AWS Lambda vs Apache Spark comparison

Apache and Amazon Web Services (AWS) are both solutions in the Compute Service category. Apache is ranked #5 with an average rating of 8.6, while Amazon Web Services (AWS) is ranked #1 with an average rating of 8.5. Apache holds a 11.3% mindshare in CS, compared to Amazon Web Services (AWS)’s 21.3% 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

1,415 Views
1,077 Comparison Views

90% willing to recommend

AWS Lambda

Read 88 AWS Lambda reviews

3,782 Views
2,037 Comparison Views

94% willing to recommend

Apache Spark

AWS Lambda

Comparison Buyer's Guide

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Executive Summary

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

Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed AWS Lambda vs. Apache Spark Report (Updated: April 2025).

Buyer's Guide

AWS Lambda vs. Apache Spark

April 2025

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Review summaries and opinions

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

Categories and Ranking

Apache Spark

Ranking in Compute Service

5th

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Hadoop (1st), Java Frameworks (2nd)

AWS Lambda

Ranking in Compute Service

1st

Average Rating

8.6

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the Compute Service category, the mindshare of Apache Spark is 11.3%, up from 10.2% compared to the previous year. The mindshare of AWS Lambda is 21.3%, up from 21.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Compute Service

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.

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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.

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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

"Spark can handle small to huge data and is suitable for any size of company."

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"The good performance. The nice graphical management console. The long list of ML algorithms."

"The data processing framework is good."

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

"The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."

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

"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term."

More Apache Spark pros

"The solution works for small applications. It is a serverless tool that is quick to spin up. We needn’t consider anything in the bag."

"The cool thing about AWS Lambda is that AWS does all the management. For compression, it is all about making the data small and then making it regular size again. We have an encode function and a decode function. AWS Lambda schedules each of those for us. It has a load balancer and all the fancy stuff, depending on the demand. The most valuable part of AWS Lambda is that I only need to write the software. I need to write two functions, and my cloud developer turns them into two AWS Lambda instances. That's it."

"Some of the most valuable features are that it's easy to install and use. The performance is also good."

"The most valuable feature of AWS Lambda is that you can trigger and run jobs instantly, and after you complete the job, that function is either destroyed or stopped automatedly."

"The ease and speed of developing the services using any available language is the most valuable feature."

"AWS Lambda's event-driven architecture is highly valuable."

"The feature I found most valuable about Lambda is the fact that it's serverless."

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

More AWS Lambda pros

Cons

"At the initial stage, the product provides no container logs to check the activity."

"Needs to provide an internal schedule to schedule spark jobs with monitoring capability."

"Dynamic DataFrame options are not yet available."

"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."

"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."

"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."

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

"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."

More Apache Spark cons

"There are other similar solutions, such as Google Cloud Platform or Microsoft Azure. They might be better for small tasks."

"Lambda has limitations on the amount of memory you can use and is not a good solution for long running processes."

"Regarding layers, you need to manually zip and install them. This step needs practice, and you might need to do it three to four times to get a hang of it."

"Lambda would benefit from a debugging feature as well."

"The tool changes its UI every month which is very frustrating for me. I don’t know why AWS keeps changing the UI. They can’t stick to a specific one"

"The overall performance of this solution could be improved. We would also like to have better integration with other AWS features."

"We can write anything as code, but the solution will not give proper error information."

"The automation with other Amazon products could be better."

More AWS Lambda cons

Pricing and Cost Advice

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"Apache Spark is an open-source tool."

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

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

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

"The tool is an open-source product. If you're using the open-source Apache Spark, no fees are involved at any time. Charges only come into play when using it with other services like Databricks."

"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."

More Apache Spark pricing and cost advice

"Price-wise, AWS Lambda is very cheap. It's not free, but it's not that expensive."

"The price of the solution is reasonable."

"Its pricing is on the higher side."

"The fees are volume-based."

"Lambda is an affordable solution. They offer free requests every month and charge per the compute time. If you are working in a big organization, usually AWS offer a savings plan where you get approximately 70% discount on pricing."

"The solution follows a pay-as-you-go licensing model, which results in cost savings."

"It computes by the cycle, and it's very cheap."

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

More AWS Lambda pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Educational Organization

68%

Financial Services Firm

Computer Software Company

Manufacturing 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?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

There is complexity when it comes to understanding the whole ecosystem, especially for beginners. I find it quite complex to understand how a Spark job is initiated, the roles of driver nodes, work...

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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 no setup process to deal with, as the entire solution is in the cloud. If you use...

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What do you like most about AWS Lambda?

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

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

The pricing of AWS Lambda is reasonable. It's beneficial and cost-effective for users regardless of the number of instances used.

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 27% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 11% of the time

Spark SQL vs Apache Spark

Compared 7% of the time

Apache NiFi vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

AWS Batch vs AWS Lambda

Compared 53% 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

Google Cloud Dataflow vs AWS Lambda

Compared 3% of the time

More AWS Lambda Competitors

Product Reports

Buyer's Guide

Apache Spark

May 2025

Download Apache Spark product report

Buyer's Guide

AWS Lambda

May 2025

Download AWS Lambda product report

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)

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

Buyer's Guide

AWS Lambda vs. Apache Spark

April 2025

Free Report: AWS Lambda vs. Apache Spark

Find out what your peers are saying about AWS Lambda vs. Apache Spark and other solutions. Updated: April 2025.

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