Apache Flink vs. Apache Spark

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Flink
Score 9.0 out of 10
N/A
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. And FlinkCEP is the Complex Event Processing (CEP) library implemented on top of Flink. Users can detect event patterns in streams of events.N/A
Apache Spark
Score 8.9 out of 10
N/A
N/AN/A
Pricing
Apache FlinkApache Spark
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache FlinkApache Spark
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details——
More Pricing Information
Community Pulse
Apache FlinkApache Spark
Considered Both Products
Apache Flink
Chose Apache Flink
Apache Spark is more user-friendly and features higher-level APIs. However, it was initially built for batch processing and only more recently gained streaming capabilities. In contrast, Apache Flink processes streaming data natively. Therefore, in terms of low latency and …
Apache Spark

No answer on this topic

Top Pros
Top Cons
Features
Apache FlinkApache Spark
Streaming Analytics
Comparison of Streaming Analytics features of Product A and Product B
Apache Flink
8.7
1 Ratings
8% above category average
Apache Spark
-
Ratings
Real-Time Data Analysis10.01 Ratings00 Ratings
Data Ingestion from Multiple Data Sources7.01 Ratings00 Ratings
Low Latency10.01 Ratings00 Ratings
Data wrangling and preparation6.01 Ratings00 Ratings
Linear Scale-Out9.01 Ratings00 Ratings
Data Enrichment10.01 Ratings00 Ratings
Best Alternatives
Apache FlinkApache Spark
Small Businesses
IBM Streams (discontinued)
IBM Streams (discontinued)
Score 9.0 out of 10

No answers on this topic

Medium-sized Companies
Confluent
Confluent
Score 7.9 out of 10
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Enterprises
Spotfire Streaming
Spotfire Streaming
Score 7.1 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 7.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache FlinkApache Spark
Likelihood to Recommend
9.0
(1 ratings)
9.3
(24 ratings)
Likelihood to Renew
-
(0 ratings)
10.0
(1 ratings)
Usability
-
(0 ratings)
8.7
(4 ratings)
Support Rating
-
(0 ratings)
8.7
(4 ratings)
User Testimonials
Apache FlinkApache Spark
Likelihood to Recommend
Apache
In well-suited scenarios, I would recommend using Apache Flink when you need to perform real-time analytics on streaming data, such as monitoring user activities, analyzing IoT device data, or processing financial transactions in real-time. It is also a good choice in scenarios where fault tolerance and consistency are crucial. I would not recommend it for simple batch processing pipelines or for teams that aren't experienced, as it might be overkill, and the steep learning curve may not justify the investment.
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Apache
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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Pros
Apache
  • Low latency Stream Processing, enabling real-time analytics
  • Scalability, due its great parallel capabilities
  • Stateful Processing, providing several built-in fault tolerance systems
  • Flexibility, supporting both batch and stream processing
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Apache
  • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
  • Faster in execution times compare to Hadoop and PIG Latin
  • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
  • Interoperability between SQL and Scala / Python style of munging data
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Cons
Apache
  • Python/SQL API, since both are relatively new, still misses a few features in comparison with the Java/Scala option
  • Steep Learning Curve, it's documentation could be improved to something more user-friendly, and it could also discuss more theoretical concepts than just coding
  • Community smaller than other frameworks
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Apache
  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
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Likelihood to Renew
Apache
No answers on this topic
Apache
Capacity of computing data in cluster and fast speed.
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Usability
Apache
No answers on this topic
Apache
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
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Support Rating
Apache
No answers on this topic
Apache
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
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Alternatives Considered
Apache
Apache Spark is more user-friendly and features higher-level APIs. However, it was initially built for batch processing and only more recently gained streaming capabilities. In contrast, Apache Flink processes streaming data natively. Therefore, in terms of low latency and fault tolerance, Apache Flink takes the lead. However, Spark has a larger community and a decidedly lower learning curve.
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Apache
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
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Return on Investment
Apache
  • Allowed for real-time data recovery, adding significant value to the busines
  • Enabled us to create new internal tools that we couldn't find in the market, becoming a strategic asset for the business
  • Enhanced the overall technical capability of the team
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Apache
  • Business leaders are able to take data driven decisions
  • Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
  • Business is able come up with new product ideas
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