Apache Pulsar
Apache Pulsar - distributed pub-sub messaging system (by apache)
Apache Spark
Apache Spark - A unified analytics engine for large-scale data processing (by apache)
Apache Pulsar | Apache Spark | |
---|---|---|
34 | 123 | |
14,718 | 41,395 | |
0.6% | 0.5% | |
9.8 | 10.0 | |
1 day ago | 7 days ago | |
Java | Scala | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Apache Pulsar
Posts with mentions or reviews of Apache Pulsar.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-04-22.
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Every Database Will Support Iceberg — Here's Why
Ingest real-time data from Kafka, Pulsar, or CDC sources like Postgresand MySQL, with built-in support for Debezium.
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Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
Apache Pulsar: Pulsar is a distributed messaging platform developed under the Apache Foundation. Its notable features include extremely low latency, support for multi-tenancy, geo-replication across regions, and tiered storage capabilities.
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Release Radar · October 2024: Major updates from the open source community
From Apache, there's Pulsar, a distributed pub-sub messaging platform with a flexible messaging model, and an intuitive client API. The latest version brings enhanced Key_Shared subscription implementation, secure Docker runtime based on Alpine Linux and Java 21, rate limiting, enhanced client compatibility, and more. Check out the release notes to read more about all the changes since the last release.
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Top 7 Kafka Alternatives For Real-Time Data Processing
Apache Pulsar is an open-source distributed messaging platform originally developed by Yahoo! It provides a highly scalable solution for messaging and stream processing with robust durability and fault tolerance.
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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 machine's temperature exceeds a certain threshold, a streaming platform can instantly trigger an alert and engineers do timely maintenance.
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Apache Pulsar VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Help finding open source Terraform configurations that are not educational projects or developer tools
Edit: Here's a good example of what I'm looking for: https://github.com/apache/pulsar. It is a full application that happens to be deployed (or deployable) with Terraform, and the configuration files are available.
- Kafka Is Dead, Long Live Kafka
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Analyzing Real-Time Movie Reviews With Redpanda and Memgraph
In recent years, it has become apparent that almost no production system is complete without real-time data. This can also be observed through the rise of streaming platforms such as Apache Kafka, Apache Pulsar, Redpanda, and RabbitMQ.
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There are about Pulsar 10k users in Slack, but about 70 in this subreddit.
It's colored black on the refreshed Apache Pulsar site. https://pulsar.apache.org/
Apache Spark
Posts with mentions or reviews of Apache Spark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-07-04.
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Introducing RisingWave's Hosted Iceberg Catalog-No External Setup Needed
Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:
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10+ Most Powerful GitHub Repos I Discovered in 2025 (You’ll Wish You Knew Sooner)
11. Apache Spark (apache/spark) – Big Data Analytics Engine
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Every Database Will Support Iceberg — Here's Why
Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly.
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How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection.
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Apache Spark VS cocoindex - a user suggested alternative
2 projects | 1 Apr 2025
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Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a healthy balance between freedom and accountability, ultimately making it easier for developers to adapt and contribute without restrictive legal barriers. Another modern twist discussed in the article is the concept of dual licensing. Dual licensing can offer an attractive method for additional commercial exploitation while still upholding open source principles. However, as the article cautions, dual licensing involves legal intricacy and demands rigor in managing Contributor License Agreements (CLAs), a challenge that the open source community navigates with ongoing debates. For developers looking to understand similar innovative approaches to licensing, further information can be explored at License Token.
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The Application of Java Programming In Data Analysis and Artificial Intelligence
[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache Spark: Lightning-Fast Unified Analytics Engine," Available: https://spark.apache.org/. [6] Java Community Process, "Java Machine Learning Libraries and Frameworks," Available: https://www.oracle.com/java/.
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Apache Spark: Revolutionizing Big Data with Sustainable Open Source Funding
Apache Spark isn’t just a framework for distributed data processing; it’s a rich ecosystem that includes libraries for machine learning, stream processing, and graph processing. A key aspect of Spark’s ecosystem is its reliance on community contributions. Developers from around the world collaborate on its GitHub repository, ensuring that Spark remains at the cutting edge of technology. The governance process, characterized by transparency and meritocracy, builds trust among contributors and sponsors alike. An essential component of Apache Spark’s model is its use of the Apache 2.0 license. This permissive license not only shields contributors with patent protection but also allows enterprises to integrate Spark into proprietary systems without legal hurdles. The license enables a free flow of innovation—companies can both use and contribute to Spark’s codebase, leading to enhancements that benefit the entire community. The funding mechanisms sustaining Apache Spark are as diverse as they are innovative. Corporate sponsorships play a significant role, with companies dedicating resources and finances to support ongoing development. Additionally, grant programs and community donations help maintain an ecosystem where improvements and new features are continuously shared with users worldwide. These sustainable funding practices ensure that Apache Spark can meet the demands of real-time analytics and high-volume data processing.
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Automating Enhanced Due Diligence in Regulated Applications
If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline.
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Run PySpark Local Python Windows Notebook
PySpark is the Python API for Apache Spark, an open-source distributed computing system that enables fast, scalable data processing. PySpark allows Python developers to leverage the powerful capabilities of Spark for big data analytics, machine learning, and data engineering tasks without needing to delve into the complexities of Java or Scala.
What are some alternatives?
When comparing Apache Pulsar and Apache Spark you can also consider the following projects:
Apache Camel - Apache Camel is an open source integration framework that empowers you to quickly and easily integrate various systems consuming or producing data.
Smile - Statistical Machine Intelligence & Learning Engine
Apache ActiveMQ - Apache ActiveMQ Classic
Scalding - A Scala API for Cascading
RocketMQ
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.