dbt vs. Python IDLE

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
dbt
Score 9.1 out of 10
N/A
dbt is an SQL development environment, developed by Fishtown Analytics, now known as dbt Labs. The vendor states that with dbt, analysts take ownership of the entire analytics engineering workflow, from writing data transformation code to deployment and documentation. dbt Core is distributed under the Apache 2.0 license, and paid Teams and Enterprise editions are available.
$0
per month per seat
Python IDLE
Score 8.5 out of 10
N/A
Python's IDLE is the integrated development environment (IDE) and learning platform for Python, presented as a basic and simple IDE appropriate for learners in educational settings.
$0
Pricing
dbtPython IDLE
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
dbtPython IDLE
Free Trial
YesNo
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
YesNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
dbtPython IDLE
Features
dbtPython IDLE
Data Transformations
Comparison of Data Transformations features of Product A and Product B
dbt
9.7
8 Ratings
19% above category average
Python IDLE
-
Ratings
Simple transformations10.08 Ratings00 Ratings
Complex transformations9.48 Ratings00 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
dbt
9.1
8 Ratings
15% above category average
Python IDLE
-
Ratings
Data model creation9.78 Ratings00 Ratings
Metadata management8.78 Ratings00 Ratings
Business rules and workflow9.08 Ratings00 Ratings
Collaboration10.06 Ratings00 Ratings
Testing and debugging8.08 Ratings00 Ratings
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User Ratings
dbtPython IDLE
Likelihood to Recommend
10.0
(10 ratings)
2.6
(7 ratings)
Usability
9.7
(3 ratings)
8.2
(2 ratings)
Support Rating
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
dbtPython IDLE
Likelihood to Recommend
dbt Labs
The prerequisite is that you have a supported database/data warehouse and have already found a way to ingest your raw data. Then dbt is very well suited to manage your transformation logic if the people using it are familiar with SQL. If you want to benefit from bringing engineering practices to data, dbt is a great fit. It can bring CI/CD practices, version control, automated testing, documentation generation, etc. It is not so well suited if the people managing the transformation logic do not like to code (in SQL) but prefer graphical user interfaces.
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Python Software Foundation
Scenarios where python IDLE is well suited 1-Quick scripting and prototyping 2-Education and training 3-small projects utilities 4-exploring python libraries and modules Scenarios where python is less appropriate 1 large scale projects 2 complex debugging and profiling 3 multi language development 4 Advanced code analysis and inspection
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Pros
dbt Labs
  • dbt supports version control through GIT, this allows teams to collaborate and track the data transformation logic.
  • dbt allows us to build data models which helps to break complex transformation logic into simple and smaller logic.
  • dbt is completely based on SQL which allows data analyst and data engineers to build the transformation logic.
  • dbt can be easily integrated with snowflake.
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Python Software Foundation
  • Firstly, I would say Python IDLE interface is user friendly.
  • Easy to learn for the beginners.
  • Syntax highlighting is nice features.
  • Smart indent helps a lot.
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Cons
dbt Labs
  • Field-level lineage (currently at table level)
  • Documentation inheritance - if a field is documented the downstream field of the same name could inherit the doc info
  • Adding python model support (in beta now)
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Python Software Foundation
  • Too simplistic
  • Could not find source revision management integration support
  • Only basic debugging is available
  • Does not have data-science-specific notebooks (but can be installed separately)
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Usability
dbt Labs
dbt is very easy to use. Basically if you can write SQL, you will be able to use dbt to get what you need done. Of course more advanced users with more technical skills can do more things.
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Python Software Foundation
The IDE Python IDLE is a good place to start as it helps you become familiar with the way Python works and understand its syntax.
This IDE allows you to configure the environment, font, size, colors, .....
It also looks like any simple text editor for any operating system, I work with Windows or Linux interchangeably, and you don't have to learn to use the IDE before programming.
Once the IDE is executed you can start programming directly in it.
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Support Rating
dbt Labs
No answers on this topic
Python Software Foundation
Python IDLE support is what the community can give you. As it is free software, it does not have support provided by the manufacturer or by third-parties.
In any case, for most of the problems that normal users can find, the solution, or alternatives, can be found quickly online.
As this IDE is made in Python, the support is the same group of Python developers.
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Alternatives Considered
dbt Labs
I actually don't know what the alternative to dbt is. I'm sure one must exist other than more 'roll your own' options like Apache Airflow, say, bu tin terms of super easy managed/cloud data transforms, dbt really does seem to be THE tool to use. It's $50/month per dev, BUT there's a FREE version for 1 dev seat with no read-only access for anyone else, so you can always start with that and then buy yourself a seat later.
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Python Software Foundation
It's easy to set up and run quick analysis in Python IDLE on my local machine. The output is direct and easy to read. But sometimes I prefer Jupyter Notebook when the datasets are large, since it would take too long to run on my local machine. It is easier to run Jupyter Notebook on my cloud desktop
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Return on Investment
dbt Labs
  • Simplified our BI layer for faster load times
  • Increased the quality of data reaching our end users
  • Makes complex transformations manageable
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Python Software Foundation
  • In a short time, we were able to develop several ML models for various teams to make accurate decisions.
  • Beginners can easily understand and adapt to GUI.
  • We could automate several manual validation tasks and so could reduce human intervention.
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