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Keras VS dbt

Compare Keras VS dbt and see what are their differences

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Keras logo Keras

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

dbt logo dbt

dbt is a data transformation tool that enables data analysts and engineers to transform, test and document data in the cloud data warehouse.
  • Keras Landing page
    Landing page //
    2023-10-16
  • dbt Landing page
    Landing page //
    2023-10-16

Keras features and specs

  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlow’s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages of Keras

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.

dbt features and specs

  • Modularity
    dbt promotes a modular approach to building analytics workflows, allowing data teams to break down transformations into smaller, more manageable SQL scripts. This improves code readability, maintainability, and collaboration among team members.
  • Version Control Integration
    By integrating with Git, dbt enables teams to version control their data transformation scripts, fostering collaboration, auditability, and change tracking over time.
  • CI/CD Pipeline Compatibility
    dbt supports integration with continuous integration and continuous deployment (CI/CD) systems, allowing automated testing and deployment of transformations as part of the data pipeline.
  • Data Quality Testing
    dbt offers built-in testing functionalities, which enable developers to write tests to validate data transformations and ensure data quality/integrity within their data models.
  • Documentation and Lineage
    dbt automatically generates documentation for the data models and creates a lineage graph, providing transparency and understanding of data flows and dependencies.

Possible disadvantages of dbt

  • SQL Limitations
    Since dbt primarily relies on SQL for transformations, complex transformations may become cumbersome or difficult to implement compared to programming languages like Python or R.
  • Learning Curve
    New users may face a learning curve in setting up and effectively using dbt, especially if they are unfamiliar with concepts like data modeling, Git, or command-line tools.
  • Performance Constraints
    The performance of dbt transformations is dependent on the underlying data warehouse. Large-scale transformations could lead to performance inefficiencies if the warehouse is not optimized.
  • Cost
    Running dbt transformations continuously can incur costs associated with warehouse usage, especially if the data models involve processing large volumes of data regularly.
  • Dependency on Data Stack
    dbt's effectiveness is reliant on having a robust data warehouse and surrounding data stack, meaning smaller or less mature setups may struggle to leverage its full potential.

Keras videos

+ Add

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos:

  • Review - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • Review - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

dbt videos

+ Add

Introduction to dbt (data build tool) from Fishtown Analytics

Category Popularity

0-100% (relative to Keras and dbt)

Keras

dbt

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Data Integration
0 0%
Data Integration
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Web Service Automation
0 0%
Web Service Automation
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Keras and dbt

Keras Reviews

10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...
Source: searchbusinessanalytics.techtarget.com

dbt Reviews

13 data integration tools: a comparative analysis of the top solutions
Reading about the previous integration tool, you probably noticed the support of dbt Core (Data Build Tools) for data transformations. In fact, dbt Core is a product of its own – an open-source command-line tool for data pipelines. In addition to the Core product, dbt also offers a Cloud platform that strives to bridge the gap between software developers and data management...
Source: blog.n8n.io

Social recommendations and mentions

Based on our record, Keras seems to be a lot more popular than dbt. While we know about 35 links to Keras, we've tracked only 2 mentions of dbt. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Keras mentions (35)

  • Top Programming Languages for AI Development in 2025
    The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / 25 days ago
  • Top 8 OpenSource Tools for AI Startups
    If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and running—an essential part of the startup hustle. - Source: dev.to / 7 months ago
  • Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
    At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / 8 months ago
  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 12 months ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 year ago
View more

dbt mentions (2)

What are some alternatives?

When comparing Keras and dbt, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Datacoves - Managed dbt-core, VS Code in the browser, and Managed Airflow.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

dataloader.io - Quickly and securely import, export and delete unlimited amounts of data for your enterprise.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

CData Sync - Straightforward data synchronizing between on-premise and cloud data sources with a wide range of traditional and emerging databases.

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