Databricks vs Upsolver comparison

Databricks and Upsolver are both solutions in the Streaming Analytics category. Databricks is ranked #1 with an average rating of 8.2, while Upsolver is ranked #20 with an average rating of 8.0. Databricks holds a 14.3% mindshare in SA, compared to Upsolver’s 0.2% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Databricks

Read 84 Databricks reviews

7,296 Views
4,867 Comparison Views

96% willing to recommend

Upsolver

Read 1 Upsolver review

111 Views
96 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 what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics.

To learn more, read our detailed Streaming Analytics Report (Updated: December 2024).

Buyer's Guide

Streaming Analytics

December 2024

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Categories and Ranking

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Data Science Platforms (1st)

Upsolver

Ranking in Streaming Analytics

20th

Average Rating

8.0

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Data Integration (40th)

Mindshare comparison

As of December 2024, in the Streaming Analytics category, the mindshare of Databricks is 14.3%, up from 9.9% compared to the previous year. The mindshare of Upsolver is 0.2%, down from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Process large-scale data sets and integrates with Apache Spark with notebook environment

Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of its strengths. Another strength is that the platform makes it very easy to manage resources. For example, setting up a cluster of five or fifteen nodes is straightforward with Databricks. The notebook environment is also excellent, making it easy to perform various tasks.

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Kireet Kokala

Technical VP Consultant at Paradigm / nClouds

Provides ETL tools with stability at a competitive price

It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality. Some customization was available, and I attended developer training that allowed me to explore it in more depth. At the time of my evaluation, features like iceberg tables were not present on the platform but are now included based on your current website. It was easier to use than the competition. I liked the real-time ingestion and transformation features. The wizard-based guidance was particularly notable.

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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

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

"The processing capacity is tremendous in the database."

"The setup was straightforward."

"The solution is an impressive tool for data migration and integration."

"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."

"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."

"It is fast, it's scalable, and it does the job it needs to do."

"The tool helps with data processing and analytics with large-scale data or big data since it is associated with managing data at a large scale."

More Databricks pros

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

Cons

"The integration and query capabilities can be improved."

"It's not easy to use, and they need a better UI."

"The solution has some scalability and integration limitations when consolidating legacy systems."

"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."

"Doesn't provide a lot of credits or trial options."

"Implementation of Databricks is still very code heavy."

"Pricing is one of the things that could be improved."

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

More Databricks cons

"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."

Pricing and Cost Advice

"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."

"The solution requires a subscription."

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

"There are different versions."

"I would rate Databricks' pricing seven out of ten."

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

"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."

"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."

More Databricks pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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?

It was competitively priced and within the customer's budget.

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What needs improvement with Upsolver?

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 aggregat...

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What is your primary use case for Upsolver?

When I test-drove Upsolver for a consulting company, I used it in POC to stream and ingest data. The goal was to move data from a source, possibly SQL Server, into a destination like Snowflake or R...

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Comparisons

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Compared 11% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 7% of the time

More Databricks Competitors

No data available

Product Reports

Buyer's Guide

Databricks

December 2024

Download Databricks product report

Buyer's Guide

Data Integration

December 2024

Download Upsolver product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

No data available

Learn More

Databricks

Upsolver

Overview

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.

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.

Sample Customers

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

Information Not Available

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Confluent and others in Streaming Analytics. Updated: December 2024.

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