TrustRadius: an HG Insights company

Save this comparison

Save this comparison

Add Product

Recommended Comparisons

    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    dbt

    Score9.2 out of 10
    N/Adbt 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

    Score8.6 out of 10
    N/APython'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
    Considered Both Products
    dbt Labs
    No answer on this topic
    Python Software Foundation
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    10 Answers
    83%
    Would buy again
    5 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    9 Answers
    100%
    Delivers good value for the price
    5 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    10 Answers
    83%
    Happy with the feature set
    5 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    8 Answers
    No answers on this topic
    Implementation went as expected
    90%
    Implementation went as expected
    9 Answers
    No answers on this topic
    Features
    dbtPython IDLE
    Data Transformations
    Comparison of Data Transformations features of dbt and Python IDLE
    Feature
    dbt
    9.8
    8 Ratings
    19% above category average
    Python IDLE
    -
    Ratings
    Simple transformations10.08 Ratings00 Ratings
    Complex transformations9.58 Ratings00 Ratings
    Data Modeling
    Comparison of Data Modeling features of dbt and Python IDLE
    Feature
    dbt
    9.1
    8 Ratings
    14% above category average
    Python IDLE
    -
    Ratings
    Data model creation9.88 Ratings00 Ratings
    Metadata management8.88 Ratings00 Ratings
    Business rules and workflow9.08 Ratings00 Ratings
    Collaboration10.06 Ratings00 Ratings
    Testing and debugging8.08 Ratings00 Ratings
    Best Alternatives
    dbtPython IDLE
    Small Businesses
    Skyvia
    Score10 out of 10
    Visual Studio
    Score8.8 out of 10
    Medium-sized Companies
    IBM InfoSphere Information Server
    Score10 out of 10
    PyCharm
    Score9.3 out of 10
    Enterprises
    SolarWinds Task Factory
    Score8.3 out of 10
    WebStorm
    Score9.6 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    dbtPython IDLE
    Likelihood to Recommend
    10.0
    (10 ratings)
    3.7
    (7 ratings)
    Usability
    9.8
    (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.
    Incentivized
    Read full review
    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
    Read full review
    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.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    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)
    Incentivized
    Read full review
    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)
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    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
    Incentivized
    Read full review
    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
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    ScreenShots