Apache Spark Streaming vs Databricks comparison

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Apache Spark Streaming

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Executive SummaryUpdated on Dec 17, 2024

Databricks and Apache Spark Streaming compete in the data analytics and machine learning space. Databricks holds an advantage with its comprehensive cloud integration and built-in optimizations, while Apache Spark Streaming excels in open-source, real-time data processing.

Features: Databricks is favored for its built-in optimization and Delta data format, which enhances performance. It offers seamless integration with Spark and Python, making it ideal for machine learning and big data. Its flexibility in supporting multiple programming languages also makes it attractive. Apache Spark Streaming is notable for its real-time data processing capabilities and low-latency performance. Its versatility and open-source nature with Python support are key highlights.

Room for Improvement: Databricks needs to enhance its visualization capabilities and expand integration options. There is also a need to expand its machine learning features and improve user interfaces for non-technical users. Apache Spark Streaming could improve its memory management and real-time analytics capabilities. Enhancements in event-level integration and interface user-friendliness are needed.

Ease of Deployment and Customer Service: Databricks provides deployment across public and private clouds with robust technical support, although response times could be better. Microsoft support is available as part of enterprise solutions. Apache Spark Streaming is typically deployed in public clouds, where documentation often suffices, but open-source community support varies in availability and responsiveness.

Pricing and ROI: Databricks is seen as expensive, particularly for non-batch applications, but offers significant ROI through scalability and integration. Its comprehensive feature set justifies the cost. Apache Spark Streaming, being open-source, offers a more affordable solution with expenses mainly associated with cloud use and optional commercial support, resulting in higher ROI due to lower initial costs.

To learn more, read our detailed Apache Spark Streaming vs. Databricks Report (Updated: June 2025).

Apache Spark Streaming 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 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

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (8th), Data Science Platforms (1st)

Mindshare comparison

As of June 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 2.7%, down from 3.8% compared to the previous year. The mindshare of Databricks is 14.5%, up from 10.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Oscar Estorach

Chief Data-strategist and Director at Theworkshop.es

Versatile and flexible when dealing with large-scale data streams

What I like about Spark is its versatility in supporting multiple languages and that makes it my preferred choice for building scalable and efficient systems, whether it is hooking databases with web applications or handling large-scale data transformations. Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows. It works well in the cloud, and you can structure data using Databricks or Spark, providing flexibility for different projects. Spark Streaming's flexibility shines when dealing with large-scale data streams. It caters to different needs, offering real-time insights for tasks like online sales analytics. The ability to prioritize data streams is valuable, especially for monitoring competitor prices online.

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ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

Capability to integrate diverse coding languages in a single notebook greatly enhances workflow

Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.

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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 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."

"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."

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

"The solution is very stable and reliable."

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

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

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

More Apache Spark Streaming pros

"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."

"The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions."

"Databricks offers various courses that I can use, whether it's PySpark, Scala, or R."

"The simplicity of development is the most valuable feature."

"The most valuable feature of Databricks is the notebook, data factory, and ease of use."

"Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."

"Databricks has a Unified Catalog that assists with secured access and governance."

"We can scale the product."

More Databricks pros

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."

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

"In terms of improvement, the UI could be better."

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

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

"The initial setup is quite complex."

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

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

More Apache Spark Streaming cons

"Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage."

"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."

"In the next release, I would like to see more optimization features."

"There has been a significant evolution in databases. One area of improvement is the Databricks File System (DBFS), where command-line challenges arise when accessing files."

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

"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."

"There are no direct connectors — they are very limited."

"Databricks' technical support takes a while to respond and could be improved."

More Databricks cons

Pricing and Cost Advice

"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."

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

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

"The solution requires a subscription."

"The solution is based on a licensing model."

"The licensing costs of Databricks is a tiered licensing regime, so it is flexible."

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

"We only pay for the Azure compute behind the solution."

"Databricks uses a price-per-use model, where you can use as much compute as you need."

"The billing of Databricks can be difficult and should improve."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

21%

Manufacturing Company

University

Financial Services Firm

18%

Computer Software Company

10%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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.

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...

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.

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 Spark Streaming

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Product Reports

Streaming Analytics

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Also Known As

Spark Streaming

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Overview

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

Apache

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Sample Customers

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

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

Apache Spark Streaming vs. Databricks