About
Apache Groovy is a powerful, optionally typed and dynamic language, with static-typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming. Concise, readable and expressive syntax, easy to learn for Java developers. Closures, builders, runtime & compile-time meta-programming, functional programming, type inference, and static compilation. Flexible & malleable syntax, advanced integration & customization mechanisms, to integrate readable business rules in your applications. Great for writing concise and maintainable tests, and for all your build and automation tasks.
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About
BoxLang is a modern, dynamically and loosely typed scripting language for the Java Virtual Machine (JVM) that supports Object-Oriented (OO) and Functional Programming (FP) constructs. It can be deployed on multiple platforms and all operating systems, web servers, Java application servers, AWS Lambda, WebAssembly, and more. BoxLang combines many features from different programming languages to provide developers with a modern, fluent, and expressive syntax. BoxLang has been designed to be a highly modular and dynamic language that takes advantage of all the modern features of the JVM. It is dynamically typed, which means there's no need to declare types. It can perform type inference, auto-casting, and promotions between different types. The language adjusts to its deployed runtime and can add, remove, or modify methods and properties at runtime.
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About
The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.
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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 looking for a multi-faceted language solution for Java
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Audience
Organizations requiring a solution to optimzie the development of scalable and adaptable applications
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Audience
Developers interested in a beautiful but advanced programming language
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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
Free
Free Version
Free Trial
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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
$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 InformationThe Apache Software Foundation
groovy-lang.org
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Company InformationBoxLang
United States
boxlang.io
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Company InformationPython
Founded: 1991
www.python.org
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Company Informationdbt Labs
Founded: 2016
United States
www.getdbt.com
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Alternatives |
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
Aider
AutoRest
ChatGPT Go
Codeanywhere
EmEditor
Flawnter
Gemini
Gemini Flash
Glitch
ImSym
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Integrations
Aider
AutoRest
ChatGPT Go
Codeanywhere
EmEditor
Flawnter
Gemini
Gemini Flash
Glitch
ImSym
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Integrations
Aider
AutoRest
ChatGPT Go
Codeanywhere
EmEditor
Flawnter
Gemini
Gemini Flash
Glitch
ImSym
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Integrations
Aider
AutoRest
ChatGPT Go
Codeanywhere
EmEditor
Flawnter
Gemini
Gemini Flash
Glitch
ImSym
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