Spring Framework VS Apache Flink

Spring Framework Reviews

Apache Flink Reviews

Spring Framework mentions (11)

• Want to Get Better at Java? Go Old School.

  We had to write our own frameworks (uphill, both ways) but most current frameworks will have similar documentation pages as well. Both Apache and Spring are especially good at that. - Source: dev.to / about 1 year ago

• Best Frameworks For Web Development

  Framework link: https://spring.io/projects/spring-framework Github Link: https://github.com/spring-projects/spring-framework. - Source: dev.to / over 1 year ago

• What to you do now?

  A common used Java framework is Spring framework (ie https://spring.io/projects/spring-framework and short tutorials at https://www.baeldung.com/spring-intro). Source: over 1 year ago

• What is Spring actually?

  The most popular libraries are Spring Boot, which I mentioned above, and the[ Spring Framework](https://spring.io/projects/spring-framework), which makes it easy to start an application with different objects for different environments (e.g. You make a blueprint for objects that are used in a testing environment, and a separate one with objects for the prod environment). Source: almost 2 years ago

• What is Spring actually?

  Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform. Source: almost 2 years ago

Apache Flink mentions (27)

• Top 10 Common Data Engineers and Scientists Pain Points in 2024

  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 / 8 days ago

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  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 / 2 months ago

• Go concurrency simplified. Part 4: Post office as a data pipeline

  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

• Five Apache projects you probably didn't know about

  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 / 4 months ago

• Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog

  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 / 4 months ago