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: September 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 (4th), Data Science Platforms (1st), Data Management Platforms (DMP) (3rd)

Mindshare comparison

As of September 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 7.0%, down from 14.4% compared to the previous year. The mindshare of Databricks is 7.4%, down from 12.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Databricks	7.4%
Apache Flink	7.0%
Other	85.6%

Streaming Analytics

Featured Reviews

Sanjay Srivastava

Software Architect at IBM

Streaming workflows have improved data integration and support real-time pipelines across platforms

We are not using Apache Flink in its advanced window capabilities. We are using the Apache Flink job in Apache SeaTunnel, meaning we can write the code inside Apache SeaTunnel. Currently, we are moving; both solutions are there. We are doing it on-premises with the help of Kubernetes and OpenShift. The main reason why Apache Flink is better is that it has more functions, and being open source with easy code in Apache SeaTunnel helps us achieve that. Cost is a major issue. I would rate the stability of the product as an eight. For Apache Flink, the final point can be rated an eight. I can recommend Apache Flink to other users for streaming support, and I am recommending it. I would rate this review an eight overall.

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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

"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."

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

"The documentation is very good."

"The end-to-end latency was drastically reduced, and our capability of handling high throughput has increased by using Flink."

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

"The setup was not too difficult."

"We value this solution's intricate system because it comes with a state inside the mechanism and product, allowing us to process batch data, stream to real-time and build pipelines, and we do not need to process data from the beginning when we pause as we can continue from the same point where we stopped, helping us save time as 95% of our pipelines will now be on Amazon and we'll save money by saving time."

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

More Apache Flink pros

"The solution offers a free community version."

"Ability to work collaboratively without having to worry about the infrastructure."

"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."

"This solution offers a lake house data concept that we have found exciting, as we are able to have a large amount of data in a data lake and can manage all relational activities, with all asset complaints properties available to ensure the quality of all data."

"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."

"I work in the data science field and I found Databricks to be very useful."

"It's very simple to use Databricks Apache Spark."

"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."

More Databricks pros

Cons

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

"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."

"Flink has become a lot more stable but the machine learning library is still not very flexible."

"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."

"I am using the Python API and I have found the solution to be underdeveloped compared to others. There needs to be better integration with notebooks to allow for more practical development."

"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."

More Apache Flink cons

"While Databricks is generally a robust solution, I have noticed a limitation with debugging in the Delta Live Table, which could be improved."

"However, the platform could become easier to use."

"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."

"A couple of times I faced an issue where a long-running process was consuming a lot of time and then stopped abruptly."

"Data governance should be addressed. We have some trouble connecting all the governance solutions with Databricks, which means the integrative capabilities are problematic."

"There is room for improvement in the documentation of processes and how it works."

"The product could be improved by offering an expansion of their visualization capabilities, which currently assists in development in their notebook environment."

"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."

More Databricks cons

Pricing and Cost Advice

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

"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."

"The solution requires a subscription."

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

"The pricing depends on the usage itself."

"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 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."

"Price-wise, I would rate Databricks a three out of five."

"I would rate Databricks' pricing seven out of ten."

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

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Retailer

14%

Computer Software Company

Manufacturing Company

Financial Services Firm

16%

Manufacturing Company

10%

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	26
Midsize Enterprise	12
Large Enterprise	60

Questions from the Community

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...

What advice do you have for others considering Apache Flink?

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...

Azure Stream Analytics vs Apache Flink

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Product Reports

Apache Flink

Download Apache Flink product report

Download Databricks product report

Also Known As

Flink

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Overview

Apache Flink is a powerful open-source framework for stateful computations over data streams, designed for both real-time and batch processing. It efficiently handles massive volumes of data with low-latency responses, offering versatility for complex event processing scenarios.

Apache Flink excels in processing high-throughput data streams, enabling seamless state management across distributed applications. Users appreciate its robust features like stateful transformations and checkpointing, simplifying deployment in diverse environments. Though powerful, it poses challenges for beginners due to its complexity and limited documentation, requiring some prior experience to master. Its flexible integration with systems like Kafka and support for Kubernetes on AWS makes it suitable for demanding environments where quick, real-time analysis is essential.

What are the key features of Apache Flink?

Stateful Transformations: Allows complex stateful operations on data streams with precise handling.
Low Latency: Ensures real-time data processing with minimal delays.
Checkpointing: Provides efficient and reliable checkpointing for fault tolerance.
Kafka Integration: Easy integration with Kafka for seamless data ingestion and processing.
API Support: Provides robust APIs for diverse data processing needs.
Flexible Deployment: Offers options for deploying on-premise or in cloud environments.

What benefits should users look for?

Versatility: Supports both batch and stream processing in a unified model.
Community Support: Backed by an active community that continuously enhances its features.
Ease of Use: Simplifies the coding process compared to similar frameworks like Apache Storm.
Real-Time Analytics: Facilitates immediate insights and data-driven decision-making.

Organizations leverage Apache Flink primarily for real-time data processing in sectors such as retail, transportation, and telecommunications. By deploying on AWS with Kubernetes, companies can utilize it for data cleaning, generating customer insights, and providing swift real-time updates. It effectively manages millions of events per second, serving use cases like cab aggregations, map-making, and outlier detection in telecom networks, enabling seamless integration of streaming data with existing pipelines.

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