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Amazon EC2 Auto Scaling vs Apache Spark comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Amazon EC2 Auto Scaling
Ranking in Compute Service
2nd
Average Rating
8.8
Reviews Sentiment
8.2
Number of Reviews
44
Ranking in other categories
No ranking in other categories
Apache Spark
Ranking in Compute Service
4th
Average Rating
8.4
Reviews Sentiment
7.7
Number of Reviews
64
Ranking in other categories
Hadoop (1st), Java Frameworks (2nd)
 

Mindshare comparison

As of December 2024, in the Compute Service category, the mindshare of Amazon EC2 Auto Scaling is 11.9%, up from 10.7% compared to the previous year. The mindshare of Apache Spark is 11.1%, up from 7.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Compute Service
 

Featured Reviews

Poulav Biswas - PeerSpot reviewer
Well-documented setup process and highly stable solution
We have several instances and applications that we run using WordPress. For that, I needed an easy, secure, and faster solution with different options to back up the website and data. Amazon EC2 offers options to back up data using the S3 version control system, which worked really well for us…
SurjitChoudhury - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"The initial setup of Amazon EC2 Auto Scaling is easy...Since we are an enterprise-sized company and a client of Amazon, the response from the technical support team was immediate."
"The solution incorporates ease of maintenance and reduction in operational overhead and costs. Patching is also easy."
"The solution includes many features for configuring networks and VPCs."
"Auto-scaling is a good feature."
"The most valuable features are that it is stable, flexible, and reliable."
"The most valuable features include the availability of various services like compute, memory, and database services in the AWS landscape."
"The solution removes the need for hardware. We can easily create servers or machines. Just by clicking or specifying our requirements, like memory size or disk space, it's set up for us. The tool eliminates the need for hardware. We can choose what we need and pay as we use it. It is flexible and can integrate with any product."
"Sometimes, Auto Scaling is more beneficial, and sometimes, Reserved Instances are preferred, especially for longer-term usage."
"The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."
"We use it for ETL purposes as well as for implementing the full transformation pipelines."
"The good performance. The nice graphical management console. The long list of ML algorithms."
"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."
"The most valuable feature of Apache Spark is its memory processing because it processes data over RAM rather than disk, which is much more efficient and fast."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"The features we find most valuable are the machine learning, data learning, and Spark Analytics."
"Features include machine learning, real time streaming, and data processing."
 

Cons

"There is room for improvement in the pricing model."
"As we are transitioning to managing containerized applications, the solution could improve by adding more managed container services as a feature in the solution."
"The technical support needs to be improved."
"The solution could improve by having more automation. Nowadays there is a vast variety of automation. Additionally, infrastructure monitoring could improve."
"The product should improve vertical scaling features."
"The solution's pricing is expensive. You pay based on how much you use it, like paying for the time or hours you use the service. There's no need to buy hardware separately."
"Amazon EC2 Auto Scaling could improve by adding better integration features with the other services. Additionally, if the alarms could be triggered from other services this would be beneficial."
"Amazon EC2 Auto Scaling can provide more discounts when using the machines the solution uses."
"One limitation is that not all machine learning libraries and models support it."
"Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn."
"The setup I worked on was really complex."
"They could improve the issues related to programming language for the platform."
"Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."
"The management tools could use improvement. Some of the debugging tools need some work as well. They need to be more descriptive."
"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."
"The migration of data between different versions could be improved."
 

Pricing and Cost Advice

"The solution is not expensive."
"The product is cheap."
"The product is quite expensive."
"There is no specific pricing for Amazon EC2 Auto Scaling, but we have to pay for the number of machines getting scaled up."
"The tool's pricing is good and not expensive."
"The licences for this solution are based on the number of instances. This determines the EC2 type that is used and this is then priced accordingly."
"The pricing is not fixed and it is based on usage."
"When we want to use more services, we need to pay more. It's a monthly subscription, rather than licensed-based. Pricing or fees for Amazon EC2 Auto Scaling could be improved."
"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."
"The product is expensive, considering the setup."
"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."
"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."
"It is quite expensive. In fact, it accounts for almost 50% of the cost of our entire project."
"Apache Spark is an open-source tool."
"The solution is affordable and there are no additional licensing costs."
"They provide an open-source license for the on-premise version."
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Computer Software Company
17%
University
8%
Government
7%
Financial Services Firm
27%
Computer Software Company
13%
Manufacturing Company
8%
Retailer
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Amazon EC2 Auto Scaling?
The solution removes the need for hardware. We can easily create servers or machines. Just by clicking or specifying our requirements, like memory size or disk space, it's set up for us. The tool e...
What is your experience regarding pricing and costs for Amazon EC2 Auto Scaling?
The pricing structure from AWS is really complex and depends on factors like the region and specific services used. Prices can vary significantly even within the same service across different locat...
What needs improvement with Amazon EC2 Auto Scaling?
There is a need for improvement in understanding the pricing structure, as it is complex and depends on several factors such as the location of data centers.
What do you like most about Apache Spark?
We use Spark to process data from different data sources.
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...
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...
 

Also Known As

AWS RAM
No data available
 

Overview

 

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Find out what your peers are saying about Amazon EC2 Auto Scaling vs. Apache Spark and other solutions. Updated: December 2024.
824,053 professionals have used our research since 2012.