AWS Lambda vs Apache Spark vs Azure Stream Analytics comparison

The compared Apache and Amazon Web Services (AWS) solutions aren't in the same category. Apache is ranked #1 in H , with an average rating of 8.6, and holds a 17.5% mindshare in the category. Amazon Web Services (AWS) is ranked #1 in CS , with an average rating of 8.4, and holds a 21.0% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 94% of Amazon Web Services (AWS) users who would recommend it.

Apache Spark

Read 66 Apache Spark reviews

1,404 Views
1,090 Comparison Views

90% willing to recommend

AWS Lambda

Read 84 AWS Lambda reviews

4,528 Views
2,369 Comparison Views

94% willing to recommend

Azure Stream Analytics

Read 26 Azure Stream Analytics reviews

4,867 Views
4,124 Comparison Views

92% willing to recommend

Apache Spark

AWS Lambda

Azure Stream Analytics

Comparison Buyer's Guide

Download the report

Executive Summary

We performed a comparison between Apache Spark, AWS Lambda, and Azure Stream Analytics based on real PeerSpot user reviews.

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

To learn more, read our detailed Hadoop Report (Updated: March 2025).

Buyer's Guide

Hadoop

March 2025

Download the complete report

Helped 849,190 peers since 2012

Review summaries and opinions

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

Mindshare comparison

Hadoop

Compute Service

Streaming Analytics

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

Read full review

Wai L Lin O

Cloud Engineer - Supervisor at KBZ Bank

A serverless solution with easy integration features

We use AWS Lambda because it provides a solution for our needs without requiring us to manage our infrastructure. With the tool, we only pay for the resources we use. Additionally, it is straightforward to implement and integrates with other services like API Gateway. The tool's serverless nature has had the most significant impact on our workflow. I find it particularly attractive because it eliminates the need for managing servers. In my previous experience, managing upgrades and updates was quite challenging. The solution's integration process with other AWS services was relatively easy. We primarily use AWS services such as EventBridge for scheduling processes and log management.

Read full review

SantiagoCordero

Director, Governance & Infrastructure & Director at VASS

Native connectors and integration simplify tasks but portfolio complexity needs addressing

There are too many products in the Azure landscape, which sometimes leads to overlap between them. Microsoft continuously releases new products or solutions, which can be frustrating when determining the appropriate features from one solution over another. A cost comparison between products is also not straightforward. They should simplify their portfolio. The Microsoft licensing system is confusing and not easy to understand, and this is something they should address. In the future, I may stop using Stream Analytics and move to other solutions. I discussed Palantir earlier, which is something I want to explore in depth because it allows me to accomplish more efficiently compared to solely using Azure. Additionally, the vendors should make the solution more user-friendly, incorporating low-code and no-code features. This is something I wish to explore further.

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

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

"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."

"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."

"The product’s most valuable feature is the SQL tool. It enables us to create a database and publish it."

"The deployment of the product is easy."

"The features we find most valuable are the machine learning, data learning, and Spark Analytics."

"The most valuable feature of Apache Spark is its flexibility."

"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."

More Apache Spark pros

"The serverless computing feature eliminates the need to manage servers, provision, or scale."

"Lambda allows you to focus on the code itself."

"We have no issues with the technical support."

"The programming language and the integration with other AWS services are the most valuable features."

"We use AWS Lambda because it provides a solution for our needs without requiring us to manage our infrastructure. With the tool, we only pay for the resources we use. Additionally, it is straightforward to implement and integrates with other services like API Gateway."

"AWS Lambda is cost-effective, providing noticeable cost savings."

"The ability to scale up and down very quickly helps because we can maintain our system performance and business at a low cost."

"AWS Lambda's best features are log analysis and event triggering and actioning."

More AWS Lambda pros

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

"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."

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

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

"Cloud tools and cloud services enable flexibility and lower entry barriers for Taiwanese enterprises."

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

More Azure Stream Analytics pros

Cons

"Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors."

"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."

"If you have a Spark session in the background, sometimes it's very hard to kill these sessions because of D allocation."

"At times during the deployment process, the tool goes down, making it look less robust. To take care of the issues in the deployment process, users need to do manual interventions occasionally."

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

"Apache Spark provides very good performance The tuning phase is still tricky."

"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."

More Apache Spark cons

"The feature to attach external storage, such as an S3 or elastic storage, must be added to AWS Lambda. This is its area for improvement."

"Amazon doesn't have enough local support based in our country."

"AWS Lambda has a maximum execution timeout of 15 minutes, which is unsuitable for long-running tasks."

