Apache Spark vs Azure Stream Analytics comparison

The compared Apache and Microsoft solutions aren't in the same category. Apache is ranked #1 in H , with an average rating of 8.7, and holds a 18.0% mindshare in the category. Microsoft is ranked #4 in SA , with an average rating of 8.1, and holds a 12.5% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 91% of Microsoft users who would recommend it.

Apache Spark

Read 64 Apache Spark reviews

1,840 Views
1,421 Comparison Views

90% willing to recommend

Azure Stream Analytics

Read 24 Azure Stream Analytics reviews

6,698 Views
5,740 Comparison Views

91% willing to recommend

Apache Spark

Azure Stream Analytics

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Executive SummaryUpdated on Oct 8, 2024

Apache Spark and Azure Stream Analytics compete in the big data and real-time analytics category. Azure Stream Analytics seems to have the upper hand due to its superior integration capabilities and ease of use, supported by its quick deployment and efficient customer service.

Features: Apache Spark offers excellent scalability, diverse language support, and the ability to handle complex analytics tasks. Azure Stream Analytics provides seamless integration with Azure services, straightforward real-time analytics, and an effective deployment process for Azure users.

Room for Improvement: Apache Spark users mention the need for easier configuration management, better debugging tools, and improved user experience. Azure Stream Analytics could benefit from enhanced documentation, improved feature functionality, and richer feature sets to further streamline the user experience.

Ease of Deployment and Customer Service: Apache Spark's deployment process can be complex but customizable, requiring expertise. Azure Stream Analytics is favored for its quick and easy deployment, with users reporting immediate customer support options.

Pricing and ROI: Apache Spark offers flexible pricing options that are attractive for large-scale deployments, highlighting its cost-effectiveness. Azure Stream Analytics may involve higher initial costs but offers faster time-to-value within integrated Azure solutions, making it appealing for those seeking seamless integration with other Azure tools.

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December 2024

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Categories and Ranking

Apache Spark

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (4th), Java Frameworks (2nd)

Azure Stream Analytics

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Streaming Analytics (4th)

Mindshare comparison

Apache Spark and Azure Stream Analytics aren’t in the same category and serve different purposes. Apache Spark is designed for Hadoop and holds a mindshare of 18.0%, down 21.8% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 12.5% mindshare, down 13.6% since last year.

Hadoop

Streaming Analytics

Featured Reviews

SurjitChoudhury

Data engineer at Cocos pt

Offers batch processing of data and in-memory processing in Spark greatly enhances performance

Spark supports real-time data processing through Spark Streaming. It allows for batch processing of data. If you have immediate data, like chat information, that needs to be processed in real-time, Spark Streaming is used. For data that can be evaluated later, batch processing with Apache Spark is suitable. Mostly, batch processing is utilized in our organization, but for streaming data processing, tools like Kafka are often integrated. In-memory processing in Spark greatly enhances performance, making it a hundred times faster than the previous MapReduce methods. This improvement is achieved through optimization techniques like caching, broadcasting, and partitioning, which help in optimizing queries for faster processing.

Read full review

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.

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

"I like Apache Spark's flexibility the most. Before, we had one server that would choke up. With the solution, we can easily add more nodes when needed. The machine learning models are also really helpful. We use them to predict energy theft and find infrastructure problems."

"I found the solution stable. We haven't had any problems with it."

"The scalability has been the most valuable aspect of the solution."

"Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark."

"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."

"We use Spark to process data from different data sources."

"Apache Spark can do large volume interactive data analysis."

"The most valuable feature of Apache Spark is its ease of use."

More Apache Spark pros

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

"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."

"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."

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

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

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

"The solution's technical support is good."

More Azure Stream Analytics pros

Cons

"The migration of data between different versions could be improved."

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."

"The setup I worked on was really complex."

"The solution needs to optimize shuffling between workers."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"It's not easy to install."

"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data."

More Apache Spark cons

"The solution's interface could be simpler to understand for non-technical people."

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

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

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

"I would like to have a contact individual at Microsoft."

"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."

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

"Azure Stream Analytics is challenging to customize because it's not very flexible."

More Azure Stream Analytics cons

Pricing and Cost Advice

"Apache Spark is an open-source tool."

"It is an open-source solution, it is free of charge."

"Spark is an open-source solution, so there are no licensing costs."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

"We are using the free version of the solution."

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

"The solution is affordable and there are no additional licensing costs."

More Apache Spark pricing and cost advice

"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."

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

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

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

"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."

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

"Azure Stream Analytics is a little bit expensive."

"The current price is substantial."

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Retailer

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?

We use Spark to process data from different data sources.

See all answers

What is your experience regarding pricing and costs for Apache Spark?

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...

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?

Stream Analytics is cheaper, especially for small-scale requirements or telemetry needs. However, for enterprise-level data handling, structured streaming with Databricks might be more cost-effecti...

See all answers

What needs improvement with Azure Stream Analytics?

More flexibility in terms of writing queries and accommodating additional facilities would be beneficial. The complexity of handling messages that need decoding and contain different characters sho...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 29% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 11% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 8% of the time

AWS Lambda vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

Databricks vs Azure Stream Analytics

Compared 27% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 22% of the time

Amazon MSK vs Azure Stream Analytics

Compared 20% of the time

Apache Flink vs Azure Stream Analytics

Compared 13% of the time

Confluent vs Azure Stream Analytics

Compared 4% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache Spark

December 2024

Download Apache Spark product report

Buyer's Guide

Azure Stream Analytics

December 2024

Download Azure Stream Analytics product report

Also Known As

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Apache

Microsoft

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

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

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

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