Apache Flink vs Coralogix comparison

Read 9 Coralogix reviews

50 Views
34 Comparison Views

90% willing to recommend

Apache Flink

Comparison Buyer's Guide

Download the report

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: March 2025).

Apache Flink vs. Coralogix

March 2025

Download the complete report

Helped 845,589 peers since 2012

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

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

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

Senior Software Development Engineer at Yahoo!

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.

Read full review

reviewer1915599

VP of Engineering at a financial services firm with 51-200 employees

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.

Read full review

Quotes from Members

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

Pros

"With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts."

"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."

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

"Easy to deploy and manage."

"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."

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

"This is truly a real-time solution."

"The setup was not too difficult."

More Apache Flink pros

"A non-tech person can easily get used to it."

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

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

"Numerous data monitoring tools are available, but Coralogix somehow fine-tunes our policies and effectively supports our teams."

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

"The initial setup is straightforward."

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

"The solution offers very good convenience filtering."

More Coralogix pros

Cons

"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."

"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."

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

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

"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 a future release, they could improve on making the error descriptions more clear."

"Apache Flink's documentation should be available in more languages."

"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."

More Apache Flink cons

"The user interface is not intuitive, especially when first onboarding, and improvements could be made here."

"The documentation of the tool could be improved"

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

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

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

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

"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."

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

More Coralogix cons

Pricing and Cost Advice

"It's an open-source solution."

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

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

"It's an open source."

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

"We are paying roughly $5,000 a month."

"The platform has a reasonable cost. I rate the pricing a three out of ten."

"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."

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

845,589 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

23%

Computer Software Company

16%

Manufacturing Company

Retailer

Computer Software Company

16%

Financial Services Firm

10%

Healthcare Company

Manufacturing Company

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...

Spring Cloud Data Flow vs Apache Flink

Comparisons

Compared 25% of the time

Databricks vs Apache Flink

Compared 21% of the time

Azure Stream Analytics vs Apache Flink

Compared 12% of the time

Amazon Kinesis vs Apache Flink

Compared 10% of the time

Google Cloud Dataflow vs Apache Flink

Compared 8% of the time

More Apache Flink Competitors

Datadog vs Coralogix

Compared 30% of the time

New Relic vs Coralogix

Compared 10% of the time

Grafana vs Coralogix

Compared 10% of the time

Sentry vs Coralogix

Compared 8% of the time

Elastic Search vs Coralogix

Compared 7% of the time

More Coralogix Competitors

Product Reports

Download Apache Flink product report

Apache Flink

April 2025

Download Coralogix product report

April 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