Apache Flink vs Confluent comparison

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Apache Flink

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Executive Summary

We performed a comparison between Apache Flink and Confluent based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Flink vs. Confluent Report (Updated: October 2024).

Apache Flink vs. Confluent

October 2024

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Categories and Ranking

Apache Flink

Ranking in Streaming Analytics

5th

Average Rating

7.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Confluent

Ranking in Streaming Analytics

3rd

Average Rating

8.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Apache Flink is 11.7%, up from 11.0% compared to the previous year. The mindshare of Confluent is 9.4%, down from 12.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Sunil Morya

Consultant at Tata Consultancy

Easy to deploy and manage; lacking simple integration with Amazon products

The issue we had with Flink was that when you had to refer the schema into the input data stream, it had to be done directly into code. The XLS format where the schema is stored, had to be stored in Python. If the schema changes, you have to redeploy Flink because the basic tasks and jobs are already running. That's one disadvantage. Another was a restriction with Amazon's CloudFormation templates which don't allow for direct deployment in the private subnet. You have to deploy into the public subnet and then from the Amazon console, specify a different private subnet that requires a lot of settings. In general, the integration with Amazon products was not good and was very time-consuming. I'd like to think that has changed.

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Gustavo-Barbosa Dos Santos

Software Architect at C&A Brasil

Has good technical support services and a valuable feature for real-time data streaming

Implementing Confluent's schema registry has significantly enhanced our organization's data quality assurance. It helps us understand the various requirements of multiple customers and validates the information for different versions. We can automate the tasks using Confluent Kafka. Thus, it guarantees us the data quality and maintains the integrity of message contracts.

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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

"The documentation is very good."

"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 setup was not too difficult."

"This is truly a real-time solution."

"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."

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

"Easy to deploy and manage."

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

More Apache Flink pros

"I would rate the scalability of the solution at eight out of ten. We have 20 people who use Confluent in our organization now, and we hope to increase usage in the future."

"The monitoring module is impressive."

"It is also good for knowledge base management."

"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."

"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."

"With Confluent Cloud we no longer need to handle the infrastructure and the plumbing, which is a concern for Confluent. The other advantage is that all portfolios have access to the data that is being shared."

"A person with a good IT background and HTML will not have any trouble with Confluent."

"The solution can handle a high volume of data because it works and scales well."

More Confluent pros

Cons

"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."

"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

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

"Apache Flink should improve its data capability and data migration."

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

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

"The solution could be more user-friendly."

More Apache Flink cons

"there is room for improvement in the visualization."

"It could be improved by including a feature that automatically creates a new topic and puts failed messages."

"It could have more themes. They should also have more reporting-oriented plugins as well. It would be great to have free custom reports that can be dispatched directly from Jira."

"There is no local support team in Saudi Arabia."

"Confluence could improve the server version of the solution. However, most companies are going to the cloud."

"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."

"It requires some application specific connectors which are lacking. This needs to be added."

"One area we've identified that could be improved is the governance and access control to the Kafka topics. We've found some limitations, like a threshold of 10,000 rules per cluster, that make it challenging to manage access at scale if we have many different data sources."

More Confluent cons

Pricing and Cost Advice

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

"It's an open-source solution."

"It's an open source."

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

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

"Confluence's pricing is quite reasonable, with a cost of around $10 per user that decreases as the number of users increases. Additionally, it's worth noting that for teams of up to 10 users, the solution is completely free."

"On a scale from one to ten, where one is low pricing and ten is high pricing, I would rate Confluent's pricing at five. I have not encountered any additional costs."

"You have to pay additional for one or two features."

"Confluent has a yearly license, which is a bit high because it's on a per-user basis."

"Confluent is an expensive solution."

"It comes with a high cost."

"Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenance complexity justify the cost, especially as we scale our platform use."

"The solution is cheaper than other products."

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Computer Software Company

17%

Manufacturing Company

Educational Organization

Financial Services Firm

19%

Computer Software Company

18%

Manufacturing Company

Retailer

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 Confluent?

I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and to...

What is your experience regarding pricing and costs for Confluent?

Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenanc...

What needs improvement with Confluent?

One area we've identified that could be improved is the governance and access control to the Kafka topics. We've found some limitations, like a threshold of 10,000 rules per cluster, that make it c...

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Product Reports

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Apache Flink

November 2024

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November 2024

Also Known As

Flink

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Apache

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.”

Confluent is an enterprise-ready, full-scale streaming platform that enhances Apache Kafka.

Confluent has integrated cutting-edge features that are designed to enhance these tasks:

Speed up application development and connectivity
Enable transformations through stream processing
Streamline business operations at scale
Adhere to strict architectural standards

Confluent is a more complete distribution of Kafka in that it enhances the integration possibilities of Kafka by introducing tools for managing and optimizing Kafka clusters while providing methods for making sure the streams are secure. Confluent supports publish-and-subscribe as well as the storing and processing of data within the streams. Kafka is easier to operate and build thanks to Confluent.

Confluent's software is available in three different varieties:

A free, open-source streaming platform that makes it simple to start using real-time data streams
An enterprise-grade version of the product with more administrative, ops, and monitoring tools
A premium cloud-based version.

Confluent Advantage Features

Confluent has many valuable key features. Some of the most useful ones include:

Multi-language
- Clients: C++, Python, Go, and .NET
- REST proxy: Can connect to Kafka from any connected network device
- Admin REST APIs: RESTful interface for performing administrator operations

Pre-built ecosystem
- Connectors: More than 100 supported connectors, including S3, Elastic, HDFS, JDBC
- MQTT proxy: Gain access to Kafka from MQTT gateways and devices
- Schema registry: Centralized database to guarantee data compatibility

Streaming database
- ksqlDB: Materialized views and real-time stream processing

GUI management
- Control panel: GUI for scalable Kafka management and monitoring
- Health+: Smart alerts and cloud-based control centers

DevOps automation that is flexible
- Confluent for Kubernetes: Complete API to deploy on Kubernetes
- Automated Ansible deployment on non-containerized environments

Dynamic performance
- Self-balancing clusters: Automated partition re-balancing across brokers in the cluster
- Tiered storage: Older Kafka data offloading to object storage with transparent access

Security that is enterprise-grade
- Role-based access control: Granular user/group access authorization
- Audit logs that are structured: Logs of user actions kept in dedicated Kafka topics
- Secret protection: Sensitive information is encrypted

Global resilience
- Linking clusters: A real-time, highly reliable, and consistent bridge across on-premises and cloud environments
- Multiple-region clusters: Single Kafka cluster with automated client failover distributed across multiple data centers
- Replicator: Asynchronous replication that is based on the Kafka Connect framework

Support
- Round the clock enterprise support from Kafka experts

Reviews from Real Users

Confluent stands out among its competitors for a number of reasons. Two major ones are its robust enterprise support and its open source option. PeerSpot users take note of the advantages of these features in their reviews:

Ravi B., a solutions architect at a tech services company, writes of the solution, “KSQL is a valuable feature, as is the Kafka Connect framework for connecting to the various source systems where you need not write the code. We get great support from Confluent because we're using the enterprise version and whenever there's a problem, they support us with fine-tuning and finding the root cause.”

Amit S., an IT consultant, notes, “The biggest benefit is that it is open source. You have the flexibility of opting or not opting for enterprise support, even though the tool itself is open source.” He adds, “The second benefit is it's very modern and built on Java and Scala. You can extend the features very well, and it doesn't take a lot of effort to do so.”

Sample Customers

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

ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost