Databricks vs Google Cloud Datalab comparison

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30,172 views|19,226 comparisons
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1,837 views|1,686 comparisons
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Databricks and Google Cloud Datalab based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Databricks vs. Google Cloud Datalab Report (Updated: March 2024).
765,386 professionals have used our research since 2012.
Featured Review
Nilesh Gode
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data.""The Delta Lake data type has been the most useful part of this solution. Delta Lake is an opensource data type and it was implemented and invented by Databricks.""Imageflow is a visual tool that helps make it easier for business people to understand complex workflows.""Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform.""The solution's features are fantastic and include interactive clusters that perform at top speed when compared to other solutions.""The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient.""Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client.""Ability to work collaboratively without having to worry about the infrastructure."

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"The infrastructure is highly reliable and efficient, contributing to a positive experience.""All of the features of this product are quite good.""The APIs are valuable.""Google Cloud Datalab is very customizable.""In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

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Cons
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity.""We'd like a more visual dashboard for analysis It needs better UI.""The tool should improve its integration with other products.""Doesn't provide a lot of credits or trial options.""Scalability is an area with certain shortcomings. The solution's scalability needs improvement.""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.""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.""It would be nice to have more guidance on integrations with ETLs and other data quality tools."

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"The interface should be more user-friendly.""The product must be made more user-friendly.""We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience.""There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option.""Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

More Google Cloud Datalab Cons →

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."
  • More Databricks Pricing and Cost Advice →

  • "It is affordable for us because we have a limited number of users."
  • "The pricing is quite reasonable, and I would give it a rating of four out of ten."
  • "The product is cheap."
  • More Google Cloud Datalab 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 »
    Top Answer:The product must be made more user-friendly. Sometimes, we have to go a roundabout way and read a lot of instruction that isn't necessary. Generally, if people use the information, they have some… more »
    Top Answer:The solution is really useful. It’s an easy way to get information. I use it as a reference for analytics, sourcing information, and research.
    Ranking
    1st
    Views
    30,172
    Comparisons
    19,226
    Reviews
    47
    Average Words per Review
    446
    Rating
    8.2
    13th
    Views
    1,837
    Comparisons
    1,686
    Reviews
    2
    Average Words per Review
    631
    Rating
    8.0
    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.”

    Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

    Sample Customers
    Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
    Information Not Available
    Top Industries
    REVIEWERS
    Computer Software Company25%
    Financial Services Firm16%
    Retailer9%
    Manufacturing Company9%
    VISITORS READING REVIEWS
    Financial Services Firm14%
    Computer Software Company12%
    Manufacturing Company8%
    Healthcare Company6%
    VISITORS READING REVIEWS
    Financial Services Firm16%
    Educational Organization12%
    Computer Software Company11%
    Manufacturing Company9%
    Company Size
    REVIEWERS
    Small Business27%
    Midsize Enterprise14%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise71%
    VISITORS READING REVIEWS
    Small Business23%
    Midsize Enterprise10%
    Large Enterprise68%
    Buyer's Guide
    Databricks vs. Google Cloud Datalab
    March 2024
    Find out what your peers are saying about Databricks vs. Google Cloud Datalab and other solutions. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Databricks is ranked 1st in Data Science Platforms with 77 reviews while Google Cloud Datalab is ranked 13th in Data Science Platforms with 5 reviews. Databricks is rated 8.2, while Google Cloud Datalab is rated 7.6. 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 Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Azure Stream Analytics, whereas Google Cloud Datalab is most compared with IBM SPSS Statistics, Cloudera Data Science Workbench, Domino Data Science Platform, IBM SPSS Modeler and KNIME. See our Databricks vs. Google Cloud Datalab report.

    See our list of best Data Science Platforms vendors.

    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.