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…
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Oracle Database
Score 8.3 out of 10
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Oracle Database, currently in edition 23ai, is a converged, multimodel database management system. It is designed to simplify development for AI, microservices, graph, document, spatial, and relational applications.
$0.05
per hour
Pricing
Jupyter Notebook
Oracle Database
Editions & Modules
No answers on this topic
Oracle Base Database Service - Standard
$0.0538
per hour
Oracle Base Database Service - Enterprise
$0.1075
per hour
Oracle Base Database Service - High Performance
$0.2218
per hour
Standard Edition
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Enterprise Edition
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Personal Edition
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Offerings
Pricing Offerings
Jupyter Notebook
Oracle Database
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Jupyter Notebook
Oracle Database
Features
Jupyter Notebook
Oracle Database
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Jupyter Notebook
9.0
22 Ratings
8% above category average
Oracle Database
-
Ratings
Connect to Multiple Data Sources
10.022 Ratings
00 Ratings
Extend Existing Data Sources
10.021 Ratings
00 Ratings
Automatic Data Format Detection
8.514 Ratings
00 Ratings
MDM Integration
7.415 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Jupyter Notebook
7.0
22 Ratings
18% below category average
Oracle Database
-
Ratings
Visualization
6.022 Ratings
00 Ratings
Interactive Data Analysis
8.022 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Jupyter Notebook
9.5
22 Ratings
15% above category average
Oracle Database
-
Ratings
Interactive Data Cleaning and Enrichment
10.021 Ratings
00 Ratings
Data Transformations
10.022 Ratings
00 Ratings
Data Encryption
8.514 Ratings
00 Ratings
Built-in Processors
9.314 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Jupyter Notebook
9.3
22 Ratings
10% above category average
Oracle Database
-
Ratings
Multiple Model Development Languages and Tools
10.021 Ratings
00 Ratings
Automated Machine Learning
9.218 Ratings
00 Ratings
Single platform for multiple model development
10.022 Ratings
00 Ratings
Self-Service Model Delivery
8.020 Ratings
00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Jupyter Notebook
10.0
20 Ratings
16% above category average
Oracle Database
-
Ratings
Flexible Model Publishing Options
10.020 Ratings
00 Ratings
Security, Governance, and Cost Controls
10.019 Ratings
00 Ratings
Relational Databases
Comparison of Relational Databases features of Product A and Product B
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.
We migrated from NoSQL to an Oracle database. One of the reasons was robust backup and recovery options available in the Oracle database, which provide zero data loss. A transactional database like Oracle is a better fit for our use case than NoSQL. On a large scale, deployment was evaluated as a cheaper option than the NoSQL engine. This conclusion came even after considering Oracle license is expensive.
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.
There is a lot of sunk cost in a product like Oracle 12c. It is doing a great job, it would not provide us much benefit to switch to another product even if it did the same thing due to the work involved in making such a switch. It would not be cost effective.
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.
Many of the powerful options can be auto-configured but there are still many things to take into account at the moment of installing and configuring an Oracle Database, compared with SQL Server or other databases. At the same time, that extra complexity allows for detailed configuration and guarantees performance, scalability, availability and security.
1. I have very good experience with Oracle Database support team. Oracle support team has pool of talented Oracle Analyst resources in different regions. To name a few regions - EMEA, Asia, USA(EST, MST, PST), Australia. Their support staffs are very supportive, well trained, and customer focused. Whenever I open Oracle Sev1 SR(service request), I always get prompt update on my case timely. 2. Oracle has zoom call and chat session option linked to Oracle SR. Whenever you are in Oracle portal - you can chat with the Oracle Analyst who is working on your case. You can request for Oracle zoom call thru which you can share the your problem server screen in no time. This is very nice as it saves lot of time and energy in case you have to follow up with oracle support for your case. 3.Oracle has excellent knowledge base in which all the customer databases critical problems and their solutions are well documented. It is very easy to follow without consulting to support team at first.
Overall the implementation went very well and after that everything came out as expected - in terms of performance and scalability. People should always install and upgrade a stable version for production with the latest patch set updates, test properly as much as possible, and should have a backup plan if anything unexpected happens
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.
Because of a rich user base and support for any critical issue, this is one of the best options to choose. In case the project has a TCO issue, it can compromise and choose Postgres as the best alternative. SQL server is also good and easy to code and maintain but performance is not as good as the Oracle