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Apache Spark Streaming vs Upsolver 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
Upsolver
Ranking in Streaming Analytics
18th
Average Rating
8.6
Reviews Sentiment
7.6
Number of Reviews
2
Ranking in other categories
Data Integration (38th)
 

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 2.6%, down from 3.7% compared to the previous year. The mindshare of Upsolver is 0.4%, up from 0.2% 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.
Snehasish Das - PeerSpot reviewer
Allows for data to be moved across platforms and different data technologies
The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies. Upsolver does this in a quick time, unlike traditional processes which are time-consuming. Additionally, it offers scalability for large volumes of data, with performance and ease of cloud-native integration.

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 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."
"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'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."
"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."
"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."
"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."
"Customer service is excellent, and I would rate it between eight point five to nine out of ten."
 

Cons

"The initial setup is quite complex."
"In terms of improvement, the UI could be better."
"Integrating event-level streaming capabilities could be beneficial."
"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."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"It was resource-intensive, even for small-scale applications."
"The solution itself could be easier to use."
"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."
"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."
"There is room for improvement in query tuning."
 

Pricing and Cost Advice

"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."
"I was using the open-source community version, which was self-hosted."
"People pay for Apache Spark Streaming as a service."
"Upsolver is affordable at approximately $225 per terabyte per year."
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Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
22%
Manufacturing Company
5%
University
4%
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.
What is your experience regarding pricing and costs for Upsolver?
Upsolver is affordable at approximately $225 per terabyte per year. Compared to what I know from others, it's cheaper than many other products.
What needs improvement with Upsolver?
There is room for improvement in query tuning. Upsolver could do a more in-depth analysis in employing machine power, such as CPU and memory, to enhance query performance. Furthermore, allocating C...
What is your primary use case for Upsolver?
I am working as a consultant and currently have my own consultancy services. I provide services to companies that are data-heavy and looking for data engineering solutions for their business needs....
 

Also Known As

Spark Streaming
No data available
 

Overview

 

Sample Customers

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.
Information Not Available
Find out what your peers are saying about Apache Spark Streaming vs. Upsolver and other solutions. Updated: June 2025.
860,168 professionals have used our research since 2012.