Aiven Platform vs Apache Spark Streaming comparison

Aiven and Apache are both solutions in the Streaming Analytics category. Aiven is ranked #13 with an average rating of 8.0, while Apache is ranked #9 with an average rating of 8.0. Aiven holds a 1.4% mindshare in SA, compared to Apache’s 3.7% mindshare. Additionally, 100% of Aiven users are willing to recommend the solution, compared to 90% of Apache users who would recommend it.

Aiven Platform

Read 1 Aiven Platform review

1,329 Views
618 Comparison Views

100% willing to recommend

Apache Spark Streaming

Read 10 Apache Spark Streaming reviews

2,712 Views
1,973 Comparison Views

90% 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 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: October 2024).

Buyer's Guide

Streaming Analytics

October 2024

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Helped 814,763 peers since 2012

Categories and Ranking

Aiven Platform

Ranking in Streaming Analytics

13th

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

9th

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Aiven Platform is 1.4%, down from 1.4% compared to the previous year. The mindshare of Apache Spark Streaming is 3.7%, down from 4.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

reviewer2539719

System Engineer - Cloud Platform at a computer software company with 1,001-5,000 employees

Aug 30, 2024

Faster and easier to use with less maintenance

We haven't had any significant issues so far. Our manager can escalate issues to the solution's engineers even when we have issues. They provide a fast response to our incidents and solve our issues. After solving the problem, the engineers also look into the issue to find the root cause. We've contacted their support before. We submit support tickets and follow up with our account manager. The account manager can then follow up with the engineers, which helps the ticket get processed faster. Their support team has several experts, especially in Apache Kafka. We can always ask them about specific issues. For example, our principal engineer once asked about Kafka offset, and the account manager was able to handle it and provide best practices.

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Oscar Estorach

Chief Data-strategist and Director at Theworkshop.es

Jan 25, 2024

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.

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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

"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."

"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."

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

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

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

"It's the fastest solution on the market with low latency data on data transformations."

"The solution is very stable and reliable."

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."

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

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

"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 cost and load-related optimizations are areas where the tool lacks and needs improvement."

"The solution itself could be easier to use."

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

"The initial setup is quite complex."

"The debugging aspect could use some improvement."

More Apache Spark Streaming cons

Pricing and Cost Advice

Information not available

"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."

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

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

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Top Industries

By visitors reading reviews

Computer Software Company

23%

Financial Services Firm

13%

Manufacturing Company

Educational Organization

Financial Services Firm

23%

Computer Software Company

20%

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?

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 usa...

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

I use Aiven for Apache Kafka to handle services dependent on each other. Instead of API calls, we use it for asynchronous processing of all our services. That's the general way we use it.

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

Apache Kafka is really good for streaming data or messaging services. It's comparable to other messaging services. Overall, I'd rate Aiven for Apache Kafka an eight out of ten.

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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.

See all answers

What needs improvement with Apache Spark Streaming?

The product's event handling capabilities, particularly compared to Kaspersky, need improvement. Integrating event-level streaming capabilities could be beneficial. This aligns with the idea of exp...

See all answers

What is your primary use case for Apache Spark Streaming?

I've used it more for ETL. It's useful for creating data pipelines, streaming datasets, generating synthetic data, synchronizing data, creating data lakes, and loading and unloading data is fast an...

See all answers

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Compared 16% of the time

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Compared 16% of the time

Apache Flink vs Apache Spark Streaming

Compared 7% of the time

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Product Reports

Buyer's Guide

Streaming Analytics

October 2024

Download Aiven Platform product report

Buyer's Guide

Streaming Analytics

October 2024

Download Apache Spark Streaming product report

Also Known As

No data available

Spark Streaming

Learn More

Aiven

Apache

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.

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

Sample Customers

Information Not Available

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

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics. Updated: October 2024.

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