Apache Spark Streaming vs SAS Event Stream Processing comparison

Apache and SAS are both solutions in the Streaming Analytics category. Apache is ranked #10 with an average rating of 8.2, while SAS is ranked #27. Apache holds a 3.4% mindshare in SA, compared to SAS’s 0.5% mindshare. Additionally, 91% of Apache users are willing to recommend the solution, compared to 100% of SAS users who would recommend it.

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

2,162 Views
1,579 Comparison Views

91% willing to recommend

SAS Event Stream Processing

Read 1 SAS Event Stream Processing review

222 Views
135 Comparison Views

100% willing to recommend

Apache Spark Streaming

SAS Event Stream Processing

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Executive Summary

We performed a comparison between Apache Spark Streaming and SAS Event Stream Processing 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: January 2025).

Buyer's Guide

Streaming Analytics

January 2025

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Helped 831,158 peers since 2012

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 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

SAS Event Stream Processing

Ranking in Streaming Analytics

27th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.4%, down from 4.3% compared to the previous year. The mindshare of SAS Event Stream Processing is 0.5%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

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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Roi Jason Buela

Lead Technical Consultant at Thakral One

A solution with useful windowing features and great for operations and marketing

The persistence could be better. Although ESP is designed for in-memory processing, it would be better if the solution is enhanced or improved on the persistence of the data that is kept in the memory. For example, if one server goes down and the information is stored in the memory, it is lost. Therefore, the persistence needs to be improved so that if there are more cases where the server is down, the information and data can still be intact.

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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

"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."

"As an open-source solution, using it is basically free."

"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."

"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 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

"The solution is beneficial on an enterprise level."

Cons

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

"The initial setup is quite complex."

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

"The solution itself could be easier to use."

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

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

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"The debugging aspect could use some improvement."

More Apache Spark Streaming cons

"The persistence could be better."

Pricing and Cost Advice

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

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

"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

Financial Services Firm

26%

Computer Software Company

20%

Manufacturing Company

University

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

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...

See all answers

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.

See all answers

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Compared 17% of the time

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Compared 17% of the time

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Compared 16% of the time

Amazon MSK vs Apache Spark Streaming

Compared 4% of the time

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Apache Flink vs SAS Event Stream Processing

Compared 54% of the time

More SAS Event Stream Processing Competitors

Product Reports

Buyer's Guide

Streaming Analytics

January 2025

Download Apache Spark Streaming product report

Buyer's Guide

Streaming Analytics

January 2025

Download SAS Event Stream Processing product report

Also Known As

Spark Streaming

No data available

Learn More

Apache

Video not available

SAS

Overview

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

Artificial intelligence and machine learning are the most transformative technologies of our time, and SAS is more committed than ever to investing in its potential to move humanity forward.

Sample Customers

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

Honda, HSBC, Lufthansa, Nestle, 89Degrees.

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

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We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.