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 #10 with an average rating of 8.1. Aiven holds a 1.4% mindshare in SA, compared to Apache’s 3.6% mindshare. Additionally, 100% of Aiven users are willing to recommend the solution, compared to 91% of Apache users who would recommend it.

Aiven Platform

Read 1 Aiven Platform review

1,290 Views
578 Comparison Views

100% willing to recommend

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

2,422 Views
1,751 Comparison Views

91% willing to recommend

Aiven Platform

Apache Spark Streaming

Comparison Buyer's Guide

Download the report

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: December 2024).

Buyer's Guide

Streaming Analytics

December 2024

Download the complete report

Helped 824,053 peers since 2012

Categories and Ranking

Aiven Platform

Ranking in Streaming Analytics

13th

Average Rating

8.0

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

10th

Average Rating

8.0

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of December 2024, in the Streaming Analytics category, the mindshare of Aiven Platform is 1.4%, up from 1.3% compared to the previous year. The mindshare of Apache Spark Streaming is 3.6%, down from 4.6% 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

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.

Read full review

Oscar Estorach

Chief Data-strategist and Director at Theworkshop.es

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.

Read full review

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's capabilities for machine learning are quite extensive and can be used in a low-code way."

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

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

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

"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."

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."

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

"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."

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

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

More Apache Spark Streaming cons

Pricing and Cost Advice

Information not available

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

"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."

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

824,053 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

21%

Financial Services Firm

14%

Real Estate/Law Firm

Retailer

Financial Services Firm

24%

Computer Software Company

20%

Manufacturing Company

University

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...

See all answers

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.

See all answers

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.

See all answers

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?

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...

See all answers

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.

See all answers

Comparisons

Confluent vs Aiven Platform

Compared 51% of the time

Amazon MSK vs Aiven Platform

Compared 27% of the time

Talend Data Streams vs Aiven Platform

Compared 11% of the time

Instaclustr vs Aiven Platform

Compared 11% of the time

More Aiven Platform Competitors

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 20% of the time

Amazon Kinesis vs Apache Spark Streaming

Compared 18% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 17% of the time

Azure Stream Analytics vs Apache Spark Streaming

Compared 15% of the time

Starburst Enterprise vs Apache Spark Streaming

Compared 7% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Streaming Analytics

December 2024

Download Aiven Platform product report

Buyer's Guide

Streaming Analytics

December 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: December 2024.

DOWNLOAD NOW

824,053 professionals have used our research since 2012.

See our list of best Streaming Analytics vendors.

We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.