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Apache Spark Streaming vs Azure Stream Analytics comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Apache Spark Streaming
Ranking in Streaming Analytics
9th
Average Rating
8.0
Number of Reviews
10
Ranking in other categories
No ranking in other categories
Azure Stream Analytics
Ranking in Streaming Analytics
4th
Average Rating
8.2
Number of Reviews
23
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 - PeerSpot reviewer
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.
Sudhendra Umarji - PeerSpot reviewer
May 29, 2022
Easy to set up and user-friendly, but could be priced better
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. It is easy to set up. The solution is stable and reliable.  It's a…

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"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'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."
"It's the fastest solution on the market with low latency data on data transformations."
"The solution is very stable and reliable."
"As an open-source solution, using it is basically free."
"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 easy to implement and maintain pipelines with minimal complexity."
"I appreciate this solution because it leverages open-source technologies. It allows us to utilize the latest streaming solutions and it's easy to develop."
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."
"Technical support is pretty helpful."
"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."
"Provides deep integration with other Azure resources."
"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."
"The way it organizes data into tables and dashboards is very helpful."
 

Cons

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"We would like to have the ability to do arbitrary stateful functions in Python."
"In terms of improvement, the UI could be better."
"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."
"Integrating event-level streaming capabilities could be beneficial."
"The UI should be a little bit better from a usability perspective."
"The solution’s customer support could be improved."
"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."
"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."
"Early in the process, we had some issues with stability."
"The solution could be improved by providing better graphics and including support for UI and UX testing."
"I would like to have a contact individual at Microsoft."
"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."
 

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."
"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 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."
"Azure Stream Analytics is a little bit expensive."
"I rate the price of Azure Stream Analytics a four out of five."
"We pay approximately $500,000 a year. It's approximately $10,000 a year per license."
"The cost of this solution is less than competitors such as Amazon or Google Cloud."
"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."
"The product's price is at par with the other solutions provided by the other cloud service providers in the market."
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Top Industries

By visitors reading reviews
Financial Services Firm
23%
Computer Software Company
20%
University
6%
Manufacturing Company
6%
Computer Software Company
16%
Financial Services Firm
14%
Manufacturing Company
8%
Insurance Company
5%
 

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.
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...
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...
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...
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...
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...
 

Also Known As

Spark Streaming
ASA
 

Learn More

 

Overview

 

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
Find out what your peers are saying about Apache Spark Streaming vs. Azure Stream Analytics and other solutions. Updated: October 2024.
814,649 professionals have used our research since 2012.