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: September 2026).

Apache Spark Streaming vs. Databricks

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

7th

Average Rating

7.8

Reviews Sentiment

6.4

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 (3rd), Data Science Platforms (1st), Data Management Platforms (DMP) (3rd)

Mindshare comparison

As of October 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 Databricks is 7.4%, down from 12.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Databricks	7.4%
Apache Spark Streaming	4.6%
Other	88.0%

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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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

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

"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."

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

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

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

"For Apache Spark Streaming, the feature I appreciated most is that it provides live data delivery; additionally, it provides the capability to send a larger amount of data in parallel."

"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."

"By integrating Apache Spark Streaming, the data freshness rate, and latency have significantly improved from 24-hour batch processing to less than one minute, facilitating faster communication to downstream systems, aiding marketing campaigns."

More Apache Spark Streaming pros

"Databricks is definitely a very stable product and reliable."

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

"I haven't heard about any major stability issues. At this time I feel like it's stable."

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

"The most valuable feature is the versatility of the ecosystem."

"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search."

"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."

"Databricks is a unified platform that provides features like streaming and batch processing so all the data scientists, analysts, and engineers can collaborate on a single platform and it has all the features you need, so you don't need to go for any other tool."

More Databricks pros

Cons

"The debugging aspect could use some improvement."

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

"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."

"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."

"The initial setup is quite complex."

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

"While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable."

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

More Apache Spark Streaming cons

"In the future, I would like to see Data Lake support. That is something that I'm looking forward to."

"Databricks has a lack of debuggers, and it would be good to see more components."

"The solution is not exactly stable. We've faced a few bugs which have really affected it, especially when it comes to connecting with Spark."

"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."

"The first deployment is difficult. It is not straightforward and you have to think about a lot of stuff."

"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."

"For a small workload, Databricks may not be worth the costs."

"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."

"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."

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

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

"My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month."

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

"Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."

"The basic version of this solution is now open-source, so there are no license costs involved. However, there is a charge for any advanced functionality and this can be quite expensive."

"Databricks are not costly when compared with other solutions' prices."

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

More Databricks pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Outsourcing Company

14%

Comms Service Provider

Marketing Services Firm

Financial Services Firm

16%

Manufacturing Company

10%

Outsourcing Company

Healthcare 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

By reviewers
Company Size	Count
Small Business	26
Midsize Enterprise	12
Large Enterprise	60

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...

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 ...

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...

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...

Azure Stream Analytics vs Apache Spark Streaming

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Product Reports

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Apache Spark Streaming

October 2026

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October 2026

Also Known As

Spark Streaming

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

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

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

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

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