Compare Apache Spark vs. Azure Stream Analytics

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Top Review
Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: August 2021.
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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
"The most valuable feature of this solution is its capacity for processing large amounts of data.""The solution is very stable.""I feel the streaming is its best feature.""The features we find most valuable are the machine learning, data learning, and Spark Analytics.""The main feature that we find valuable is that it is very fast.""The processing time is very much improved over the data warehouse solution that we were using.""The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly.""AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."

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"Provides deep integration with other Azure resources.""The most valuable features are the IoT hub and the Blob storage.""Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time.""I like the IoT part. We have mostly used Azure Stream Analytics services for it""The solution has a lot of functionality that can be pushed out to companies."

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Cons
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.""The solution needs to optimize shuffling between workers.""When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources.""We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.""We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.""I would like to see integration with data science platforms to optimize the processing capability for these tasks.""The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate.""Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

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"If something goes wrong, it's very hard to investigate what caused it and why.""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.""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.""The collection and analysis of historical data could be better.""The solution offers a free trial, however, it is too short."

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Pricing and Cost Advice
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

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

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Questions from the Community
Top Answer: I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
Top Answer: The solution has been very stable.
Top Answer: We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud.
Top Answer: The product does have a free trial offer, however, it is much too short. It's only 14 days and that's not enough time to run a proper POC.
Top Answer: While it depends on the business scenario, in some cases AWS offers better features. It's hard to speak to missing features at it really depends on the business case. However, in general, it has all… more »
Top Answer: The company I'm working for is basically one of the biggest companies in the entire Gulf region, including Dubai, Qatar, and Oman. Our core domain is providing logistics. They have different… more »
Ranking
1st
out of 22 in Hadoop
Views
10,189
Comparisons
8,201
Reviews
11
Average Words per Review
472
Rating
8.6
5th
out of 36 in Streaming Analytics
Views
7,167
Comparisons
6,260
Reviews
5
Average Words per Review
744
Rating
8.2
Comparisons
Also Known As
ASA
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Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

AzureStream Analytics is a fully managed event-processing engine that lets you set up real-time analytic computations on streaming data.The data can come from devices, sensors, web sites, social media feeds, applications, infrastructure systems, and more.
Offer
Learn more about Apache Spark
Learn more about Azure Stream Analytics
Sample Customers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks
Top Industries
REVIEWERS
Financial Services Firm40%
Computer Software Company20%
Marketing Services Firm10%
Non Profit10%
VISITORS READING REVIEWS
Computer Software Company24%
Comms Service Provider19%
Financial Services Firm10%
Media Company10%
VISITORS READING REVIEWS
Computer Software Company29%
Comms Service Provider20%
Energy/Utilities Company7%
Manufacturing Company5%
Company Size
REVIEWERS
Small Business38%
Midsize Enterprise21%
Large Enterprise41%
No Data Available
Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: August 2021.
534,057 professionals have used our research since 2012.

Apache Spark is ranked 1st in Hadoop with 11 reviews while Azure Stream Analytics is ranked 5th in Streaming Analytics with 5 reviews. Apache Spark is rated 8.6, while Azure Stream Analytics is rated 8.2. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, the top reviewer of Azure Stream Analytics writes "A serverless scalable event processing engine with a valuable IoT feature". Apache Spark is most compared with Spring Boot, AWS Batch, AWS Lambda, SAP HANA and Cloudera Distribution for Hadoop, whereas Azure Stream Analytics is most compared with Databricks, Apache Flink, Apache Spark Streaming, Apache NiFi and Google Cloud Dataflow.

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