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: June 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) (5th)

Mindshare comparison

As of June 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 8.2%, down from 13.7% compared to the previous year. The mindshare of Databricks is 7.9%, down from 14.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Databricks	7.9%
Apache Flink	8.2%
Other	83.9%

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

"The setup was not too difficult."

"The documentation is very good."

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

"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 provides faster and low-cost investment for me; I find it to have low hardware requirements, and it's faster with low code, meaning it's easy to understand for moving the streaming data."

"The main advantage is the turnaround time, which has been reduced drastically because of Apache Flink, and now everything is in almost real time with no waiting or lag of data in the application while machine resources are utilized much more efficiently."

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

"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

"The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster."

"The initial setup phase of Databricks was good."

"Databricks integrates well with other solutions."

"Easy to use and requires minimal coding and customizations."

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

"Scalability is one of the main features of Databricks."

"Data is open to everyone; they can access it through many channels, including notebooks or SQL."

"Databricks is a one-stop shop for everything data related, and it can scale with you."

More Databricks pros

Cons

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

"The solution could be more user-friendly."

"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."

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

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

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

"The technical support from Apache is not good; support needs to be improved. I would rate them from one to ten as not good."

"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."

More Apache Flink cons

"The API deployment and model deployment are not easy on the Databricks side."

"The integration features could be more interesting, more involved."

"I think setting up the whole account for one person and giving access are areas that can be difficult to manage and should be made a little easier."

"In my opinion, areas of Databricks that have room for improvement involve the dashboards. Until recently, everyone used third-party systems such as Power BI to connect to Databricks for dashboards and reports, but they're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further."

"However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms."

"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."

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

"The product needs samples and templates to help invite users to see results and understand what the product can do."

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."

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

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

"It's an open source."

"We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is."

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

"The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts."

"There are different versions."

"I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."

"The solution is affordable."

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

Manufacturing Company

Financial Services Firm

18%

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	27
Midsize Enterprise	12
Large Enterprise	57

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 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