Quarkus
Guava
Java
RxJava
Node.js
Thymeleaf
JaVers
Micronaut Framework
Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Apache Airflow
Spark Mail
Confluent
Apache Kafka
Quarkus Apache FlinkBased on our record, Apache Flink seems to be a lot more popular than Quarkus. While we know about 29 links to Apache Flink, we've tracked only 1 mention of Quarkus. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
First of all, extensions are developed and maintained by the Quarkus team. You can find them on the Quarkus GitHub repository. They integrate seamlessly into the Quarkus architecture as they can be processed at build time and be built in native mode with GraalVM. - Source: dev.to / over 1 year 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 / 7 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 / 21 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
Guava - Google core libraries for Java 6+.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Java - A concurrent, class-based, object-oriented, language specifically designed to have as few implementation dependencies as possible
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
RxJava - RxJava – Reactive Extensions for the JVM is a library for composing asynchronous and event-based programs using observable sequences.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.