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Apache Spark Streaming vs SAS Event Stream Processing 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 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
SAS Event Stream Processing
Ranking in Streaming Analytics
26th
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of June 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 2.7%, down from 3.8% compared to the previous year. The mindshare of SAS Event Stream Processing is 0.6%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Oscar Estorach - PeerSpot reviewer
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.
Roi Jason Buela - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The solution is very stable and reliable."
"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."
"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."
"It's the fastest solution on the market with low latency data on 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."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"Spark Streaming is critical, quite stable, full-featured, and scalable."
"The solution is beneficial on an enterprise level."
 

Cons

"We would like to have the ability to do arbitrary stateful functions in Python."
"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 debugging aspect could use some improvement."
"We don't have enough experience to be judgmental about its flaws."
"The initial setup is quite complex."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"It was resource-intensive, even for small-scale applications."
"The persistence could be better."
 

Pricing and Cost Advice

"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."
"I was using the open-source community version, which was self-hosted."
"Spark is an affordable solution, especially considering its open-source nature."
"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
21%
Manufacturing Company
6%
University
5%
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.
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.
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Also Known As

Spark Streaming
No data available
 

Overview

 

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), Microsoft and others in Streaming Analytics. Updated: June 2025.
856,873 professionals have used our research since 2012.