Kafka Streams Reviews and details

Social recommendations and mentions

• 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 / about 1 year ago

• Forward Compatible Enum Values in API with Java Jackson

  We’re not discussing the technical details behind the deduplication process. It could be Apache Flink, Apache Spark, or Kafka Streams. Anyway, it’s out of the scope of this article. - Source: dev.to / over 2 years ago

• Kafka Internals - Learn kafka in-depth (Part-1)

  In pub-sub systems, you cannot have multiple services to consume the same data because the messages are deleted after being consumed by one consumer. Whereas in Kafka, you can have multiple services to consume. This opens the door to a lot of opportunities such as Kafka streams, Kafka connect. We’ll discuss these at the end of the series. - Source: dev.to / over 2 years ago

• Event streaming in .Net with Kafka

  Internally, Streamiz use the .Net client for Apache Kafka released by Confluent and try to provide the same features than Kafka Streams. There is gap between these two library, but the trend is decreasing after each release. - Source: dev.to / over 2 years ago

• Apache Pulsar vs Apache Kafka - How to choose a data streaming platform

  Both Kafka and Pulsar provide some kind of stream processing capability, but Kafka is much further along in that regard. Pulsar stream processing relies on the Pulsar Functions interface which is only suited for simple callbacks. On the other hand, Kafka Streams and ksqlDB are more complete solutions that could be considered replacements for Apache Spark or Apache Flink, state-of-the-art stream-processing... - Source: dev.to / over 2 years ago

• Running Apache Kafka on Containers

  Apache Kafka is one of the most famous data stores. It's a go-to tool to collect streaming data at scale and process them with either Kafka streams or Apache Spark. - Source: dev.to / over 2 years ago

• Evolutionary Data Infrastructure

  Therefore, I still recommend using a streaming framework such as Apache Flink or Apache Kafka Streams. - Source: dev.to / over 2 years ago

• Spark vs Flink vs ksqlDB for stream processing

  The database ksqlDB is for building stream processing applications on top of Apache Kafka. It is based on the Kafka Streams API and licensed under the Confluent Community License Agreement. It is also a distributed, scalable, real-time stream processing framework that provides a lightweight SQL syntax. - Source: dev.to / over 2 years ago

• Supporting Cross Node Interactive Queries In Kafka Streams

  Kafka Streams is a powerful tool that adds a high-level abstraction on top of Kafka’s rock-solid infrastructure to enable building streaming applications. It has several features mainly grouped around two concepts, KStreams which represents an infinite stream of data, and KTables which represent a projection of a stream’s data. Even calling these two concepts different is not completely true due to the... - Source: dev.to / about 3 years ago

• Example of a consumer and producer in one application?

  Sounds like you should have a look at Kafka streams: https://kafka.apache.org/documentation/streams/. Source: over 3 years ago

• Spring Cloud Stream & Kafka Streams Binder first steps

  Kafka Streams Documentation Kafka Streams Developer Guide v2.7 Kafka Streams DSL v2.7 Testing Kafka Streams v2.7. - Source: dev.to / over 3 years ago

• confluent Schema Registry and Rust

  So what is actually the Schema Registry? And how does it help to make sense of binary data? In essence Schema Registry is an application with some Rest endpoints, from which schema's can be registered and retrieved. It used to only support Apache Avro. Later support for Protobuf and JSON Schema was added. Part of the same Github project, and what makes Schema Registry easy to use, is a Collection of Java classes... - Source: dev.to / almost 4 years ago

• Apache Heron: A realtime, distributed, fault-tolerant stream processing engine

  - Hadoop Streaming is a thing [1]. - Kafka Streams is a thing [2]. - Spark Streaming is a thing, as you mentioned [3]. The only one that should be left out is Confluent, the rest are all Apache products with 'streaming' features that are confusingly similar to someone not already an expert in distinguishing among them. [1] https://hadoop.apache.org/docs/current/hadoop-streaming/HadoopStreaming.html [2]... - Source: Hacker News / almost 4 years ago

• Processing Time-Series Data with Redis and Apache Kafka

  That's where Apache Kafka comes in! In addition to the core broker, it has a rich ecosystem of components, including Kafka Connect (which is a part of the solution architecture presented in this blog post), client libraries in multiple languages, Kafka Streams, Mirror Maker etc. - Source: dev.to / almost 4 years ago