Apache Spark Streaming vs Azure Stream Analytics comparison

Apache Spark Streaming

Read 10 Apache Spark Streaming reviews

3,856 Views
3,060 Comparison Views

90% willing to recommend

Azure Stream Analytics

Read 23 Azure Stream Analytics reviews

9,387 Views
7,970 Comparison Views

95% 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: May 2024).

Buyer's Guide

Apache Spark Streaming vs. Azure Stream Analytics

May 2024

Download the complete report

Helped 787,104 peers since 2012

Categories and Ranking

Apache Spark Streaming

Ranking in Streaming Analytics

8th

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Azure Stream Analytics

Ranking in Streaming Analytics

3rd

Average Rating

8.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Market share comparison

As of June 2024, in the Streaming Analytics category, the market share of Apache Spark Streaming is 3.5% and it decreased by 15.0% compared to the previous year. The market share of Azure Stream Analytics is 11.9% and it decreased by 9.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Unique Categories:

No other categories found

Featured Reviews

RajeevKumar10

DevOps engineer at Vvolve management consultants

Jun 3, 2024

Handles large datasets and is relatively easy to manage, especially with cloud technologies but scalability features could be enhanced

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 and easy. In my ETL work, I often move data from multiple sources into a data lake. Apache Spark is…

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Sachin Shukre

Sr Manager at a transportation company with 10,001+ employees

Dec 6, 2023

Helpful data visualization capabilities but lacks detailed job monitoring features

We use it to stream data from IT devices and process it. We use almost all Azure services, right from Azure AD, Event Hub, Cosmos DB, Azure Stream Analytics, Azure monitoring services, Azure ML Studio, and everything. The way it organizes data into tables and dashboards is very helpful, along…

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

"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."

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

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

"The solution is very stable and reliable."

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

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

More Apache Spark Streaming pros

"It's easy to implement and maintain pipelines with minimal complexity."

"The solution's most valuable feature is its ability to create a query using SQ."

"We find the query editor feature of this solution extremely valuable for our business."

"The most valuable features are the IoT hub and the Blob storage."

"I like the IoT part. We have mostly used Azure Stream Analytics services for it"

"It's a product that can scale."

"Technical support is pretty helpful."

"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."

More Azure Stream Analytics pros

Cons

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

"The initial setup is quite complex."

"The debugging aspect could use some improvement."

"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 solution itself could be easier to use."

"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."

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

More Apache Spark Streaming cons

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

"Its features for event imports and architecture could be enhanced."

"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."

"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."

"The solution doesn't handle large data packets very efficiently, which could be improved upon."

"There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting."

"Azure Stream Analytics could improve by having clearer metrics as to the scale, more metrics around the data set size that is flowing through it, and performance tuning recommendations."

"The initial setup is complex."

More Azure Stream Analytics cons

Pricing and Cost Advice

"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."

"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."

"Azure Stream Analytics is a little bit expensive."

"We pay approximately $500,000 a year. It's approximately $10,000 a year per license."

"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 current price is substantial."

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

21%

Computer Software Company

20%

Manufacturing Company

Retailer

Computer Software Company

15%

Financial Services Firm

12%

Manufacturing Company

Retailer

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?

In terms of improvement, the UI could be better. Additionally, Spark Streaming works well for various use cases, but improvements could be made for ultra-fast scenarios where seconds matter. While ...

See all answers

What is your primary use case for Apache Spark Streaming?

As a data engineer, I use Apache Spark Streaming to process real-time data for web page analytics and integrate diverse data sources into centralized data warehouses.

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?

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

See all answers

What needs improvement with Azure Stream Analytics?

Azure Stream Analytics was not meeting our company's expectations because it was tedious to change the job, write queries, or if I needed to change something, I needed to stop the entire stream pro...

See all answers

Comparisons

Amazon Kinesis vs Apache Spark Streaming

Compared 21% of the time

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 19% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 10% of the time

Confluent vs Apache Spark Streaming

Compared 8% of the time

Starburst Enterprise vs Apache Spark Streaming

Compared 7% of the time

More Apache Spark Streaming Competitors

Amazon Kinesis vs Azure Stream Analytics

Compared 30% of the time

Databricks vs Azure Stream Analytics

Compared 24% of the time

Amazon MSK vs Azure Stream Analytics

Compared 14% of the time

Apache Flink vs Azure Stream Analytics

Compared 10% of the time

Confluent vs Azure Stream Analytics

Compared 4% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Streaming Analytics

May 2024

Download Apache Spark Streaming product report

Buyer's Guide

Azure Stream Analytics

June 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

May 2024

Free Report: Apache Spark Streaming vs. Azure Stream Analytics

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