Software Alternatives, Accelerators & Startups

Azure Stream Analytics VS Kafka Streams

Compare Azure Stream Analytics VS Kafka Streams and see what are their differences

Azure Stream Analytics logo Azure Stream Analytics

Azure Stream Analytics offers real-time stream processing in the cloud.

Kafka Streams logo Kafka Streams

Apache Kafka: A Distributed Streaming Platform.
  • Azure Stream Analytics Landing page
    Landing page //
    2023-01-21
  • Kafka Streams Landing page
    Landing page //
    2022-11-21

Azure Stream Analytics videos

Azure Stream Analytics

More videos:

  • Review - Real-time Analytics with Azure Stream Analytics
  • Demo - Introduction to Azure Stream Analytics + Demo

Kafka Streams videos

Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Review - Big Data Analytics in Near-Real-Time with Apache Kafka Streams - Allen Underwood
  • Review - Spring Tips: Spring Cloud Stream Kafka Streams

Category Popularity

0-100% (relative to Azure Stream Analytics and Kafka Streams)
Stream Processing
57 57%
43% 43
Data Management
100 100%
0% 0
Big Data
52 52%
48% 48
Analytics
54 54%
46% 46

User comments

Share your experience with using Azure Stream Analytics and Kafka Streams. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, Kafka Streams seems to be more popular. It has been mentiond 14 times since March 2021. 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.

Azure Stream Analytics mentions (0)

We have not tracked any mentions of Azure Stream Analytics yet. Tracking of Azure Stream Analytics recommendations started around Mar 2021.

Kafka Streams mentions (14)

  • 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 month 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 1 year 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 1 year 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 1 year 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 1 year ago
View more

What are some alternatives?

When comparing Azure Stream Analytics and Kafka Streams, you can also consider the following products

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

PieSync - Seamless two-way sync between your CRM, marketing apps and Google in no time

Apache NiFi - An easy to use, powerful, and reliable system to process and distribute data.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Kibana - Easily visualize data pushed into Elasticsearch from Logstash, es-hadoop or 3rd party technologies...