Aiven Platform vs Apache Spark Streaming comparison

Aiven and Apache are both solutions in the Streaming Analytics category. Aiven is ranked #14 with an average rating of 8.5, while Apache is ranked #11 with an average rating of 8.3. Aiven holds a 1.7% mindshare in SA, compared to Apache’s 3.1% mindshare. Additionally, 100% of Aiven users are willing to recommend the solution, compared to 92% of Apache users who would recommend it.

Aiven Platform

Read 2 Aiven Platform reviews

984 Views
925 Comparison Views

100% willing to recommend

Apache Spark Streaming

Read 12 Apache Spark Streaming reviews

1,349 Views
1,349 Comparison Views

92% willing to recommend

Aiven Platform

Apache Spark Streaming

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Executive Summary

We performed a comparison between Aiven Platform and Apache Spark Streaming based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Aiven Platform vs. Apache Spark Streaming Report (Updated: July 2025).

Buyer's Guide

Aiven Platform vs. Apache Spark Streaming

July 2025

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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

Aiven Platform

Ranking in Streaming Analytics

14th

Average Rating

8.6

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

11th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the Streaming Analytics category, the mindshare of Aiven Platform is 1.7%, up from 1.3% compared to the previous year. The mindshare of Apache Spark Streaming is 3.1%, down from 3.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

njadm

CTO and Co-Founder at PeerSpot

Seamlessly handle database upgrades and minimize downtime disruptions

One of the most valuable features of Aiven Platform is that it handles the upgrades for us seamlessly, saving us time that would be spent on routine upgrades. It also provides reliable backups. The ability to minimize disruption during upgrades is very important since any database downtime would mean system-wide disruptions.

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Venkata Phaneendra Reddy Janga

Data Engineer III at Verizon

Improved data latency and integration with diverse data sources enables robust real-time processing

The best feature of Apache Spark Streaming is that it's built upon the Spark SQL engine. This is easy for someone coming from a SQL background to work with real-time data, even if they are new to real-time processing. They can quickly get started using the Spark SQL engine. Another valuable feature is that we can control many aspects such as the configuration of the engine, memory management, and have a checkpointing mechanism that allows us to manually start or restart jobs from a specific point. This is particularly useful for restoring messages of a Kafka topic from a specific date and time using the checkpointing mechanism. The integration with Spark's ecosystems such as MLlib and GraphX has significant potential, although I have not worked on that part as we focus mainly on data engineering. We can handle late-arriving data with Apache Spark Streaming. Sometimes aggregation results might be missed if data arrives out of order, but features such as windowing allow us to manage out-of-order data by specifying a watermark time. Recently released mechanisms to query the state make it easier to handle data programmatically.

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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

"One of the most valuable features of Aiven Platform is that it handles the upgrades for us seamlessly, saving us time that would be spent on routine upgrades."

"What I like best about the tool is that the process for the services is faster compared to other methods. It's easier to use because Aiven for Apache Kafka handles the maintenance, so we have less to manage. We only use Kafka to manage its connectivity."

"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 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."

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

"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."

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

More Apache Spark Streaming pros

Cons

"One challenge we face is when we want to update the version, for example, from 3.6 to 3.7. It will spawn new nodes, and then there's rebalancing and syncing from other brokers. There's high CPU usage during this process, so the solution can't be used for a while, causing some downtime in our services. To tackle this challenge, we schedule maintenance updates during low-traffic periods when there's less risk and fewer users use the services."

"I would really like to see Aiven Platform add a user interface for database backups, as this would eliminate the need for a third-party solution."

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

"The solution itself could be easier to use."

"The initial setup is quite complex."

"The debugging aspect could use some improvement."

"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."

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

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

19%

Financial Services Firm

17%

Real Estate/Law Firm

Hospitality Company

Computer Software Company

22%

Financial Services Firm

21%

University

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What needs improvement with Aiven for Apache Kafka?

I would really like to see Aiven Platform add a user interface for database backups, as this would eliminate the need for a third-party solution. Additionally, the customer service could be more re...

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What is your primary use case for Aiven for Apache Kafka?

Our primary use case is having our PostgreSQL and MySQL databases hosted by Aiven Platform. They serve as our production databases.

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What advice do you have for others considering Aiven for Apache Kafka?

In our experience, we encountered issues with Aiven Platform's connection to Redis. It was not smooth, and though we like the solution overall, we are hesitant about using Redis integration again. ...

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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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Compared 9% of the time

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Product Reports

Buyer's Guide

Streaming Analytics

August 2025

Download Aiven Platform product report

Buyer's Guide

Streaming Analytics

August 2025

Download Apache Spark Streaming product report

Also Known As

No data available

Spark Streaming

Overview

Aiven for Apache Kafka is a robust data streaming platform utilized for real-time analytics, event-driven architectures, and message brokering, enhancing data processing across systems. It features scalable operations, excellent data replication, and comprehensive monitoring, significantly improving organizational efficiency and decision-making processes through high-level data management capabilities.

Aiven

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

Apache

Sample Customers

Information Not Available

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

Buyer's Guide

Aiven Platform vs. Apache Spark Streaming

July 2025

Free Report: Aiven Platform vs. Apache Spark Streaming

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