Apache Spark Streaming vs Redpanda comparison

Why PeerSpot?

Apache and Redpanda Data are both solutions in the Streaming Analytics category. Apache is ranked #7 with an average rating of 7.9, while Redpanda Data is ranked #5 with an average rating of 8.5. Apache holds a 4.6% mindshare in SA, compared to Redpanda Data’s 2.1% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 100% of Redpanda Data users who would recommend it.

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

3,138 Views
3,138 Comparison Views

94% willing to recommend

Redpanda

Read 14 Redpanda reviews

1,839 Views
1,821 Comparison Views

100% willing to recommend

Apache Spark Streaming

Redpanda

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Executive SummaryUpdated on Dec 17, 2024

Apache Spark Streaming and Redpanda are competing products in the scalable data streaming solutions category. Apache Spark Streaming seems to have the upper hand due to its integration capabilities with the Apache ecosystem, while Redpanda captures attention with its modern streaming engine and efficiency.

Features: Apache Spark Streaming provides integration with Apache tools like Hadoop, utilizes in-memory processing, and supports distributed data processing tasks. Redpanda emphasizes low latency, high throughput, and offers Kafka API compatibility for faster deployment.

Room for Improvement: Apache Spark Streaming could benefit from simpler setup processes, improved real-time processing capabilities, and enhanced support options. Redpanda might enhance its toolset compatibility, expand ecosystem integration, and offer a clearer pricing structure.

Ease of Deployment and Customer Service: Redpanda's single-binary node architecture simplifies deployment and reduces maintenance, with customer service praised for responsiveness. Apache Spark Streaming involves more complex setups, requiring customization, and relies on community support, which may be slower.

Pricing and ROI: Apache Spark Streaming, as an open-source solution, has lower initial costs but higher operational costs due to infrastructure needs. Redpanda's higher initial investment is offset by faster ROI through efficiency and reduced operational burden.

To learn more, read our detailed Apache Spark Streaming vs. Redpanda Report (Updated: September 2026).

Buyer's Guide

Apache Spark Streaming vs. Redpanda

September 2026

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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

7th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Redpanda

Ranking in Streaming Analytics

5th

Average Rating

8.6

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2026, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 4.6%, up from 3.6% compared to the previous year. The mindshare of Redpanda is 2.1%, up from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Redpanda	2.1%
Apache Spark Streaming	4.6%
Other	93.3%

Streaming Analytics

Featured Reviews

Khoa Dang Le

Principal AI Engineer at IMT Solutions

Have faced challenges with complex data handling and seek smoother integration for machine learning workflows

I find the fault tolerance feature beneficial because I use it for serving data from a landing area. I understand all of the structures we have for Spark SQL, Spark Streaming, and MLlib. The ability of Apache Spark Streaming to handle out-of-order data using watermarking and windowing is something we use in our pipeline. Nearly 50% of our usage is based on that because we use it for landing data, and we appreciate that we can work with it. The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage. The fast storage capability is crucial because Apache Spark replaces Hadoop's MapReduce, allowing us to manage our data more efficiently.

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ArpitShah

Software Analyst at CLSA

Event streaming has simplified video data cleanup and now powers real-time analytics

One area for improvement is providing more examples. For instance, Redpanda could be more useful as a sink where you get the data and can directly push to S3. While this is possible through the API, there are better and faster ways to do it. You can make a million API calls and accomplish the task in one and a half hours, but the same thing can be done in ten minutes through other methods. These faster approaches are not documented in obvious places. You have to find information scattered across various blogs. Redpanda should collect all the good blogs and best practices and put them in their documentation. This is more about knowledge management and making it easy for users to understand the product for complex use cases. For simple use cases, it is straightforward. We all use the basic pipe functionality. However, providing more examples would be useful. For example, integration with AWS and the AWS ecosystem would be cool.

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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."

"The solution is better than average and some of the valuable features include efficiency and stability."

"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."

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

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

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."

"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."

More Apache Spark Streaming pros

"The cost savings have been significant."

"I would recommend Redpanda to others because it's easy to set up, consumes less resources, and is stable compared to other tools."

"I have seen positive outcomes including improved efficiency, cost savings, and better performance metrics because it is open-source, fast, and good."

"The performance is superb, and the value we are getting for the money we pay is great."

"Through a lot of benchmarks and whatnot, we found out that on a larger scale, when the latency of the messages is important, not talking about throughput, the latency is important, Redpanda offers a way better latency profile than Apache Kafka."

"Redpanda is developer-friendly, and we need to do much less configuration because Redpanda provides out-of-the-box configuration for us."

"Redpanda has positively impacted my organization by being easy to implement quickly for use cases such as the implementation of MVPs and PoCs, making scalability, tolerance, and availability very interesting and facilitating the provisioning of infrastructure efficiently."

