memory; it can do in-memory, but also has file and server options: https://h2database.com/html/features.html#connection_modes Exact same story for its HyperSQL and Derby friends: http://hsqldb.org/doc/2.0/guide/running-chapt.html#rgc_hsqldb_db https://db.apache.org/derby/#What+is+Apache+Derby%3F. - Source: Hacker News / over 2 years 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 / 23 days 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 / 4 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

Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago