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Apache Flink vs Coralogix comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

Review summaries and opinions

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

Categories and Ranking

Apache Flink
Ranking in Streaming Analytics
6th
Average Rating
7.6
Reviews Sentiment
6.9
Number of Reviews
16
Ranking in other categories
No ranking in other categories
Coralogix
Ranking in Streaming Analytics
19th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
9
Ranking in other categories
Application Performance Monitoring (APM) and Observability (33rd), Log Management (39th), Security Information and Event Management (SIEM) (42nd), API Management (29th), Anomaly Detection Tools (1st)
 

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 13.2%, up from 9.5% compared to the previous year. The mindshare of Coralogix is 0.2%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Ilya Afanasyev - PeerSpot reviewer
A great solution with an intricate system and allows for batch data processing
We value this solution's intricate system because it comes with a state inside the mechanism and product. The system allows us to process batch data, stream to real-time and build pipelines. Additionally, we do not need to process data from the beginning when we pause, and we can continue from the same point where we stopped. It helps us save time as 95% of our pipelines will now be on Amazon, and we'll save money by saving time.
reviewer1915599 - PeerSpot reviewer
Good capabilities, has a helpful interface and is straightforward to set up
We have asked for a couple of features from the company already. What typically happens is a lot of people - and developers are one of the biggest consumers of this product - go to this product to optimize their investigation process and specific configurations. That increases our data flow at times, so the cost changes. And a lot of changes happen due to that. We have asked the company to auto-revert the changes after a while so that the system works typically. We want it to work at what it is expected to work at and not really based on the updated configuration which one developer has decided to change.

Quotes from Members

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

Pros

"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."
"The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis."
"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."
"Apache Flink's best feature is its data streaming tool."
"Allows us to process batch data, stream to real-time and build pipelines."
"It is user-friendly and the reporting is good."
"Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."
"The setup was not too difficult."
"A non-tech person can easily get used to it."
"The best feature of this solution allows us to correlate logs, metrics and traces."
"For now, we have not experienced any stability issues."
"The solution offers very good convenience filtering."
"The solution is easy to use and to start with."
"Coralogix scales well, and I will rate it nine out of ten."
"The log monitoring is good, and the dashboards that we create are beneficial."
"Numerous data monitoring tools are available, but Coralogix somehow fine-tunes our policies and effectively supports our teams."
 

Cons

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."
"The solution could be more user-friendly."
"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."
"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."
"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."
"The state maintains checkpoints and they use RocksDB or S3. They are good but sometimes the performance is affected when you use RocksDB for checkpointing."
"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."
"In a future release, they could improve on making the error descriptions more clear."
"The user interface could be more intuitive and explanatory."
"Coralogix should have some AI capabilities to auto-detect anomalies and provide suggestions. The increasing volume of data and the resulting bandwidth charges are concerns."
"The documentation of the tool could be improved"
"From my experience, Coralogix has horrible Terraform providers."
"We want it to work at what it is expected to work at and not really based on the updated configuration which one developer has decided to change."
"It would be helpful if Coralogix could integrate the main modules that any organization requires into a single subscription."
"The user interface is not intuitive, especially when first onboarding, and improvements could be made here."
"Coralogix should have some AI capabilities to auto-detect anomalies and provide suggestions."
 

Pricing and Cost Advice

"The solution is open-source, which is free."
"Apache Flink is open source so we pay no licensing for the use of the software."
"It's an open source."
"It's an open-source solution."
"This is an open-source platform that can be used free of charge."
"The cost of the solution is per volume of data ingested."
"Currently, we are at a very minimal cost, which is around $400 per month since we have reduced our usage. Initially, we were at $900 per month."
"The platform has a reasonable cost. I rate the pricing a three out of ten."
"We are paying roughly $5,000 a month."
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Top Industries

By visitors reading reviews
Financial Services Firm
24%
Computer Software Company
16%
Manufacturing Company
7%
Retailer
4%
Computer Software Company
16%
Financial Services Firm
10%
Healthcare Company
8%
Manufacturing Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Apache Flink?
The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. ...
What is your experience regarding pricing and costs for Apache Flink?
The solution is expensive. I rate the product’s pricing a nine out of ten, where one is cheap and ten is expensive.
What needs improvement with Apache Flink?
There are more libraries that are missing and also maybe more capabilities for machine learning. It could have a friendly user interface for pipeline configuration, deployment, and monitoring.
What do you like most about Coralogix?
Numerous data monitoring tools are available, but Coralogix somehow fine-tunes our policies and effectively supports our teams.
What is your experience regarding pricing and costs for Coralogix?
The pricing is expensive. We need to reduce logs to manage costs. Despite the expense, I believe it is worth the money to have Coralogix as a tool.
What needs improvement with Coralogix?
We need to reduce the number of logs generated by our system, which are substantial. We require some form of grouping or categorization of logs to identify them better. Additionally, we find that t...
 

Comparisons

 

Also Known As

Flink
No data available
 

Overview

 

Sample Customers

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.
Payoneer, AGS, Monday.com, Capgemini
Find out what your peers are saying about Apache Flink vs. Coralogix and other solutions. Updated: March 2025.
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