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 18.2% mindshare in the category. Microsoft is ranked #4 in SA , with an average rating of 8.5, and holds a 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

Read 64 Apache Spark reviews

1,975 Views
1,544 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

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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November 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

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 18.2%, down 21.9% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 12.6% mindshare, down 13.5% 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

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"The tool's most valuable feature is its speed and efficiency. It's much faster than other tools and excels in parallel data processing. Unlike tools like Python or JavaScript, which may struggle with parallel processing, it allows us to handle large volumes of data with more power easily."

"Provides a lot of good documentation compared to other solutions."

"The solution is very stable."

"Now, when we're tackling sentiment analysis using NLP technologies, we deal with unstructured data—customer chats, feedback on promotions or demos, and even media like images, audio, and video files. For processing such data, we rely on PySpark. Beneath the surface, Spark functions as a compute engine with in-memory processing capabilities, enhancing performance through features like broadcasting and caching. It's become a crucial tool, widely adopted by 90% of companies for a decade or more."

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

"The product's deployment phase is easy."

"I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library."

More Apache Spark pros

"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."

"Technical support is pretty helpful."

"The way it organizes data into tables and dashboards is very helpful."

"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."

"Provides deep integration with other Azure resources."

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

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

"It's a product that can scale."

More Azure Stream Analytics pros

Cons

"There could be enhancements in optimization techniques, as there are some limitations in this area that could be addressed to further refine Spark's performance."

"It should support more programming languages."

"Apache Spark could potentially improve in terms of user-friendliness, particularly for individuals with a SQL background. While it's suitable for those with programming knowledge, making it more accessible to those without extensive programming skills could be beneficial."

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

"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."

"From my perspective, the only thing that needs improvement is the interface, as it was not easily understandable."

"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

"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."

More Apache Spark cons

"The collection and analysis of historical data could be better."

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

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

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

"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."

"The initial setup is complex."

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

More Azure Stream Analytics cons

Pricing and Cost Advice

"It is an open-source platform. We do not pay for its subscription."

"They provide an open-source license for the on-premise version."

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

"The product is expensive, considering the setup."

"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

"Apache Spark is an open-source solution, and there is no cost involved in deploying the solution on-premises."

"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."

"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."

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."

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

"The current price is substantial."

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

"Azure Stream Analytics is a little bit expensive."

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

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

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

University

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?

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 Boot vs Apache Spark

Compared 30% of the time

SAP HANA vs Apache Spark

Compared 11% 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 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

Confluent vs Azure Stream Analytics

Compared 3% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache Spark

November 2024

Download Apache Spark product report

Buyer's Guide

Azure Stream Analytics

November 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

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: November 2024.

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