Apache Spark Streaming vs Databricks comparison

Read 91 Databricks reviews

23,965 Views
4,314 Comparison Views

96% willing to recommend

Apache Spark Streaming

Comparison Buyer's Guide

Download the report

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: July 2025).

Apache Spark Streaming vs. Databricks

July 2025

Download the complete report

Helped 864,574 peers since 2012

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

11th

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 August 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.1%, down from 3.7% compared to the previous year. The mindshare of Databricks is 13.5%, up from 11.8% 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.

Read full review

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

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

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

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

"The solution is better than average and some of the valuable features include efficiency and stability."

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

"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."

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

More Apache Spark Streaming pros

"Having one solution for everything, from data engineering to machine learning, is beneficial since everything comes under one hood."

"The solution is built from Spark and has integration with MLflow, which is important for our use case."

"The initial setup phase of Databricks was good."

"It offers AI functionalities that assist with code management and machine learning processes."

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

"Databricks is a robust solution for big data processing, offering flexibility and powerful features."

"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."

"Databricks integrates well with other solutions."

More Databricks pros

Cons

"The solution itself could be easier to use."

"The initial setup is quite complex."

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

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

"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 don't have enough experience to be judgmental about its flaws."

"The debugging aspect could use some improvement."

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

More Apache Spark Streaming cons

"In my opinion, areas of Databricks that have room for improvement involve the dashboards. Until recently, everyone used third-party systems such as Power BI to connect to Databricks for dashboards and reports, but they're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further."

"It would be very helpful if Databricks could integrate with platforms in addition to Azure."

"This solution only supports queries in SQL and Python, which is a bit limiting."

"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."

"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."

"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."

"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."

"I think setting up the whole account for one person and giving access are areas that can be difficult to manage and should be made a little easier."

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."

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

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

"The solution requires a subscription."

"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."

"The cost is around $600,000 for 50 users."

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

"I rate the price of Databricks as eight out of ten."

"The solution is affordable."

"We're charged on what the data throughput is and also what the compute time is."

"Databricks' cost could be improved."

More Databricks pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

864,574 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Computer Software Company

23%

Financial Services Firm

23%

University

Healthcare Company

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

Comparisons

Compared 15% of the time

Azure Stream Analytics vs Apache Spark Streaming

Compared 10% of the time

Informatica Data Engineering Streaming vs Apache Spark Streaming

Compared 10% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 9% of the time

Talend Data Streams vs Apache Spark Streaming

Compared 5% of the time

More Apache Spark Streaming Competitors

Dataiku vs Databricks

Compared 10% of the time

Microsoft Power BI vs Databricks

Compared 9% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Dremio vs Databricks

Compared 6% of the time

Tableau Enterprise vs Databricks

Compared 5% of the time

More Databricks Competitors

Product Reports

Download Apache Spark Streaming product report

Streaming Analytics

July 2025

Download Databricks product report

August 2025

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