"Redpanda has positively impacted my organization by allowing us to move from a batch approach to a more streaming approach for our jobs, which cuts down on our delivery time and allows us to better meet our SLAs for our clients."

More Redpanda pros

Cons

"Integrating event-level streaming capabilities could be beneficial."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

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

"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 downside is when you have this the other way around in the columns, it becomes really hard to use."

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

More Apache Spark Streaming cons

"Redpanda can be improved in several ways, and the more I experiment with the product, the more limitations I find."

"Redpanda needs more visibility and primarily greater access to documentation."

"We had one incident that we concluded was a case of memory leak."

"In Redpanda, the areas that have room for improvement are in the clustering part."

"One area for improvement is providing more examples."

"I think Redpanda is overall very good for us, and I am uncertain whether Redpanda can scale to very large companies as we are a medium-sized startup."

"Recently, for the documentation, they've built their own AI chatbot, which is focused on giving you answers based on their documentation. While using that, I did not find it to be very good."

"I think Redpanda needs to increase the connector options."

More Redpanda cons

Pricing and Cost Advice

"People pay for Apache Spark Streaming as a service."

"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."

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

"It's free. Everybody can use it, only support is paid."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Outsourcing Company

13%

Comms Service Provider

Healthcare Company

Financial Services Firm

18%

Comms Service Provider

12%

Outsourcing Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	1
Large Enterprise	4

Questions from the Community

What needs improvement with Apache Spark Streaming?

One of the improvements we need is in Spark SQL and the machine learning library. I don't think there is too much to work on, but the issue is when we want to use machine learning, we always need t...

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What is your primary use case for Apache Spark Streaming?

We work with Apache Spark Streaming for our project because we use that as one of the landing data sources, and we work with it to ensure we can get all of the data before it goes through our data ...

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What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

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What is your experience regarding pricing and costs for Redpanda?

Regarding my experience with pricing, setup cost, and licensing for Redpanda, I am exploring the product. If I am convinced about the product and the capabilities, and I am sure I will be because I...

See all answers

What needs improvement with Redpanda?

Redpanda can be improved in several ways, and the more I experiment with the product, the more limitations I find. I understand that this is about business, and the product should grow, and they ha...

See all answers

What is your primary use case for Redpanda?

My main use case for Redpanda is primarily for streaming, and I am currently focusing on building data lakehouses because I find them really interesting. Redpanda fits into my data lakehouse setup ...

See all answers

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Product Reports

Buyer's Guide

Apache Spark Streaming

September 2026

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Buyer's Guide

Redpanda

September 2026

Download Redpanda product report

Also Known As

Spark Streaming

No data available

Overview

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

Apache

Redpanda offers a modern, intuitive interface with efficient resource usage, seamlessly integrating with Kafka, and enhancing performance through fast operations and reliable support. Organizations benefit from its memory efficiency and high performance for demanding data workloads.

Built on a C++ foundation, Redpanda integrates easily with Kafka clients and stands out for fast operations, simplified Docker setup, and effective metrics monitoring. Performance is enhanced by memory efficiency and high throughput capabilities. The community provides robust support, and clear documentation aids the adoption process. However, improvements could be made in version control, command-line tools, and documentation, particularly in areas such as automation file management and chatbot documentation assistance. Redpanda is widely utilized in data streaming and normalization, efficiently handling large telemetry data volumes with minimal latency, essential for building asynchronous applications across microservices and monitoring systems.

What are the most important features of Redpanda?

Modern UI: Provides an intuitive interface for ease of use.
Efficient Resource Usage: Built on C++ for performance optimization.
Seamless Integration: Works well with Kafka clients for smooth operations.
Simplified Setup: Docker templates facilitate easy installation.
Built-in Metrics Exporters: Enhance monitoring and visibility.
High Performance: Ensures memory efficiency and quick data processing.

What benefits and ROI can users expect?

Fast Operations: Accelerates data processing for efficient workflows.
Reliable Support: Strong community aids troubleshooting and development.
Clear Documentation: Helps users quickly onboard and manage Redpanda.
Low Latency: Critical for applications requiring quick response times.
Sub-second Response: Supports demanding performance and latency needs.

Redpanda is commonly implemented in tech and software industries to streamline data streaming and normalization processes, handling high telemetry data volumes effectively. Its capacity for sub-second response times makes it crucial for companies developing asynchronous applications, especially in microservices and monitoring systems.

Redpanda Data

Sample Customers

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

Information Not Available

Buyer's Guide

Apache Spark Streaming vs. Redpanda

September 2026

Free Report: Apache Spark Streaming vs. Redpanda

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