Apache Spark Streaming vs Starburst Enterprise comparison

Apache and Starburst are both solutions in the Streaming Analytics category. Apache is ranked #10 with an average rating of 8.2, while Starburst is ranked #12 with an average rating of 8.5. Apache holds a 3.4% mindshare in SA, compared to Starburst’s 2.8% mindshare. Additionally, 91% of Apache users are willing to recommend the solution, compared to 100% of Starburst users who would recommend it.

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

2,162 Views
1,579 Comparison Views

91% willing to recommend

Starburst Enterprise

Read 2 Starburst Enterprise reviews

792 Views
701 Comparison Views

100% willing to recommend

Apache Spark Streaming

Starburst Enterprise

Comparison Buyer's Guide

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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: January 2025).

Buyer's Guide

Apache Spark Streaming vs. Starburst Enterprise

January 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 Spark Streaming

Ranking in Streaming Analytics

10th

Average Rating

8.0

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Starburst Enterprise

Ranking in Streaming Analytics

12th

Average Rating

8.6

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Data Science Platforms (14th)

Mindshare comparison

As of January 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.4%, down from 4.3% compared to the previous year. The mindshare of Starburst Enterprise is 2.8%, up from 1.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

AbhishekGupta

Engineering Leader at Walmart

Easy integration, beneficial auto-scaling, and good open-sourced support community

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. Apache Spark Streaming does not have auto-tuning. A customer needs to invest a lot, in terms of management and maintenance.

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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 Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."

"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."

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

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

"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."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

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 initial setup is quite complex."

"The debugging aspect could use some improvement."

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

"We would like to have the ability to do arbitrary stateful functions in Python."

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

"The solution itself could be easier to use."

"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."

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

More Apache Spark Streaming 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

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

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

"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

26%

Computer Software Company

20%

Manufacturing Company

University

Financial Services Firm

43%

Computer Software Company

10%

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 Spark Streaming?

Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.

See all answers

What needs improvement with Apache Spark Streaming?

We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...

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What is your primary use case for Apache Spark Streaming?

We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.

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

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Compared 16% of the time

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Compared 7% of the time

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Compared 29% of the time

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Compared 22% of the time

Starburst Galaxy vs Starburst Enterprise

Compared 21% of the time

Alteryx vs Starburst Enterprise

Compared 14% of the time

Confluent vs Starburst Enterprise

Compared 3% of the time

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Product Reports

Buyer's Guide

Streaming Analytics

January 2025

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Buyer's Guide

Data Science Platforms

January 2025

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Also Known As

Spark Streaming

No data available

Learn More

Apache

Starburst

Overview

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

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.

Sample Customers

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

Information Not Available

Buyer's Guide

Apache Spark Streaming vs. Starburst Enterprise

January 2025

Free Report: Apache Spark Streaming vs. Starburst Enterprise

Find out what your peers are saying about Apache Spark Streaming vs. Starburst Enterprise and other solutions. Updated: January 2025.

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