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Apache Flink VS Apache Pulsar

Compare Apache Flink VS Apache Pulsar and see what are their differences

Apache Flink logo Apache Flink

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

Apache Pulsar logo Apache Pulsar

Apache Pulsar is an open-source, distributed messaging and streaming platform built for the cloud.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • Apache Pulsar Landing page
    Landing page //
    2023-12-17

Apache Flink videos

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

Apache Pulsar videos

Introduction to Apache Pulsar Basics

Category Popularity

0-100% (relative to Apache Flink and Apache Pulsar)
Big Data
100 100%
0% 0
Developer Tools
61 61%
39% 39
Stream Processing
100 100%
0% 0
Data Integration
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Flink and Apache Pulsar

Apache Flink Reviews

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Apache Pulsar Reviews

Best message queue for cloud-native apps
Pulsar also provides a rich set of client libraries for various programming languages, making it easy to build messaging and streaming applications using Pulsar. Apache Pulsar is a popular choice for real-time data processing and messaging in large-scale data processing applications, such as those used in the financial, telecommunications, and internet-of-things industries.
Source: docs.vanus.ai

Social recommendations and mentions

Based on our record, Apache Flink seems to be a lot more popular than Apache Pulsar. While we know about 30 links to Apache Flink, we've tracked only 1 mention of Apache Pulsar. 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.

Apache Flink mentions (30)

  • Show HN: Restate, low-latency durable workflows for JavaScript/Java, in Rust
    Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 3 days ago
  • Array Expansion in Flink SQL
    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 / 23 days ago
  • Show HN: An SQS Alternative on Postgres
    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 / about 1 month ago
  • 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 / 2 months 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 / 4 months ago
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Apache Pulsar mentions (1)

  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Stream-processing platforms such as Apache Kafka, Apache Pulsar, or Redpanda are specifically engineered to foster event-driven communication in a distributed system and they can be a great choice for developing loosely coupled applications. Stream processing platforms analyze data in motion, offering near-zero latency advantages. For example, consider an alert system for monitoring factory equipment. If a... - Source: dev.to / 4 months ago

What are some alternatives?

When comparing Apache Flink and Apache Pulsar, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

RabbitMQ - RabbitMQ is an open source message broker software.

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

NATS - NATS.io is an open source messaging system for cloud native applications, IoT messaging, Edge, and microservices architectures.

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Memphis - Highly scalable, and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming applications fast.