Apache Spark Streaming vs Azure Stream Analytics comparison

Apache and Microsoft are both solutions in the Streaming Analytics category. Apache is ranked #9 with an average rating of 8.0, while Microsoft is ranked #4 with an average rating of 8.5. Apache holds a 3.7% mindshare in SA, compared to Microsoft’s 12.6% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 91% of Microsoft users who would recommend it.

Apache Spark Streaming

Read 10 Apache Spark Streaming reviews

2,712 Views
1,973 Comparison Views

90% willing to recommend

Azure Stream Analytics

Read 24 Azure Stream Analytics reviews

7,257 Views
6,175 Comparison Views

91% willing to recommend

Apache Spark Streaming

Azure Stream Analytics

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Apache Spark Streaming and Azure Stream Analytics 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 Apache Spark Streaming vs. Azure Stream Analytics Report (Updated: October 2024).

Buyer's Guide

Apache Spark Streaming vs. Azure Stream Analytics

October 2024

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Categories and Ranking

Apache Spark Streaming

Ranking in Streaming Analytics

9th

Average Rating

8.0

Reviews Sentiment

6.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Azure Stream Analytics

Ranking in Streaming Analytics

4th

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 Apache Spark Streaming is 3.7%, down from 4.7% compared to the previous year. The mindshare of Azure Stream Analytics is 12.6%, down from 13.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

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.

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Sudhendra Umarji

Technical Architect at Infosys

Easy to set up and user-friendly, but could be priced better

I haven't come across missing items. It does what I need it to do. The pricing is a little bit high. The UI should be a little bit better from a usability perspective. The endpoint, if you are outsourcing to a third party, should have easier APIs. I'd like to have more destination sources available to us.

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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

"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."

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

"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."

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

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

More Apache Spark Streaming pros

"It's scalable as a cloud product."

"It provides the capability to streamline multiple output components."

"Technical support is pretty helpful."

"The life cycle, report management and crash management features are great."

"We use Azure Stream Analytics for simulation and internal activities."

"It's a product that can scale."

"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."

"I like the way the UI looks, and the real-time analytics service is aligned to this. That can be helpful if I have to use this on a production service."

More Azure Stream Analytics pros

Cons

"The debugging aspect could use some improvement."

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

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

"The solution itself could be easier to use."

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

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

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

"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."

More Apache Spark Streaming cons

"The UI should be a little bit better from a usability perspective."

"The only challenge was that the streaming analytics area in Azure Stream Analytics could not meet our company's expectations, making it a component where improvements are required."

"Easier scalability and more detailed job monitoring features would be helpful."

"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."

"If something goes wrong, it's very hard to investigate what caused it and why."

"One area that could use improvement is the handling of data validation. Currently, there is a review process, but sometimes the validation fails even before the job is executed. This results in wasted time as we have to rerun the job to identify the failure."

"Early in the process, we had some issues with stability."

"More flexibility in terms of writing queries and accommodating additional facilities would be beneficial."

More Azure Stream Analytics cons

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."

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

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

"Azure Stream Analytics is a little bit expensive."

"There are different tiers based on retention policies. There are four tiers. The pricing varies based on steaming units and tiers. The standard pricing is $10/hour."

"The product's price is at par with the other solutions provided by the other cloud service providers in the market."

"The licensing for this product is payable on a 'pay as you go' basis. This means that the cost is only based on data volume, and the frequency that the solution is used."

"The current price is substantial."

"The cost of this solution is less than competitors such as Amazon or Google Cloud."

"I rate the price of Azure Stream Analytics a four out of five."

"When scaling up, the pricing for Azure Stream Analytics can get relatively high. Considering its capabilities compared to other solutions, I would rate it a seven out of ten for cost. However, we've found ways to optimize costs using tools like Databricks for specific tasks."

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Computer Software Company

20%

University

Manufacturing Company

Computer Software Company

15%

Financial Services Firm

14%

Manufacturing Company

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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.