"The execution time could be better. One of the major limitations is the time period because it doesn't give you more than seven minutes. So, before thinking about Lambda, you should think through your use case and ensure it's a good fit. Otherwise, you can use batch, step functions, or other methods. Reports and the monitoring board could also be improved in terms of alerts. The threshold alerts are there but can be improved. It takes some time to get used to these methods and get the hang of them."

"The solution should continue to streamline integrations with AWS services."

"One area of improvement is to include support for more programming languages. AWS Lambda does not support a lot of programming languages. You have to write the Lambda functions in a certain programming language. We are using C++. My developer knows a couple of other languages. Python is his favorite language, but Python is not supported in AWS Lambda."

"We'd love to see more integration potential in the future."

"I have seen some drawbacks with certain integrations."

More AWS Lambda cons

"Easier scalability and more detailed job monitoring features would be helpful."

"The solution could be improved by providing better graphics and including support for UI and UX testing."

"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."

"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."

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

"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

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

"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."

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

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

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

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

"Apache Spark is an expensive solution."

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

More Apache Spark pricing and cost advice

"AWS is slightly more expensive than Azure."

"The price of AWS Lambda is priced very low."

"AWS Lambda's cloud version isn't expensive, and I'd rate its pricing as five out of five."

"We only need to pay for the compute time our code consumes."

"AWS Lambda is cheap."

"The solution is part of the AWS subscription model that is paid annually."

"The pricing varies based on the specific solution you're implementing, and in comparison to the value it provides, the overall cost is reasonable."

"The fees are volume-based."

More AWS Lambda pricing and cost advice

"The current price is substantial."

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

"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."

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

"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."

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

"Azure Stream Analytics is a little bit expensive."

More Azure Stream Analytics pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Hadoop solutions are best for your needs.

See recommendations

849,190 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Educational Organization

68%

Financial Services Firm

Computer Software Company

Manufacturing Company

Computer Software Company

15%

Financial Services Firm

14%

Manufacturing Company

10%

Retailer

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 requir...

See all answers

What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential ...

See all answers

Which is better, AWS Lambda or Batch?

AWS Lambda is a serverless solution. It doesn’t require any infrastructure, which allows for cost savings. There is n...

See all answers

What do you like most about AWS Lambda?

The tool scales automatically based on the number of incoming requests.

See all answers

What is your experience regarding pricing and costs for AWS Lambda?

AWS Lambda is cheaper compared to running an instance continuously. You only pay for what you use, making it cost-eff...

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 analyti...

See all answers

What is your experience regarding pricing and costs for Azure Stream Analytics?

I have no problem with pricing. We sell the data analytics value and operational value to customers, focusing on prod...

See all answers

What needs improvement with Azure Stream Analytics?

There is a lack of technical support from Microsoft's local office, particularly in Taiwan. We often have to learn on...

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 27% 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 7% of the time

AWS Fargate vs Apache Spark

Compared 3% of the time

More Apache Spark Competitors

AWS Batch vs AWS Lambda

Compared 52% of the time

Apache NiFi vs AWS Lambda

Compared 17% of the time

Amazon EC2 Auto Scaling vs AWS Lambda

Compared 9% of the time

Amazon EC2 vs AWS Lambda

Compared 4% of the time

More AWS Lambda Competitors

Databricks vs Azure Stream Analytics

Compared 26% of the time

Amazon MSK vs Azure Stream Analytics

Compared 23% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 18% of the time

Apache Flink vs Azure Stream Analytics

Compared 16% of the time

Apache Kafka vs Azure Stream Analytics

Compared 1% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

AWS Lambda

April 2025

Download AWS Lambda product report

Buyer's Guide

Azure Stream Analytics

April 2025

Download Azure Stream Analytics product report

Also Known As

No data available

ASA

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

Apache

AWS Lambda is a compute service that lets you run code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second. You pay only for the compute time you consume - there is no charge when your code is not running. With AWS Lambda, you can run code for virtually any type of application or backend service - all with zero administration. AWS Lambda runs your code on a high-availability compute infrastructure and performs all of the administration of the compute resources, including server and operating system maintenance, capacity provisioning and automatic scaling, code monitoring and logging. All you need to do is supply your code in one of the languages that AWS Lambda supports (currently Node.js, Java, C# and Python).

You can use AWS Lambda to run your code in response to events, such as changes to data in an Amazon S3 bucket or an Amazon DynamoDB table; to run your code in response to HTTP requests using Amazon API Gateway; or invoke your code using API calls made using AWS SDKs. With these capabilities, you can use Lambda to easily build data processing triggers for AWS services like Amazon S3 and Amazon DynamoDB process streaming data stored in Amazon Kinesis, or create your own back end that operates at AWS scale, performance, and security.

Amazon Web Services (AWS)

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

Microsoft

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

Netflix

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: March 2025.

DOWNLOAD NOW

849,190 professionals have used our research since 2012.