Databricks vs Upsolver comparison

Databricks and Upsolver are both solutions in the Streaming Analytics category. Databricks is ranked #1 with an average rating of 8.3, while Upsolver is ranked #20 with an average rating of 8.3. Databricks holds a 9.5% mindshare in SA, compared to Upsolver’s 0.9% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Databricks

Read 93 Databricks reviews

18,865 Views
3,396 Comparison Views

96% willing to recommend

Upsolver

Read 3 Upsolver reviews

772 Views
162 Comparison Views

100% willing to recommend

Databricks

Upsolver

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Databricks and Upsolver based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Databricks vs. Upsolver Report (Updated: December 2025).

Buyer's Guide

Databricks vs. Upsolver

December 2025

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Helped 881,707 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

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (9th), Data Science Platforms (1st), Data Management Platforms (DMP) (5th)

Upsolver

Ranking in Streaming Analytics

20th

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Data Integration (38th)

Mindshare comparison

As of February 2026, in the Streaming Analytics category, the mindshare of Databricks is 9.5%, down from 14.1% compared to the previous year. The mindshare of Upsolver is 0.9%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Databricks	9.5%
Upsolver	0.9%
Other	89.6%

Streaming Analytics

Featured Reviews

Satyam Wagh

Consultant at Nice Software Solutions

Unified data workflows have cut ticket processing times and are driving faster business insights

Databricks already provides monthly updates and continuously works on delivering new features while enhancing existing ones. However, the platform could become easier to use. While instruction-led workshops are available, offering more free instructional workshops would allow a wider audience to access and learn about Databricks. Additionally, providing use cases would help beginners gain more knowledge and hands-on experience. Regarding my experience, I was initially unfamiliar with the platform and had to conduct research and learn through various videos. I did find some instruction-led classes, but several of those required payment. The platform should provide more free resources to enable a broader audience to access and learn about Databricks. The platform itself is user-friendly and easy to use without complex issues, so I believe it does not need improvement in its core functionality. Rather, supporting aspects can be enhanced.

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reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

Streaming pipelines have become simpler and onboarding new data sources is now much faster

One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

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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

"Databricks serves as a single platform for conducting the entire end-to-end lifecycle of machine learning models or AI ops."

"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."

"The solution offers a free community version."

"The setup was straightforward."

"The technical support is good."

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

"The most valuable feature is the Spark cluster which is very fast for heavy loads, big data processing and Pi Spark."

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

More Databricks pros

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."

"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

Cons

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

"There could be more support for automated machine learning in the database. I would like to see more ways to do analysis so that the reporting is more understandable."

"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."

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

"The initial setup is difficult."

"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."

"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."

"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."

More Databricks cons

"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."

"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

"There is room for improvement in query tuning."

Pricing and Cost Advice

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

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

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

"The pricing depends on the usage itself."

"The solution requires a subscription."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"I would rate the tool’s pricing an eight out of ten."

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

More Databricks pricing and cost advice

"Upsolver is affordable at approximately $225 per terabyte per year."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Manufacturing Company

Computer Software Company

Healthcare Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	26
Midsize Enterprise	12
Large Enterprise	56

No data available

Questions from the Community

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 ...

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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...

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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...

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What is your experience regarding pricing and costs for Upsolver?

My experience with pricing, setup cost, and licensing was a very good experience, but it is not a direct experience because it was not my responsibility. It was in charge of the customer. However, ...

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What needs improvement with Upsolver?

I think that Upsolver can be improved in orchestration because it is not a full orchestration tool. I believe it could be better in this regard. The cost needs attention at a very large scale. I th...

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What is your primary use case for Upsolver?

My main use case for Upsolver is during an IT consulting project for a large enterprise running a cloud-native data platform on AWS. I used Upsolver to ingest and process high-volume stream data fr...

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Comparisons

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Compared 6% of the time

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Compared 5% of the time

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Compared 4% of the time

Informatica PowerCenter vs Databricks

Compared 4% of the time

Alteryx vs Databricks

Compared 3% of the time

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Alteryx Designer vs Upsolver

Compared 16% of the time

SAS Access vs Upsolver

Compared 14% of the time

Rivery vs Upsolver

Compared 14% of the time

Informatica PowerCenter vs Upsolver

Compared 12% of the time

Striim vs Upsolver

Compared 12% of the time

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Product Reports

Buyer's Guide

Databricks

January 2026

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Buyer's Guide

Data Integration

January 2026

Download Upsolver product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Overview

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.

Databricks

Upsolver's mission is to turn engineering-intensive data lakes into easy-to-use repositories that serve 50X more data professionals compared to the status-quo today. Upsolver cuts 95% from the tedious process of preparing event data for analytics and machine learning, using a visual SQL-based interface, a cloud-native data platform and deep technology for stream-processing, indexing and eventually-consistent file systems.

Upsolver

Sample Customers

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

Information Not Available

Buyer's Guide

Databricks vs. Upsolver

December 2025

Free Report: Databricks vs. Upsolver

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