Databricks Lakehouse Platform vs. Jupyter Notebook

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Databricks Lakehouse Platform
Score 8.1 out of 10
N/A
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
Jupyter Notebook
Score 8.8 out of 10
N/A
Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for…N/A
Pricing
Databricks Lakehouse PlatformJupyter Notebook
Editions & Modules
Standard
$0.07
Per DBU
Premium
$0.10
Per DBU
Enterprise
$0.13
Per DBU
No answers on this topic
Offerings
Pricing Offerings
Databricks Lakehouse PlatformJupyter Notebook
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Databricks Lakehouse PlatformJupyter Notebook
Top Pros
Top Cons
Features
Databricks Lakehouse PlatformJupyter Notebook
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Databricks Lakehouse Platform
-
Ratings
Jupyter Notebook
8.5
21 Ratings
1% above category average
Connect to Multiple Data Sources00 Ratings9.021 Ratings
Extend Existing Data Sources00 Ratings9.220 Ratings
Automatic Data Format Detection00 Ratings8.514 Ratings
MDM Integration00 Ratings7.415 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Databricks Lakehouse Platform
-
Ratings
Jupyter Notebook
9.6
21 Ratings
13% above category average
Visualization00 Ratings9.621 Ratings
Interactive Data Analysis00 Ratings9.621 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Databricks Lakehouse Platform
-
Ratings
Jupyter Notebook
9.0
21 Ratings
9% above category average
Interactive Data Cleaning and Enrichment00 Ratings9.320 Ratings
Data Transformations00 Ratings8.921 Ratings
Data Encryption00 Ratings8.514 Ratings
Built-in Processors00 Ratings9.314 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Databricks Lakehouse Platform
-
Ratings
Jupyter Notebook
8.9
21 Ratings
5% above category average
Multiple Model Development Languages and Tools00 Ratings9.020 Ratings
Automated Machine Learning00 Ratings9.218 Ratings
Single platform for multiple model development00 Ratings9.221 Ratings
Self-Service Model Delivery00 Ratings8.020 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Databricks Lakehouse Platform
-
Ratings
Jupyter Notebook
8.8
19 Ratings
3% above category average
Flexible Model Publishing Options00 Ratings8.819 Ratings
Security, Governance, and Cost Controls00 Ratings8.718 Ratings
Best Alternatives
Databricks Lakehouse PlatformJupyter Notebook
Small Businesses

No answers on this topic

Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
Medium-sized Companies
Snowflake
Snowflake
Score 9.0 out of 10
Mathematica
Mathematica
Score 8.3 out of 10
Enterprises
Snowflake
Snowflake
Score 9.0 out of 10
Dataiku
Dataiku
Score 8.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Databricks Lakehouse PlatformJupyter Notebook
Likelihood to Recommend
8.4
(17 ratings)
8.4
(22 ratings)
Usability
9.4
(3 ratings)
10.0
(1 ratings)
Support Rating
8.6
(2 ratings)
9.0
(1 ratings)
Contract Terms and Pricing Model
8.0
(1 ratings)
-
(0 ratings)
Professional Services
10.0
(1 ratings)
-
(0 ratings)
User Testimonials
Databricks Lakehouse PlatformJupyter Notebook
Likelihood to Recommend
Databricks
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
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Open Source
I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.
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Pros
Databricks
  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
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Open Source
  • Simple and elegant code writing ability. Easier to understand the code that way.
  • The ability to see the output after each step.
  • The ability to use ton of library functions in Python.
  • Easy-user friendly interface.
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Cons
Databricks
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
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Open Source
  • Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
  • Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
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Usability
Databricks
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX
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Open Source
Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.
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Support Rating
Databricks
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
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Open Source
I haven't had a need to contact support. However, all required help is out there in public forums.
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Alternatives Considered
Databricks
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
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Open Source
With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.
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Return on Investment
Databricks
  • The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
  • DB has the ability to terminate/time out instances which helps manage cost.
  • The ability to quickly access typical hard to build data scenarios easily is a strength.
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Open Source
  • Positive impact: flexible implementation on any OS, for many common software languages
  • Positive impact: straightforward duplication for adaptation of workflows for other projects
  • Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration
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ScreenShots