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Apache Pulsar vs Apache Spark Streaming comparison

 

Comparison Buyer's Guide

Executive Summary

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
18th
Average Rating
8.0
Reviews Sentiment
6.2
Number of Reviews
1
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
11
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Apache Pulsar is 2.0%, up from 1.8% compared to the previous year. The mindshare of Apache Spark Streaming is 2.7%, down from 3.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

it_user1087029 - PeerSpot reviewer
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.
AbhishekGupta - PeerSpot reviewer
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.

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 has features like checkpointing and Streaming API that are useful."
"As an open-source solution, using it is basically free."
"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"The solution is very stable and reliable."
"The solution is better than average and some of the valuable features include efficiency and stability."
"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."
"It's the fastest solution on the market with low latency data on data transformations."
 

Cons

"Documentation is poor because much of it is in Chinese with no English translation."
"We don't have enough experience to be judgmental about its flaws."
"We would like to have the ability to do arbitrary stateful functions in Python."
"The debugging aspect could use some improvement."
"Integrating event-level streaming capabilities could be beneficial."
"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."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"The solution itself could be easier to use."
"It was resource-intensive, even for small-scale applications."
 

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."
"Spark is an affordable solution, especially considering its open-source nature."
"I was using the open-source community version, which was self-hosted."
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Top Industries

By visitors reading reviews
Computer Software Company
21%
Financial Services Firm
8%
Manufacturing Company
8%
Government
7%
Financial Services Firm
28%
Computer Software Company
19%
Manufacturing Company
6%
University
5%
 

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.
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...
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.
 

Also Known As

No data available
Spark Streaming
 

Overview

 

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: March 2025.
848,207 professionals have used our research since 2012.