Apache Pulsar vs Apache Spark Streaming comparison

Pulsar and Apache are both solutions in the Streaming Analytics category. Pulsar is ranked #17, while Apache is ranked #10 with an average rating of 8.1. Pulsar holds a 2.0% mindshare in SA, compared to Apache’s 3.6% 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

1,324 Views
857 Comparison Views

100% willing to recommend

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

2,422 Views
1,751 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), Confluent and others in Streaming Analytics.

To learn more, read our detailed Streaming Analytics Report (Updated: December 2024).

Buyer's Guide

Streaming Analytics

December 2024

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Categories and Ranking

Apache Pulsar

Ranking in Streaming Analytics

17th

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 December 2024, in the Streaming Analytics category, the mindshare of Apache Pulsar is 2.0%, up from 2.0% compared to the previous year. The mindshare of Apache Spark Streaming is 3.6%, down from 4.6% 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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Oscar Estorach

Chief Data-strategist and Director at Theworkshop.es

Versatile and flexible when dealing with large-scale data streams

What I like about Spark is its versatility in supporting multiple languages and that makes it my preferred choice for building scalable and efficient systems, whether it is hooking databases with web applications or handling large-scale data transformations. Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows. It works well in the cloud, and you can structure data using Databricks or Spark, providing flexibility for different projects. Spark Streaming's flexibility shines when dealing with large-scale data streams. It caters to different needs, offering real-time insights for tasks like online sales analytics. The ability to prioritize data streams is valuable, especially for monitoring competitor prices online.

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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."

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

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

"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."

"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."

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

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

More Apache Spark Streaming pros

Cons

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

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

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

"We don't have enough experience to be judgmental about its flaws."

"It was resource-intensive, even for small-scale applications."

"The initial setup is quite complex."

"The debugging aspect could use some improvement."

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

More Apache Spark Streaming cons

Pricing and Cost Advice

Information not available

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

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

"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."

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Top Industries

By visitors reading reviews

Computer Software Company

22%

Manufacturing Company

Financial Services Firm

Government

Financial Services Firm

24%

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.

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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 30% of the time

Amazon MSK vs Apache Pulsar

Compared 13% of the time

Amazon Kinesis vs Apache Pulsar

Compared 10% of the time

Azure Stream Analytics vs Apache Pulsar

Compared 7% of the time

Google Cloud Dataflow vs Apache Pulsar

Compared 6% of the time

More Apache Pulsar Competitors

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 20% of the time

Amazon Kinesis vs Apache Spark Streaming

Compared 18% of the time

Azure Stream Analytics vs Apache Spark Streaming

Compared 15% of the time

Starburst Enterprise vs Apache Spark Streaming

Compared 7% of the time

Confluent vs Apache Spark Streaming

Compared 6% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Streaming Analytics

December 2024

Download Apache Pulsar product report

Buyer's Guide

Streaming Analytics

December 2024

Download Apache Spark Streaming product report

Also Known As

No data available

Spark Streaming

Learn More

Video not available

Pulsar

Apache

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

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

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), Confluent and others in Streaming Analytics. Updated: December 2024.

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