Apache Flink vs Starburst Enterprise comparison

Apache and Starburst are both solutions in the Streaming Analytics category. Apache is ranked #5 with an average rating of 7.8, while Starburst is ranked #15 with an average rating of 8.5. Apache holds a 14.5% mindshare in SA, compared to Starburst’s 3.6% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Apache Flink

Read 18 Apache Flink reviews

3,972 Views
3,972 Comparison Views

94% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

1,367 Views
793 Comparison Views

100% willing to recommend

Apache Flink

Starburst Enterprise

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Apache Flink and Starburst Enterprise are competing in the real-time data processing and analytics category. Apache Flink has an edge in stream processing, whereas Starburst Enterprise excels in data integration and analytics flexibility.

Features: Apache Flink's notable features include stateful computation, strong event processing capabilities, and support for complex event-driven applications. Starburst Enterprise offers comprehensive data exploration, integration with diverse data platforms, and a high-performance distributed query engine.

Ease of Deployment and Customer Service: Apache Flink provides flexible deployment options across cloud and on-premises environments, focusing on containerized deployment strategies, with community-based support channels. Starburst Enterprise offers a user-friendly setup with excellent customer support, ensuring smooth deployments and comprehensive technical assistance.

Pricing and ROI: Apache Flink typically offers lower setup costs, providing strong ROI for streaming applications but might need significant customization for particular use cases. Starburst Enterprise generally involves higher initial costs, but its robust analytics capabilities and resource management tools lead to substantial long-term ROI, appealing to enterprises with strategic data initiatives.

To learn more, read our detailed Apache Flink vs. Starburst Enterprise Report (Updated: July 2025).

Buyer's Guide

Apache Flink vs. Starburst Enterprise

July 2025

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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

5th

Average Rating

7.8

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Starburst Enterprise

Ranking in Streaming Analytics

15th

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Data Science Platforms (12th)

Mindshare comparison

As of August 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 14.5%, up from 9.9% compared to the previous year. The mindshare of Starburst Enterprise is 3.6%, up from 1.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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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KamleshPant

Senior Software Architect at USEReady

Connects to any data source from any region and offers unified access

There are no specific projects supported by Starburst regarding AI initiatives or machine learning projects. In the future, if we have all the data available, we can definitely capitalize on AI/ML and LLM capabilities to summarize data and gain insights. That's our future goal, but we haven't reached that point yet. There should be support for REST API data sources to access data from the web. We often have data coming in and communicate with data sources via REST API calls. I don't see that capability in Starburst currently; everything is through JDBC or ODBC. If Starburst could seamlessly access data using REST API capabilities, it would be a game-changer. The self-service data management features, like self-service materialized views, are great, but they can be a bit complex for basic users to understand.

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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

"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."

"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."

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

"It is user-friendly and the reporting is good."

"The setup was not too difficult."

"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 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. We use Apache Flink to control our clients' installations."

More Apache Flink pros

"It's very scalable, fast performing, and supports many catalogs."

"We have noticed improvements in performance using Starburst Enterprise. It handles complex data, including reading and partitioning files. We can add a new catalog to Starburst Enterprise by providing connection details and service account information. This allows us to integrate with existing tools, such as the Snowflake database, which we use for data protection in our project."

Cons

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

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

"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."

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

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

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

"The solution could be more user-friendly."

More Apache Flink cons

"Starburst Enterprise could improve by offering additional features similar to those provided by other SQL query tools. For example, incorporating functionalities like pivot tables would make it more feasible to use."

"There should be support for REST API data sources to access data from the web."

Pricing and Cost Advice

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

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

"It's an open-source solution."

"It's an open source."

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

"I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of using Starburst Enterprise can vary based on the amount of data you're processing and the type of machines you opt for, whether on AWS or another cloud platform."

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Computer Software Company

12%

Retailer

Manufacturing Company

Financial Services Firm

43%

Computer Software Company

Energy/Utilities Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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. ...

See all answers

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.

See all answers

What needs improvement with Apache Flink?

Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool. There is a need for increased awareness and education, especially around best ...

See all answers

What is your experience regarding pricing and costs for Starburst Enterprise?

I haven't personally dealt with the pricing aspects first-hand, but from what I understand, it largely depends on the specifics of your setup, especially the machines you use on AWS. The cost of us...

See all answers

What needs improvement with Starburst Enterprise?

See all answers

What is your primary use case for Starburst Enterprise?

We use Starburst with one client who is exploring their ecosystem to remove data silos and enable data access across departments. It's a very big ecosystem, like a finance institute. They are curre...

See all answers

Comparisons

Spring Cloud Data Flow vs Apache Flink

Compared 21% of the time

Databricks vs Apache Flink

Compared 16% of the time

Amazon Kinesis vs Apache Flink

Compared 10% of the time

Azure Stream Analytics vs Apache Flink

Compared 9% of the time

Google Cloud Dataflow vs Apache Flink

Compared 8% of the time

More Apache Flink Competitors

Databricks vs Starburst Enterprise

Compared 42% of the time

Starburst Galaxy vs Starburst Enterprise

Compared 14% of the time

Dremio vs Starburst Enterprise

Compared 13% of the time

Alteryx vs Starburst Enterprise

Compared 7% of the time

Dataiku vs Starburst Enterprise

Compared 3% of the time

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Product Reports

Buyer's Guide

Apache Flink

August 2025

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Buyer's Guide

Data Science Platforms

August 2025

Download Starburst Enterprise product report

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

Starburst Enterprise is a data analytics platform that enables organizations to access and analyze data from multiple sources, including cloud-based and on-premises data warehouses. It provides a single access point to all data sources, allowing users to query and analyze data without moving it between systems.

By providing a unified view, Starburst Enterprise helps organizations make better-informed decisions and improve operational efficiency, leading to better customer insights and more accurate forecasting. Overall, Starburst Enterprise is a powerful tool for organizations looking to unlock the full potential of their data.

Starburst

Sample Customers

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

Information Not Available

Buyer's Guide

Apache Flink vs. Starburst Enterprise

July 2025

Free Report: Apache Flink vs. Starburst Enterprise

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