Databricks vs Yellowfin comparison

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Databricks Logo
28,975 views|18,474 comparisons
96% willing to recommend
Yellowfin Logo
287 views|222 comparisons
100% willing to recommend
Comparison Buyer's Guide
Executive Summary

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

Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms.
To learn more, read our detailed Data Science Platforms Report (Updated: April 2024).
769,065 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:
Pros
"The integration with Python and the notebooks really helps.""The simplicity of development is the most valuable feature.""The setup was straightforward.""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 is easy to use and has a quick start-up time due to being on the cloud.""Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good.""Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client.""Databricks has helped us have a good presence in data."

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"It is able to create information dashboards for various users' throughout.""It is a central source of up-to-date data and information.""It reduces time to reproduce reports, provides easy access to organisational data, and has the ability to generate a wide range of reports and analysis."

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Cons
"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps.""I have seen better user interfaces, so that is something that can be improved.""If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks.""There should be better integration with other platforms.""Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing.""Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's.""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.""Databricks would have more collaborative features than it has. It should have some more customization for the jobs."

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"It needs more presentation/charting capabilities and integration with GIS."

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Pricing and Cost Advice
  • "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."
  • "I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."
  • "Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
  • "We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."
  • "The pricing depends on the usage itself."
  • "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."
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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
    Views
    28,975
    Comparisons
    18,474
    Reviews
    47
    Average Words per Review
    441
    Rating
    8.3
    Views
    287
    Comparisons
    222
    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.”

    Yellowfin offers a business intelligence (BI) platform that allows users to interact with big data in a variety of ways. Yellowfin works to provide big data access to companies of all sizes, from small business to billion-dollar enterprises. It is designed for providing data quickly and allowing users to filter for specific results, maximizing gain from big data. Yellowfin has a variety of dashboards so the user can filter, drill to detail, and have data analysis in the same browser. The data can also be easily shared with others via email, wiki embed, and reports. There is a mobile app as well, which works automatically with the desktop dashboards to allow users to access their data on the go, and alerts can be set up to notify a user via email or push to a mobile device if the data is indicating a critical event taking place. Yellowfin also can map data to locations, providing not only the what of data, but also the where. Lancashire Care NHS Foundation Trust provides its community in England with mental health and community care services. Using Yellowfin's mobile business intelligence capabilities to deliver reporting and analytics to approximately 6000 clinicians, Lancashire Care NHS Foundation Trust was able to greatly improve the quality of its patient care. Yellowfin allowed the Trust to access and share independent data from mobile devices as well as create a Balanced Scoreboard to quickly gather and report information on the company's performance to its executives, management, and directors.
    Sample Customers
    Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
    NCS, Universitat Konstanz, AT&T, PG&E, SingTel, InternetStores
    Top Industries
    REVIEWERS
    Computer Software Company25%
    Financial Services Firm16%
    Retailer9%
    Manufacturing Company9%
    VISITORS READING REVIEWS
    Financial Services Firm15%
    Computer Software Company12%
    Manufacturing Company8%
    Healthcare Company6%
    VISITORS READING REVIEWS
    Computer Software Company27%
    Government11%
    Financial Services Firm7%
    Insurance Company6%
    Company Size
    REVIEWERS
    Small Business27%
    Midsize Enterprise14%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business36%
    Midsize Enterprise20%
    Large Enterprise44%
    Buyer's Guide
    Data Science Platforms
    April 2024
    Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms. Updated: April 2024.
    769,065 professionals have used our research since 2012.

    Databricks is ranked 1st in Data Science Platforms with 78 reviews while Yellowfin is ranked 36th in BI (Business Intelligence) Tools. Databricks is rated 8.2, while Yellowfin is rated 8.0. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of Yellowfin writes "Very scalable design and easy to implement. It can reside alongside more complex enterprise systems". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio, whereas Yellowfin is most compared with Microsoft Power BI, Apache Superset, 9 Spokes, Tableau and SAP Crystal Reports.

    We monitor all Data Science Platforms 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.