Compare Apache Spark vs. Spring Boot

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10,877 views|8,741 comparisons
Spring Boot Logo
12,741 views|10,310 comparisons
Most Helpful Review
Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: July 2021.
522,281 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.""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.""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.""AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI."

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

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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.""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.""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.""Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

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

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Pricing and Cost Advice
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

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"Spring Boot is free; even the Spring Tools Suite for Eclipse is free.""This is an open-source product."

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Questions from the Community
Top Answer: I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library.
Top Answer: Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera.
Top Answer: The logging for the observability platform could be better.
Top Answer: 1. Open Source 2. Excellent Community Support -- Widely used across different projects -- so your search for answers would be easy and almost certain. 3. Extendable Stack with a wide array of… more »
Top Answer: The platform is easy for developers to download.
Ranking
1st
out of 11 in Java Frameworks
Views
10,877
Comparisons
8,741
Reviews
12
Average Words per Review
441
Rating
8.6
2nd
out of 11 in Java Frameworks
Views
12,741
Comparisons
10,310
Reviews
6
Average Words per Review
552
Rating
8.7
Popular Comparisons
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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

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.

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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 Solutions
Information Not Available
Top Industries
REVIEWERS
Financial Services Firm40%
Computer Software Company20%
Marketing Services Firm10%
Non Profit10%
VISITORS READING REVIEWS
Computer Software Company23%
Comms Service Provider19%
Financial Services Firm11%
Media Company9%
VISITORS READING REVIEWS
Comms Service Provider29%
Computer Software Company22%
Financial Services Firm12%
Government6%
Company Size
REVIEWERS
Small Business36%
Midsize Enterprise21%
Large Enterprise42%
REVIEWERS
Small Business56%
Midsize Enterprise11%
Large Enterprise33%
Find out what your peers are saying about Apache Spark vs. Spring Boot and other solutions. Updated: July 2021.
522,281 professionals have used our research since 2012.

Apache Spark is ranked 1st in Java Frameworks with 10 reviews while Spring Boot is ranked 2nd in Java Frameworks with 6 reviews. Apache Spark is rated 8.6, 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, AWS Batch, SAP HANA, AWS Lambda and Apache NiFi, whereas Spring Boot is most compared with Eclipse MicroProfile, Jakarta EE, Open Liberty, Vert.x and Oracle Application Development Framework. See our Apache Spark vs. Spring Boot report.

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