Apache Flink vs Coralogix comparison

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Apache Flink

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Executive SummaryUpdated on Dec 17, 2024

Coralogix and Apache Flink compete in the realm of data analytics and processing solutions. Apache Flink appears to have the upper hand due to its advanced real-time stream processing capabilities, providing significant value for event-driven and real-time analytics.

Features: Coralogix offers robust log analytics, real-time alerting, and seamless integration with various data sources, providing detailed insights into application performance. Apache Flink excels in distributed stream processing with stateful transformations and low-latency data handling, supporting complex event-driven applications and real-time data processing without distinction between batch and streaming data.

Room for Improvement: Coralogix could enhance its application by expanding real-time processing capabilities and better handling large-scale data. Its user interface, while user-friendly, may benefit from more customization options. Additionally, further development in flexibility and integration with emerging technologies could boost its appeal. Apache Flink, although powerful, could improve its deployment complexity, reduce resource demands such as high RAM requirements, and simplify operations to broaden its accessibility. Enhancements in onboarding processes and documentation would also help users better utilize its full potential.

Ease of Deployment and Customer Service: Coralogix stands out in ease of deployment with its flexible cloud-based setup and responsive customer support, making it accessible for businesses focused on log management with limited IT resources. Apache Flink requires more advanced deployment efforts, often needing dedicated IT expertise due to its complex infrastructure setups. Its community-driven support offers a different experience compared to Coralogix's formalized support framework.

Pricing and ROI: Coralogix offers competitive pricing with scalable plans appropriate for businesses seeking extensive log monitoring solutions with good return on investment without incurring high upfront costs. Apache Flink, as an open-source platform, mandates a more considerable investment in expertise and infrastructure to optimize its capabilities, potentially yielding substantial ROI for companies harnessing high-throughput data processing. Decision-makers must weigh the total cost of ownership against the operational benefits it may bring.

To learn more, read our detailed Apache Flink vs. Coralogix Report (Updated: July 2025).

Apache Flink vs. Coralogix

July 2025

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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

4th

Average Rating

7.8

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Coralogix

Ranking in Streaming Analytics

14th

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Application Performance Monitoring (APM) and Observability (21st), Log Management (22nd), Security Information and Event Management (SIEM) (23rd), API Management (16th), Anomaly Detection Tools (1st)

Mindshare comparison

As of September 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 14.6%, up from 10.1% compared to the previous year. The mindshare of Coralogix is 0.4%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Apache Flink	14.6%
Coralogix	0.4%
Other	85.0%

Streaming Analytics

Featured Reviews

Aswini Atibudhi

Distinguished AI Leader at Walmart Global Tech at Walmart

Enables robust real-time data processing but documentation needs refinement

Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing. It's essential to have a clear foundation; hence, it can be tough for beginners. However, once they grasp the concepts and have examples or references, it becomes easier. Intermediate users who are integrating with Kafka or other sources may find it smoother. After setting up and understanding the concepts, it becomes quite stable and scalable, allowing for customization of jobs. Every software, including Apache Flink, has room for improvement as it evolves. One key area for enhancement is user-friendliness and the developer experience; improving documentation and API specifications is essential, as they can currently be verbose and complex. Debugging and local testing pose challenges for newcomers, particularly when learning about concepts such as time semantics and state handling. Although the APIs exist, they aren't intuitive enough. We also need to simplify operational procedures, such as developing tools and tuning Flink clusters, as these processes can be quite complex. Additionally, implementing one-click rollback for failures and improving state management during dynamic scaling while retaining the last states is vital, as the current large states pose scaling challenges.

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Jorge Florez

Head SRE Latam at PayU

SaaS platform used by developers to store and conveniently search for logs

If a company has the budget and the log service is critical for them, I would say use Coralogix. It is a very good service for that. I would rate Coralogix an eight out of ten. It is an excellent service for storing logs for a long time. The capacity is unlimited for unindexed logs. The cost model is also very efficient because you pay for the ingested data per month. This can be compared to a solution like New Relic where you have to pay it upfront and cannot limit the data ingestion. Coralogix provides an easy way to search for logs and to visualize them. This a great feature because developers are constantly looking for or browsing logs.

