Compare Amazon EC2 vs. Apache Spark

You must select at least 2 products to compare!
Amazon EC2 Logo
305 views|141 comparisons
Apache Spark Logo
11,256 views|9,343 comparisons
Most Helpful Review
Use Amazon EC2? Share your opinion.
Find out what your peers are saying about Amazon EC2 vs. Apache Spark and other solutions. Updated: September 2020.
442,141 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:

"The scalability of the solution is fantastic. It's one of our favorite features.""The most valuable feature of this solution is the ability to have standard operating systems along with the Windows, Linux operating systems, and their maintenance-free structure, which we prefer.""All of my lower maintenance overheads are taken care of. I don't have to worry about it."

More Amazon EC2 Pros »

"I found the solution stable. We haven't had any problems with it.""The scalability has been the most valuable aspect of the solution.""The most valuable feature of this solution is its capacity for processing large amounts of data.""The solution is very stable.""I feel the streaming is its best feature.""The features we find most valuable are the machine learning, data learning, and Spark Analytics.""The main feature that we find valuable is that it is very fast.""The processing time is very much improved over the data warehouse solution that we were using."

More Apache Spark Pros »

"The customization could be simplified.""One of the challenges is the AMI upgrades.""Technical itself could be a bit more helpful, especially when it comes to integration assistance. When we talk to the technical team, often it's some issue with integration and they'll tell us to talk to the other company. Often, the other company will look at everything and not see an issue from their end and then we are at an impasse."

More Amazon EC2 Cons »

"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.""When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.""The solution needs to optimize shuffling between workers.""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.""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.""We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.""I would like to see integration with data science platforms to optimize the processing capability for these tasks."

More Apache Spark Cons »

Pricing and Cost Advice
"For our usage, the cost is approximately $20,000 to $23,000 per month."

More Amazon EC2 Pricing and Cost Advice »

Information Not Available
Use our free recommendation engine to learn which Compute Service solutions are best for your needs.
442,141 professionals have used our research since 2012.
Questions from the Community
Top Answer: The scalability of the solution is fantastic. It's one of our favorite features.
Top Answer: We're charged depending on the run time, but there are other costs as well, including costs for transactions and storage.
Top Answer: The customization and configuration could be simplified. Updates could be automated and simplified.
Top Answer: SQreamDB is a GPU DB. It is not suitable for real-time oltp of course. Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)… more »
Top Answer: I love every core functionality of Apache Spark Initially they have only provided RDD basic interface to process the data across distributed cluster. Then it evolved to dataframe and dataset interface… more »
Top Answer: Apache spark is available in cloud services like AWS cloud, Azure. We have to use the specific service for our use case. For example we can use AWS Glue which runs spark for ETL process, AWS EMR… more »
out of 13 in Compute Service
Average Words per Review
Avg. Rating
out of 13 in Compute Service
Average Words per Review
Avg. Rating
Popular Comparisons
Compared 100% of the time.
Compared 32% of the time.
Compared 7% of the time.
Compared 7% of the time.
Compared 6% of the time.
Also Known As
Amazon Elastic Compute Cloud, EC2

Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.

Amazon EC2’s simple web service interface allows you to obtain and configure capacity with minimal friction. It provides you with complete control of your computing resources and lets you run on Amazon’s proven computing environment. Amazon EC2 reduces the time required to obtain and boot new server instances to minutes, allowing you to quickly scale capacity, both up and down, as your computing requirements change. Amazon EC2 changes the economics of computing by allowing you to pay only for capacity that you actually use. Amazon EC2 provides developers the tools to build failure resilient applications and isolate them from common failure scenarios.

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

Learn more about Amazon EC2
Learn more about Apache Spark
Sample Customers
Netflix, Expedia, TimeInc., Novaris, airbnb, LamborghiniNASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab,, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Top Industries
No Data Available
Financial Services Firm38%
Computer Software Company25%
Marketing Services Firm13%
Non Profit13%
Computer Software Company31%
Media Company14%
Comms Service Provider13%
Financial Services Firm5%
Company Size
No Data Available
Small Business40%
Midsize Enterprise20%
Large Enterprise40%
Find out what your peers are saying about Amazon EC2 vs. Apache Spark and other solutions. Updated: September 2020.
442,141 professionals have used our research since 2012.
Amazon EC2 is ranked 4th in Compute Service with 3 reviews while Apache Spark is ranked 1st in Compute Service with 11 reviews. Amazon EC2 is rated 8.4, while Apache Spark is rated 8.2. The top reviewer of Amazon EC2 writes "Good user interface with great built-in monitoring and very good documentation". On the other hand, the top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". Amazon EC2 is most compared with Apache NiFi, whereas Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, SAP HANA and AWS Lambda. See our Amazon EC2 vs. Apache Spark report.

See our list of best Compute Service vendors.

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.