Compare Apache Spark vs. Spring Boot

Cancel
You must select at least 2 products to compare!
Apache Spark Logo
11,227 views|9,365 comparisons
Spring Boot Logo
6,593 views|5,234 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: September 2020.
436,846 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 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.The processing time is very much improved over the data warehouse solution that we were using.The main feature that we find valuable is that it is very fast.The features we find most valuable are the machine learning, data learning, and Spark Analytics.I feel the streaming is its best feature.The solution is very stable.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.

More Apache Spark Pros »

The platform is easy for developers to download.The cloud version is very scalable.It gives you confidence in a readily available platform.Features that help with monitoring and tracking network calls between several micro services.I have found the starter solutions valuable, as well as integration with other products.It's great because it simplifies development. Together with MyBatis they make a beautiful pair for Java development.

More Spring Boot Pros »

Cons
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.I would like to see integration with data science platforms to optimize the processing capability for these tasks.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.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.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 solution needs to optimize shuffling between workers.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.

More Apache Spark Cons »

It needs to be simplified, more user-friendly.The security could be simplified.communicationbetween different services from the third party layers or with the legacy applications needs to improve.Having to restart the application to reload properties.Perhaps an even lighter-weight, leaner version could be made available, to compete with alternative solutions, such as NodeJS.The product could be improved by supporting and integrating Hadoop.

More Spring Boot Cons »

Pricing and Cost Advice
Information Not Available
This is an open-source product.Spring Boot is free; even the Spring Tools Suite for Eclipse is free.

More Spring Boot Pricing and Cost Advice »

report
Use our free recommendation engine to learn which Java Frameworks solutions are best for your needs.
436,846 professionals have used our research since 2012.
Questions from the Community
Top Answer: I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505
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 »
Top Answer: I have found the starter solutions valuable, as well as integration with other products.
Top Answer: Spring Boot is free; even the Spring Tools Suite for Eclipse is free. I advise others to use the cost savings to invest in Postman Pro, and to use that product to create and run suites of integration… more »
Top Answer: Perhaps an even lighter-weight, leaner version could be made available, to compete with alternative solutions, such as NodeJS. It would also be extremely helpful if hand-holding templates were… more »
Ranking
1st
out of 11 in Java Frameworks
Views
11,227
Comparisons
9,365
Reviews
11
Average Words per Review
353
Avg. Rating
8.2
2nd
out of 11 in Java Frameworks
Views
6,593
Comparisons
5,234
Reviews
4
Average Words per Review
626
Avg. Rating
9.0
Popular Comparisons
Compared 7% of the time.
Compared 6% of the time.
Compared 6% of the time.
Compared 5% of the time.
Compared 23% of the time.
Compared 18% of the time.
Compared 7% of the time.
Compared 7% of the time.
Learn
Apache
VMware
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

Spring Boot is designed to get developers up and running as quickly as possible, with minimal upfront configuration of Spring. Spring Boot takes an opinionated view of building production-ready applications. Make implementing modern application best practices an intuitive and easy first practice! Build microservices with REST, WebSocket, Messaging, Reactive, Data, Integration, and Batch capabilities via a simple and consistent development experience.

Offer
Learn more about Apache Spark
Learn more about Spring Boot
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
Information Not Available
Top Industries
REVIEWERS
Financial Services Firm29%
Computer Software Company29%
Healthcare Company14%
Marketing Services Firm14%
VISITORS READING REVIEWS
Computer Software Company34%
Media Company14%
Comms Service Provider10%
University5%
VISITORS READING REVIEWS
Computer Software Company41%
Media Company11%
Comms Service Provider9%
Insurance Company6%
Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: September 2020.
436,846 professionals have used our research since 2012.
Apache Spark is ranked 1st in Java Frameworks with 11 reviews while Spring Boot is ranked 2nd in Java Frameworks with 6 reviews. Apache Spark is rated 8.2, while Spring Boot is rated 8.6. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, the top reviewer of Spring Boot writes "Good security and integration, and the autowiring feature saves on development time". Apache Spark is most compared with Azure Stream Analytics, SAP HANA, AWS Batch, AWS Lambda and Amazon EMR, whereas Spring Boot is most compared with Jakarta EE, Eclipse MicroProfile, Oracle Application Development Framework, Open Liberty and Thorntail. See our Apache Spark vs. Spring Boot report.

See our list of best Java Frameworks vendors.

We monitor all Java Frameworks 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.