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

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

See all answers

What is your experience regarding pricing and costs for Azure Stream Analytics?

When scaling up, the pricing for Azure Stream Analytics can get relatively high. Considering its capabilities compared to other solutions, I would rate it a seven out of ten for cost. However, we'v...

See all answers

What needs improvement with Azure Stream Analytics?

Azure Stream Analytics is challenging to customize because it's not very flexible. It's good for quickly setting up and implementing solutions, but for building complex data pipelines and engineeri...

See all answers

Comparisons

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 20% of the time

Amazon Kinesis vs Apache Spark Streaming

Compared 19% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 16% of the time

Apache Flink vs Apache Spark Streaming

Compared 7% of the time

SAS Event Stream Processing vs Apache Spark Streaming

Compared 6% of the time

More Apache Spark Streaming Competitors

Databricks vs Azure Stream Analytics

Compared 27% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 23% of the time

Amazon MSK vs Azure Stream Analytics

Compared 19% of the time

Apache Flink vs Azure Stream Analytics

Compared 12% of the time

Google Cloud Dataflow vs Azure Stream Analytics

Compared 3% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Streaming Analytics

November 2024

Download Apache Spark Streaming product report

Buyer's Guide

Azure Stream Analytics

November 2024

Download Azure Stream Analytics product report

Also Known As

Spark Streaming

ASA

Learn More

Apache

Microsoft

Overview

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

Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.

Azure Stream Analytics has the ability to analyze and accurately process exorbitant volumes of high-speed streaming data from numerous sources at the same time. Patterns and scenarios are quickly identified and information is gathered from various input sources, such as social media feeds, applications, clickstreams, sensors, and devices. These patterns can then be implemented to trigger actions and launch workflows, such as feeding data to a reporting tool, storing data for later use, or creating alerts. Azure Stream Analytics is also offered on Azure IoT Edge runtime, so the data can be processed on IoT devices.

Top Benefits

User friendly: Azure Stream Analytics is very straightforward and easy to use. Out of the box and with a few clicks, users are able to connect to numerous sources and sinks, and easily develop an end-to-end pipeline. Stream Analytics can easily connect to Azure IoT Hub and Azure Event Hub for streaming ingestion, in addition to connecting with Azure Blob storage for historical data ingestion.
Flexible deployment: For low-latency analytics, Azure Stream Analytics can run on Azure Stack or IoT edge. For large-scale analytics, the solution can run in the cloud. Azure Stream Analytics uses the same query language and tools for both the cloud and the edge, facilitating an easier process for developers to design exceptional hybrid architectures for streaming processes.
Cost-effective: With Azure Stream Analytics, users only pay for the streaming units they consume; there are no upfront costs. Users can easily scale up or down as needed; there is no commitment or cluster provisioning.
Trustworthy: Azure Stream Analytics guarantees event processing to be 99.99% available with a minute level of granularity. Azure Stream Analytics has embedded recovery capabilities and checkpoints to keep things running smoothly at all times. Events are never lost with Azure Stream Analytics at-least once delivery of events and exactly one event processing.

Reviews from Real Users

“Azure Stream Analytics is something that you can use to test out streaming scenarios very quickly in the general sense and it is useful for IoT scenarios. If I was to do a project with IoT and I needed a streaming solution, Azure Stream Analytics would be a top choice. The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.” - Olubisi A., Team Lead at a tech services company.

“It's used primarily for data and mining - everything from the telemetry data side of things. It's great for streaming and makes everything easy to handle. The streaming from the IoT hub and the messaging are aspects I like a lot.” - Sudhendra U., Technical Architect at Infosys

Sample Customers

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

Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks

Buyer's Guide

Apache Spark Streaming vs. Azure Stream Analytics

October 2024

Free Report: Apache Spark Streaming vs. Azure Stream Analytics

Find out what your peers are saying about Apache Spark Streaming vs. Azure Stream Analytics and other solutions. Updated: October 2024.

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