Apache Spark Streaming vs Starburst Enterprise comparison

Why PeerSpot?

Apache and Starburst are both solutions in the Streaming Analytics category. Apache is ranked #8 with an average rating of 7.9, while Starburst is ranked #24. Apache holds a 4.6% mindshare in SA, compared to Starburst’s 2.2% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

2,888 Views
2,888 Comparison Views

94% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

2,535 Views
1,369 Comparison Views

100% willing to recommend

Apache Spark Streaming

Starburst Enterprise

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Executive SummaryUpdated on Dec 17, 2024

Apache Spark Streaming and Starburst Enterprise are leading solutions in the analytics tools market. Apache Spark Streaming has a slight advantage in terms of cost-effectiveness and support, while Starburst Enterprise leads with advanced features and performance.

Features: Apache Spark Streaming is notable for its real-time data processing, scalability, and ability to manage complex workflows. It integrates easily with various data sources for seamless data management. Starburst Enterprise is characterized by its powerful SQL engine, interoperability with diverse platforms, and advanced security, providing an optimized environment for complex SQL queries.

Ease of Deployment and Customer Service: Apache Spark Streaming ensures efficient deployment across a wide range of environments with strong customer support for smooth integrations. Starburst Enterprise offers tailored deployment options suited to diverse enterprise needs and supports them with premium customer service.

Pricing and ROI: Apache Spark Streaming is cost-effective, appealing to businesses with budget constraints, and offers substantial ROI with its real-time processing capabilities. Starburst Enterprise requires a higher initial investment but provides significant ROI through its performance improvements and advanced query handling.

To learn more, read our detailed Apache Spark Streaming vs. Starburst Enterprise Report (Updated: September 2026).

Buyer's Guide

Apache Spark Streaming vs. Starburst Enterprise

September 2026

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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 Spark Streaming

Ranking in Streaming Analytics

8th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Starburst Enterprise

Ranking in Streaming Analytics

24th

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Data Science Platforms (21st)

Mindshare comparison

As of September 2026, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 4.6%, up from 3.6% compared to the previous year. The mindshare of Starburst Enterprise is 2.2%, down from 3.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Spark Streaming	4.6%
Starburst Enterprise	2.2%
Other	93.2%

Streaming Analytics

Featured Reviews

Khoa Dang Le

Principal AI Engineer at IMT Solutions

Have faced challenges with complex data handling and seek smoother integration for machine learning workflows

I find the fault tolerance feature beneficial because I use it for serving data from a landing area. I understand all of the structures we have for Spark SQL, Spark Streaming, and MLlib. The ability of Apache Spark Streaming to handle out-of-order data using watermarking and windowing is something we use in our pipeline. Nearly 50% of our usage is based on that because we use it for landing data, and we appreciate that we can work with it. The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage. The fast storage capability is crucial because Apache Spark replaces Hadoop's MapReduce, allowing us to manage our data more efficiently.

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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

"It's the fastest solution on the market with low latency data on data transformations."

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"It is the most scalable tool that I have seen before."

"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."

"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."

"With Apache Spark Streaming's integration with Anaconda and Miniconda with Python, I interact with databases using data frames or data sets in micro versions and create solutions based on business expectations for decision-making, logistic regression, linear regression, or machine learning which provides image or voice record and graphical data for improved accuracy."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

"As an open-source solution, using it is basically free."

More Apache Spark Streaming pros

"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."

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

Cons

"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

"The downside is when you have this the other way around in the columns, it becomes really hard to use."

"Monitoring is an area where they could definitely improve Apache Spark Streaming. When you have a streaming application, it generates numerous logs. After some time, the logs become meaningless because they're quite large and impossible to open."

"It was resource-intensive, even for small-scale applications."

"The debugging aspect could use some improvement."

"Integrating event-level streaming capabilities could be beneficial."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

More Apache Spark Streaming cons

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

"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."

Pricing and Cost Advice

"People pay for Apache Spark Streaming as a service."

"I was using the open-source community version, which was self-hosted."

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."

"Spark is an affordable solution, especially considering its open-source nature."

"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

15%

Outsourcing Company

13%

Comms Service Provider

Healthcare Company

Financial Services Firm

32%

Manufacturing Company

Computer Software Company

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

No data available

Questions from the Community

What needs improvement with Apache Spark Streaming?

One of the improvements we need is in Spark SQL and the machine learning library. I don't think there is too much to work on, but the issue is when we want to use machine learning, we always need t...

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What is your primary use case for Apache Spark Streaming?

We work with Apache Spark Streaming for our project because we use that as one of the landing data sources, and we work with it to ensure we can get all of the data before it goes through our data ...

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What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

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Product Reports

Buyer's Guide

Apache Spark Streaming

September 2026

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Buyer's Guide

Data Science Platforms

September 2026

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Also Known As

Spark Streaming

No data available

Overview

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

Apache

Starburst Enterprise optimizes data processing for businesses, offering a robust platform tailored for efficient data handling. Ideal for tech-savvy audiences, it powers seamless data analysis and management.

Starburst Enterprise provides an advanced infrastructure that simplifies querying massive data sets from a variety of sources. Its integration capabilities allow users to access and analyze data without extensive data movement, ensuring cost-effective operations and speedy insights. Businesses can leverage comprehensive data analytics strategies, significantly enhancing their decision-making processes while minimizing latency.

What are the key features of Starburst Enterprise?

Data Federation: Connects disparate data sources without consolidation.
High-Performance Query: Delivers fast, interactive SQL analytics even on diverse data sets.
Security and Access Control: Ensures data is protected with robust security protocols and user management systems.
Scalability: Capable of handling increasing workloads efficiently.

What benefits and ROI should users look for?

Cost Efficiency: Reduces data warehousing costs by querying data at its source.
Time Savings: Accelerates data access and query responses, improving productivity.
Scalability: Supports growing data needs effortlessly, reducing infrastructure investments.

In industries like finance and retail, Starburst Enterprise is implemented to streamline big data operations, enhance customer experiences, and facilitate better risk management. Its ability to integrate with existing infrastructures allows for seamless adoption into company operations, delivering substantial analytical advantages.

Starburst

Sample Customers

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.

Information Not Available

Buyer's Guide

Apache Spark Streaming vs. Starburst Enterprise

September 2026

Free Report: Apache Spark Streaming vs. Starburst Enterprise

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