About
Mature and complete async-await for user interfaces containing event-driven code, paired with isolate-based concurrency. A programming language optimized for building user interfaces with features such as sound null safety, the spread operator for expanding collections, and collection if for customizing UI for each platform. Write code using a flexible type system with rich static analysis and powerful, configurable tooling. Target the web with complete, mature, fast compilers for JavaScript. Run backend code supporting your app, written using a single programming language. This collection is not exhaustive—it’s just a brief introduction to the language for people who like to learn by example. You might also want to check out the language and library tours, or the Dart cheatsheet codelab.
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About
IronPython is an open-source implementation of the Python programming language which is tightly integrated with .NET. IronPython can use .NET and Python libraries, and other .NET languages can use Python code just as easily. Experience a more interactive .NET and Python development experience with Python Tools for Visual Studio. IronPython is an excellent addition to .NET, providing Python developers with the power of the .NET. Existing .NET developers can also use IronPython as a fast and expressive scripting language for embedding, testing, or writing a new application from scratch. The CLR is a great platform for creating programming languages, and the DLR makes it all the better for dynamic languages. Also, the .NET (base class library, presentation foundation, etc.) gives developers an amazing amount of functionality and power. IronPython uses Python syntax and standard libraries and so your Python code will need to be updated accordingly.
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About
PureScript is a strongly typed, purely functional programming language that compiles JavaScript. It enables developers to build robust web applications, web servers, and mobile apps using functional programming techniques. PureScript offers features such as algebraic data types, pattern matching, row polymorphism, extensible records, higher-kinded types, type classes with functional dependencies, and higher-rank polymorphism. The language emphasizes strong static typing and pure functions, ensuring code reliability and maintainability. Developers can compile PureScript code into readable JavaScript, facilitating seamless integration with existing JavaScript codebases. The ecosystem includes an extensive collection of libraries, excellent tooling, and editor support with instant rebuilds. An active community provides numerous learning resources, including the PureScript book, which offers practical projects for beginners.
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About
dbt helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, data analysts and data engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, lean on detailed metadata to troubleshoot and optimize pipelines, and deploy transformations reliably at scale. Built on modern software engineering best practices, dbt brings transparency and governance to every step of the data transformation workflow.
Thousands of companies, from startups to Fortune 500 enterprises, rely on dbt to improve data quality and trust as well as drive efficiencies and reduce costs as they deliver AI-ready data across their organization. Whether you’re scaling data operations or just getting started, dbt empowers your team to move from raw data to actionable analytics with confidence.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Developers
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Audience
Developers requiring a scripting language for embedding, testing, or writing new applications
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Audience
Developers interested in a solution to build reliable and maintainable applications
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Audience
SQL users looking for a ETL solution to engineer data transformations
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Pricing
$100 per user/ month
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationDart Language
dart.dev/
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Company InformationIronPython
ironpython.net
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Company InformationPureScript
Founded: 2017
United States
www.purescript.org
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Company Informationdbt Labs
Founded: 2016
United States
www.getdbt.com
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Alternatives |
Alternatives |
Alternatives |
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Categories |
Categories |
Categories |
Categoriesdbt powers the transformation layer of modern data pipelines. Once data has been ingested into a warehouse or lakehouse, dbt enables teams to clean, model, and document it so it’s ready for analytics and AI. With dbt, teams can: - Transform raw data at scale with SQL and Jinja. - Orchestrate pipelines with built-in dependency management and scheduling. - Ensure trust with automated testing and continuous integration. - Visualize lineage across models and columns for faster impact analysis. By embedding software engineering practices into pipeline development, dbt helps data teams build reliable, production-grade pipelines to accelerate time to insight, and deliver AI-ready data. dbt brings rigor and scalability to data preparation by enabling teams to clean, transform, and structure raw data directly in the warehouse. Instead of siloed spreadsheets or manual workflows, dbt uses SQL and software engineering best practices to make data preparation reliable, repeatable, and collaborative. With dbt, teams can: - Clean and standardize data with reusable, version-controlled models. - Apply business logic consistently across all datasets. - Validate outputs through automated tests before data is exposed to analysts. - Document and share context so every prepared dataset comes with lineage and definitions. By treating data preparation as code, dbt ensures that prepared datasets aren’t just quick fixes — they’re trusted, governed, and production-ready assets that scale with the business. dbt modernizes the “T” in ETL: Transformation. Instead of relying on legacy pipelines or black-box transformations, dbt empowers data teams to build, test, and document transformations directly inside the data warehouse or lakehouse. With dbt, teams can: - Transform raw data into analytics-ready models using SQL and Jinja. - Ensure reliability with built-in testing, version control, and CI/CD. - Standardize workflows across teams with reusable models and shared documentation. - Leverage modern platforms like Snowflake, Databricks, BigQuery, and Redshift for scalable transformation. By focusing on the transformation layer, dbt helps organizations shorten pipeline development cycles, reduce data debt, and deliver trusted insights faster — complementing ingestion and loading tools in a modern ELT stack. |
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Big Data Features
Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates
Data Lineage Features
Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View
Data Preparation Features
Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface
ETL Features
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
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Integrations
Buffer Editor
Cake AI
DataHub
Datakin
Dfns
GetDot.ai
IntelliSense
Kestra
Kodezi
Mode
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Integrations
Buffer Editor
Cake AI
DataHub
Datakin
Dfns
GetDot.ai
IntelliSense
Kestra
Kodezi
Mode
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Integrations
Buffer Editor
Cake AI
DataHub
Datakin
Dfns
GetDot.ai
IntelliSense
Kestra
Kodezi
Mode
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Integrations
Buffer Editor
Cake AI
DataHub
Datakin
Dfns
GetDot.ai
IntelliSense
Kestra
Kodezi
Mode
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