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Quotes from Members

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

Pros

"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."

"Allows us to process batch data, stream to real-time and build pipelines."

"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."

"Easy to deploy and manage."

"The ease of usage, even for complex tasks, stands out."

"Apache Flink offers a range of powerful configurations and experiences for development teams. Its strength lies in its development experience and capabilities."

"Apache Flink's best feature is its data streaming tool."

"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."

More Apache Flink pros

"The solution is easy to use and to start with."

"The solution offers very good convenience filtering."

"The most valuable feature of Coralogix is that it is a very good vendor for metrics."

"The initial setup is straightforward."

"The best feature of this solution allows us to correlate logs, metrics and traces."

"For now, we have not experienced any stability issues."

"The overall stability and reliability of Coralogix are excellent, and I rarely encounter issues."

"Coralogix scales well, and I will rate it nine out of ten."

More Coralogix pros

Cons

"Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool."

"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."

"There is a learning curve. It takes time to learn."

"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 machine learning library is not very flexible."

"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."

"There is room for improvement in the initial setup process."

More Apache Flink cons

"The documentation of the tool could be improved"

"The user interface could be more intuitive and explanatory."

"The customizable dashboards haven't really helped with my company's efficiency at all, and I think there's room for improvement."

"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."

"Coralogix should have some AI capabilities to auto-detect anomalies and provide suggestions."

"Maybe they could make it more user-friendly."

"From my experience, Coralogix has horrible Terraform providers."

"It would be helpful if Coralogix could integrate the main modules that any organization requires into a single subscription."

More Coralogix cons

Pricing and Cost Advice

"It's an open source."

"The solution is open-source, which is free."

"It's an open-source solution."

"This is an open-source platform that can be used free of charge."

"Apache Flink is open source so we pay no licensing for the use of the software."

"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 cost of the solution is per volume of data ingested."

"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

22%

Computer Software Company

11%

Retailer

11%

Manufacturing Company

Financial Services Firm

11%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	11

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	1
Large Enterprise	4

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?

Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool. There is a need for increased awareness and education, especially around best ...

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?

Change might not be the correct word, but with every service, there is always room to improve. They are improving their services daily and deploy new features. When we had missing features that we ...

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Product Reports

Download Apache Flink product report

Apache Flink

September 2025

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September 2025

Also Known As

Flink

No data available

Overview

Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.

Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.

Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.

Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.

Apache Flink Features

Distributed execution of streaming programs on clusters of computers
Support for multiple data sources and sinks: this includes Hadoop file systems, databases, and other data sources
Streaming SQL query engine with support for windowing functions
Low latency query execution in milliseconds
Runs in a distributed fashion: it can be deployed on multiple machines or nodes to increase performance and reliability of data processing pipelines.
Powerful API that supports both batch and streaming applications
Runs on clusters of commodity hardware with minimal configuration
Can be integrated with other technologies, such as Apache Spark for complex data mining

Apache Flink Benefits

Ease of use: Flink has an intuitive API and provides high-level abstractions for handling data streams. Even beginners in the field can work with the platform with ease.

Fault tolerance: Flink can automatically detect and recover from failures in the system.

Scalability: Flink scales to thousands of nodes. It can run on clusters of any size and the user does not have to worry about managing the cluster.

Reviews from Real Users

Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:

The head of data and analytics at a computer software company notes, “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.”

Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”

Apache

Coralogix is a stateful streaming data platform that provides real-time insights and long-term trend analysis with no reliance on storage or indexing, solving the monitoring challenges of data growth in large-scale systems.

Ingest log, metric, and security data from any source for a single, centralized platform to monitor and alert on your applications. As data is ingested, Coralogix instantly narrows millions of events down to common patterns for deeper insights and faster troubleshooting. Proactive data storage optimization enables up to 70% savings on monitoring costs with better performance.

Sample Customers

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.

Payoneer, AGS, Monday.com, Capgemini