Apache Pulsar vs Apache Spark Streaming comparison

Pulsar and Apache are both solutions in the Streaming Analytics category. Pulsar is ranked #19, while Apache is ranked #10 with an average rating of 8.2. Pulsar holds a 2.0% mindshare in SA, compared to Apache’s 3.1% mindshare. Additionally, 100% of Pulsar users are willing to recommend the solution, compared to 91% of Apache users who would recommend it.

Apache Pulsar

Read 1 Apache Pulsar review

1,183 Views
756 Comparison Views

100% willing to recommend

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

1,992 Views
1,481 Comparison Views

91% willing to recommend

Apache Pulsar

Apache Spark Streaming

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Executive Summary

We performed a comparison between Apache Pulsar and Apache Spark Streaming based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Microsoft and others in Streaming Analytics.

To learn more, read our detailed Streaming Analytics Report (Updated: January 2025).

Buyer's Guide

Streaming Analytics

January 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Apache Pulsar

Ranking in Streaming Analytics

19th

Average Rating

8.0

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

10th

Average Rating

8.0

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2025, in the Streaming Analytics category, the mindshare of Apache Pulsar is 2.0%, up from 1.9% compared to the previous year. The mindshare of Apache Spark Streaming is 3.1%, down from 3.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Christian Bonami

Solution Architect at Vlaanderen connect.

The solution can mimic other APIs without changing a line of code

The solution operates as a classic message broker but also as a streaming platform. It operates differently than a traditional streaming platform with storage and computing handled separately. It scales easier and better than Kafka which can be stubborn. You can even make it act like Kafka because it understands Kafka APIs. There are even companies that will sell you Kafka but underneath it is Apache Pulsar. The solution is very compatible because it can mimic other APIs without changing a line of code.

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AbhishekGupta

Engineering Leader at Walmart

Easy integration, beneficial auto-scaling, and good open-sourced support community

The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better. Apache Spark Streaming does not have auto-tuning. A customer needs to invest a lot, in terms of management and maintenance.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The solution operates as a classic message broker but also as a streaming platform."

"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."

"It's the fastest solution on the market with low latency data on data transformations."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."

"The solution is very stable and reliable."

More Apache Spark Streaming pros

Cons

"Documentation is poor because much of it is in Chinese with no English translation."

"The solution itself could be easier to use."

"Integrating event-level streaming capabilities could be beneficial."

"The initial setup is quite complex."

"We would like to have the ability to do arbitrary stateful functions in Python."

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

"The debugging aspect could use some improvement."

"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

"In terms of improvement, the UI could be better."

More Apache Spark Streaming cons

Pricing and Cost Advice

Information not available

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."

"People pay for Apache Spark Streaming as a service."

"I was using the open-source community version, which was self-hosted."

"Spark is an affordable solution, especially considering its open-source nature."

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Top Industries

By visitors reading reviews

Computer Software Company

20%

Financial Services Firm

10%

Government

Manufacturing Company

Financial Services Firm

26%

Computer Software Company

20%

Manufacturing Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

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What do you like most about Apache Spark Streaming?

Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.

See all answers

What needs improvement with Apache Spark Streaming?

We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...

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What is your primary use case for Apache Spark Streaming?

We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.

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Comparisons

Apache Flink vs Apache Pulsar

Compared 38% of the time

Amazon MSK vs Apache Pulsar

Compared 13% of the time

Amazon Kinesis vs Apache Pulsar

Compared 9% of the time

Redpanda vs Apache Pulsar

Compared 9% of the time

More Apache Pulsar Competitors

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 19% of the time

Amazon Kinesis vs Apache Spark Streaming

Compared 16% of the time

Azure Stream Analytics vs Apache Spark Streaming

Compared 15% of the time

Starburst Enterprise vs Apache Spark Streaming

Compared 9% of the time

Apache Flink vs Apache Spark Streaming

Compared 8% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Streaming Analytics

January 2025

Download Apache Pulsar product report

Buyer's Guide

Streaming Analytics

January 2025

Download Apache Spark Streaming product report

Also Known As

No data available

Spark Streaming

Overview

Apache Pulsar is a cloud-native, distributed messaging and streaming platform originally created at Yahoo! and now a top-level Apache Software Foundation project

Pulsar

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

Apache

Sample Customers

Information Not Available

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Microsoft and others in Streaming Analytics. Updated: January 2025.

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