AWS Lambda vs Apache Spark comparison

Apache and Amazon Web Services (AWS) are both solutions in the Compute Service category. Apache is ranked #4 with an average rating of 8.6, while Amazon Web Services (AWS) is ranked #1 with an average rating of 8.4. 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

1,526 Views
1,149 Comparison Views

90% willing to recommend

AWS Lambda

Read 85 AWS Lambda reviews

4,528 Views
2,369 Comparison Views

94% willing to recommend

Apache Spark

AWS Lambda

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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

4th

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

8.4

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

"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."

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

"DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort."

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

"With Spark, we parallelize our operations, efficiently accessing both historical and real-time data."

"The solution is very stable."

"It is highly scalable, allowing you to efficiently work with extensive datasets that might be problematic to handle using traditional tools that are memory-constrained."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

More Apache Spark pros

"It is serverless and scalable. It can scale infinitely. You don't have to worry about the size of the servers that you're pre-allocating. You don't have to build server scale-out models. Auto scale and other similar features are just inherent in Lambda. So, for atomic and fairly non-persistent transactional units of work, Lambda works very well."

"The fact that it is serverless is really important."

"AWS Lambda has improved our productivity and functionality."

"The solution offers good performance."

"The most valuable feature of this solution is the API Gateway."

"It's also suitable for companies of any size. For example, we're a large enterprise, and we've used Lambda without any problems in the last 10 months."

"It enables the launch of thousands of instances simultaneously,"

"The solution runs on the latest cloud technology so it is easy to deploy cloud-native projects."

More AWS Lambda pros

Cons

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"Apache Spark lacks geospatial data."

"It should support more programming languages."

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

"The initial setup was not easy."

"Spark could be improved by adding support for other open-source storage layers than Delta Lake."

"Apart from the restrictions that come with its in-memory implementation. It has been improved significantly up to version 3.0, which is currently in use."

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

More Apache Spark cons

"AWS Lambda can improve its file system-based sharing capabilities and restrictions."

"The user-friendliness of the solution could be improved."

"We don't have the inbuilt modules in AWS Lambda. If more modules were built into or integrated with AWS Lambda, that would help developers to code."

"If you are setting it up on hybrid solution, there is a lot of work that needs to go in."

"AWS Lambda should support additional languages."

"AWS Lambda could be improved by increasing the size of the payload. Also, sometimes Lambda doesn't implement well for bigger solutions."

"The setup was pretty complex because there were many steps. For me, it was complex because I was somewhat new at it. It could be easier for someone who has done it a bunch of times. I just found that it was a very dense user experience. There's a lot going on during setup."

"It currently requires manual user maintenance to upgrade and evaluate, and an automated provision for this would be beneficial."

More AWS Lambda cons

Pricing and Cost Advice

"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."

"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."

"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 platform. We do not pay for its subscription."

"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."

"Apache Spark is an expensive solution."

"Apache Spark is an open-source tool."

More Apache Spark pricing and cost advice

"AWS Lambda license is paid on a monthly basis."

"I think the price is okay. However, if they add more functionality, they can have better prices. In fact, they should have better and more flexible packages for clients who have greater consumption of Lambda."

"The price of the solution is reasonable."

"You're not paying for a server if you're not using it, which is another reason I like it. So, you're not paying if you're not using it. It scales, and you're charged based on usage. It all depends on the use case. Some can be extremely inexpensive if you have very low volume transaction rates. That way, you don't have to fire up and absorb the cost of the servers just sitting there waiting for a transaction to come through. You're only paying when you use it. So, depending upon the use model, Lambda could be highly efficient relative to an EC2 solution. You don't have to have things reallocated."

"AWS Lambda is not expensive for micro testing but is expensive if used for long deployment or long services."

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

"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 price of AWS Lambda is priced very low."

More AWS Lambda pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Educational Organization

67%

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?

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 Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

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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...

See all answers

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?

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

See all answers

Comparisons

Spring Boot vs Apache Spark

Compared 26% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Apache NiFi vs Apache Spark

Compared 4% of the time

More Apache Spark Competitors

AWS Batch vs AWS Lambda

Compared 52% of the time

Apache NiFi vs AWS Lambda

Compared 18% 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

April 2025

Download Apache Spark product report

Buyer's Guide

AWS Lambda

April 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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