Databricks vs Upsolver comparison

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45,847 views|34,080 comparisons
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395 views|356 comparisons
Comparison Buyer's Guide
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, Microsoft, Confluent and others in Streaming Analytics.
To learn more, read our detailed Streaming Analytics Report (Updated: November 2022).
655,711 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pricing and Cost Advice
  • "I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."
  • "The price is okay. It's competitive."
  • "Databricks uses a price-per-use model, where you can use as much compute as you need."
  • "There are different versions."
  • "The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."
  • "The solution requires a subscription."
  • "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."
  • "We pay as we go, so there isn't a fixed price. It's charged by the unit. I don't have any details detail about how they measure this, but it should be a mix between processing and quantity of data handled. We run a simulation based on our use cases, which gives us an estimate. We've been monitoring this, and the costs have met our expectations."
  • More Databricks Pricing and Cost Advice →

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    Questions from the Community
    Top Answer: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… more »
    Top Answer: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… more »
    Top Answer: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… more »
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    Ranking
    1st
    out of 38 in Streaming Analytics
    Views
    45,847
    Comparisons
    34,080
    Reviews
    39
    Average Words per Review
    450
    Rating
    8.2
    21st
    out of 38 in Streaming Analytics
    Views
    395
    Comparisons
    356
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    Comparisons
    Also Known As
    Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
    Learn More
    Overview

    Databricks is an industry-leading data analytics platform which is a one-stop product for all data requirements. Databricks is made by the creators of Apache Spark, Delta Lake, ML Flow, and Koalas. It builds on these technologies to deliver a true lakehouse data architecture, making it a robust platform that is reliable, scalable, and fast. Databricks speeds up innovations by synthesizing storage, engineering, business operations, security, and data science.

    Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform. This enables users to easily manage a colossal amount of data and to continuously train and deploy machine learning models for AI applications. The platform handles all analytic deployments, ranging from ETL to models training and deployment.

    Databricks deciphers the complexities of processing data to empower data scientists, engineers, and analysts with a simple collaborative environment to run interactive and scheduled data analysis workloads. The program takes advantage of AI’s cost-effectivity, flexibility, and cloud storage.

    Databricks Key Features

    Some of Databricks key features include:

    • Cloud-native: Works well on any prominent cloud provider.
    • Data storage: Stores a broad range of data, including structured, unstructured, and streaming.
    • Self-governance: Built-in governance and security controls.
    • Flexibility: Flexible for small-scale jobs as well as running large-scale jobs like Big Data processing because it’s built from Spark and is specifically optimized for Cloud environments.
    • Data science tools: Production-ready data tooling, from engineering to BI, AI, and ML.
    • Familiar languages: While Databricks is Spark-based, it allows commonly used programming languages like R, SQL, Scala, and Python to be used.
    • Team sharing workspaces: Creates an environment that provides interactive workspaces for collaboration, which allow multiple members to collaborate for data model creation, machine learning, and data extraction.
    • Data source: Performs limitless Big Data analytics by connecting to Cloud providers AWS, Azure, and Google, as well as on-premises SQL servers, JSON and CSV.

    Reviews from Real Users

    Databricks stands out from its competitors for several reasons. Two striking features are its collaborative ability and its ability to streamline multiple programming languages.

    PeerSpot users take note of the advantages of these features. A Chief Research Officer in consumer goods writes, “We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for.”

    A business intelligence coordinator in construction notes, “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.”

    An Associate Manager who works in consultancy mentions, “The technology that allows us to write scripts within the solution is extremely beneficial. If I was, for example, able to script in SQL, R, Scala, Apache Spark, or Python, I would be able to use my knowledge to make a script in this solution. It is very user-friendly and you can also process the records and validation point of view. The ability to migrate from one environment to another is useful.”

    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.

    Offer
    Learn more about Databricks
    Learn more about Upsolver
    Sample Customers
    Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
    Information Not Available
    Top Industries
    REVIEWERS
    Financial Services Firm20%
    Retailer12%
    Computer Software Company12%
    Mining And Metals Company8%
    VISITORS READING REVIEWS
    Computer Software Company17%
    Financial Services Firm12%
    Comms Service Provider8%
    Manufacturing Company7%
    VISITORS READING REVIEWS
    Computer Software Company29%
    Comms Service Provider10%
    Financial Services Firm8%
    Insurance Company6%
    Company Size
    REVIEWERS
    Small Business26%
    Midsize Enterprise15%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise11%
    Large Enterprise73%
    VISITORS READING REVIEWS
    Small Business23%
    Midsize Enterprise18%
    Large Enterprise59%
    Buyer's Guide
    Streaming Analytics
    November 2022
    Find out what your peers are saying about Databricks, Microsoft, Confluent and others in Streaming Analytics. Updated: November 2022.
    655,711 professionals have used our research since 2012.

    Databricks is ranked 1st in Streaming Analytics with 39 reviews while Upsolver is ranked 21st in Streaming Analytics. Databricks is rated 8.2, while Upsolver is rated 0.0. The top reviewer of Databricks writes "Good integration with majority of data sources through Databricks Notebooks using Python, Scala, SQL, R". On the other hand, Databricks is most compared with Microsoft Azure Machine Learning Studio, Amazon SageMaker, Dataiku Data Science Studio, Azure Stream Analytics and Alteryx, whereas Upsolver is most compared with Matillion Data Loader and Rivery.

    See our list of best Streaming Analytics vendors.

    We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.