Compare AWS Lambda vs. Apache Spark

Apache Spark is ranked 1st in Compute Service with 7 reviews while AWS Lambda is ranked 2nd in Compute Service with 4 reviews. Apache Spark is rated 8.0, while AWS Lambda is rated 7.6. The top reviewer of Apache Spark writes "Fast performance and has an easy initial setup". On the other hand, the top reviewer of AWS Lambda writes "Simple to set up, scales well, and integrates well with other AWS services". Apache Spark is most compared with Spring Boot, AWS Lambda and Azure Stream Analytics, whereas AWS Lambda is most compared with Apache NiFi, Apache Spark and Apache Storm. See our AWS Lambda vs. Apache Spark report.
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Apache Spark Logo
11,333 views|9,306 comparisons
AWS Lambda Logo
3,831 views|3,328 comparisons
Most Helpful Review
Find out what your peers are saying about AWS Lambda vs. Apache Spark and other solutions. Updated: September 2019.
372,124 professionals have used our research since 2012.
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 this solution is its capacity for processing large amounts of data.I found the solution stable. We haven't had any problems with it.The scalability has been the most valuable aspect of the solution.Features include machine learning, real time streaming, and data processing.The fault tolerant feature is provided.It provides a scalable machine learning library.With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware.

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The most valuable feature of this solution is the API Gateway.The ability to scale up and down very quickly helps because we can maintain our system performance and business at a low cost.Provides a good, easy path from when you're using an AWS cluster.

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Cons
When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster.The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive.It should support more programming languages.Needs to provide an internal schedule to schedule spark jobs with monitoring capability.Include more machine learning algorithms and the ability to handle streaming of data versus micro batch processing.

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The security needs to be improved.Lamba functions have cold-starts that can cause some delay.I would like to see some better integration with other providers, like Cohesity, Druva, and others. I also think the Lambda interface could be better.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.

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Ranking
1st
out of 8 in Compute Service
Views
11,333
Comparisons
9,306
Reviews
8
Average Words per Review
182
Avg. Rating
7.9
2nd
out of 8 in Compute Service
Views
3,831
Comparisons
3,328
Reviews
3
Average Words per Review
406
Avg. Rating
7.3
Top Comparisons
Compared 30% of the time.
Compared 12% of the time.
Compared 38% of the time.
Compared 34% of the time.
Compared 7% of the time.
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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

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.

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Sample Customers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi SolutionsNetflix
Top Industries
REVIEWERS
Financial Services Firm29%
Software R&D Company29%
Marketing Services Firm14%
Healthcare Company14%
VISITORS READING REVIEWS
Software R&D Company27%
Comms Service Provider12%
Financial Services Firm11%
Media Company9%
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Find out what your peers are saying about AWS Lambda vs. Apache Spark and other solutions. Updated: September 2019.
372,124 professionals have used our research since 2012.
We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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