Apache Flink vs Databricks comparison

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Apache Flink

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Executive SummaryUpdated on Dec 17, 2024

Databricks and Apache Flink compete in the big data and machine learning space. Databricks seems to have the upper hand due to its seamless cloud integration and user-friendly interface, while Apache Flink has strengths in real-time streaming but requires more technical expertise.

Features: Databricks offers extensive features such as scalability, ease of use, and robust collaboration options with shared workspaces and notebooks. It supports multiple programming languages and integrates well with Azure, making it suitable for advanced analytics and data governance. Apache Flink excels in real-time and batch processing with its stateful computations and low latency. Its checkpointing feature supports failure recovery, making it ideal for real-time analytics and streaming data processing.

Room for Improvement: Databricks could improve its integration with coding IDEs, enhance data governance, and offer better price clarity. Its initial setup process could be simplified for non-data scientists. Apache Flink needs better integration with Python, improved documentation, and more user-friendly reporting and infrastructure management.

Ease of Deployment and Customer Service: Databricks is strong in public and hybrid cloud environments, offering comprehensive support channels but with occasional delays. Apache Flink requires more technical expertise for deployment and lacks detailed customer support feedback, indicating a need for improved accessibility and guidance.

Pricing and ROI: Databricks uses a pay-as-you-go model, potentially expensive when scaling, but offers good ROI through its usability and time efficiency. Apache Flink, as an open-source solution, provides significant cost savings with no licensing fees, making it appealing for budget-conscious projects with its effective real-time data processing capabilities.

To learn more, read our detailed Apache Flink vs. Databricks Report (Updated: March 2026).

Apache Flink vs. Databricks

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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

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (6th), Data Science Platforms (1st), Data Management Platforms (DMP) (5th)

Mindshare comparison

As of March 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 10.9%, down from 12.5% compared to the previous year. The mindshare of Databricks is 9.0%, down from 14.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Databricks	9.0%
Apache Flink	10.9%
Other	80.1%

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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SimonRobinson

Governance And Engagement Lead

Improved data governance has enabled sensitive data tracking but cost management still needs work

I believe we could improve Databricks integration with cloud service providers. The impact of our current integration has not been particularly good, and it's becoming very expensive for us. The inefficiencies in our implementation, such as not shutting down warehouses when they're not in use or reserving the right number of credits, have led to increased costs. We made several beginner mistakes, such as not taking advantage of incremental loading and running overly complicated queries all the time. We should be using ETL tools to help us instead of doing it directly in Databricks. We need more experienced professionals to manage Databricks effectively, as it's not as forgiving as other platforms such as Snowflake. I think introducing customer repositories would facilitate easier implementation with Databricks.

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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

"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 documentation is very good."

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

"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."

"What I appreciate best about Apache Flink is that it's open source and geared towards a distributed stream processing framework."

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

"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."

More Apache Flink pros

"It's easy to increase performance as required."

"The initial setup phase of Databricks was good."

"I like cloud scalability and data access for any type of user."

"It offers AI functionalities that assist with code management and machine learning processes."

"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."

"Databricks has a Unified Catalog that assists with secured access and governance."

"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."

"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."

More Databricks pros

Cons

"There are more libraries that are missing and also maybe more capabilities for machine learning."

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

"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."

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

"The solution could be more user-friendly."

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

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

More Apache Flink cons

"We often use a single cluster to ingest Databricks, which Databricks doesn't recommend. They suggest using a no-cluster solution like job clusters. This can be overwhelming for us because we started smaller."

"It would be very helpful if Databricks could integrate with platforms in addition to Azure."

"The biggest problem associated with the product is that it is quite pricey."

"Anyone who doesn't know SQL may find the product difficult to work with."

"In the next release, I would like to see more optimization features."

"I have seen better user interfaces, so that is something that can be improved."

"There should be better integration with other platforms."

"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."

More Databricks cons

Pricing and Cost Advice

"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 solution."

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

"It's an open source."

"We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."

"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

"We only pay for the Azure compute behind the solution."

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

"The price is okay. It's competitive."

"The solution is a good value for batch processing and huge workloads."

"The price of Databricks is reasonable compared to other solutions."

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Retailer

13%

Computer Software Company

10%

Manufacturing Company

Financial Services Firm

17%

Manufacturing Company

Computer Software 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	12

By reviewers
Company Size	Count
Small Business	27
Midsize Enterprise	12
Large Enterprise	56

Questions from the Community

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 could improve Apache Flink by providing more functionality, as they need to fully support data integration. The connectors are still very few for Apache Flink. There is a lack of functionali...

What is your primary use case for Apache Flink?

I am working with Apache Flink, which is the tool we use for data integration. Apache Flink is for data, and we are working on the data integration project, not big data, using Apache Flink and Apa...

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

Spring Cloud Data Flow vs Apache Flink

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Product Reports

Apache Flink

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Also Known As

Flink

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

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

Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.

Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.

What features make Databricks unique?

Notebook: Enables collaborative work among team members.
Delta Lake: Optimizes data management operations.
Unity Catalog: Provides governance over data assets.
Cloud Integration: Seamlessly connects with major cloud platforms.

What benefits can users expect from Databricks?

Versatility: Supports diverse applications in data science and engineering.
Performance: Delivers efficient handling of large-scale analytics tasks.
Collaboration: Enhances teamwork in data projects.
Unified Environment: Centralizes machine learning and analytics activities.

In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.

Sample Customers

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

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

Apache Flink vs. Databricks