About
Cube is a platform that provides a universal semantic layer to simplify and unify enterprise data management and analytics. By transforming how data is managed, Cube eliminates the need for inconsistent models and metrics, delivering trusted data to users while making it AI-ready. This platform helps organizations scale their data infrastructure by integrating disparate data sources and creating consistent metrics that can be used across teams. Cube is designed for enterprises looking to enhance their analytics capabilities, make their data accessible, and power AI-driven insights with ease.
|
About
FastScript is a cross-platform, multi-language scripting engine. It is useful for programmers who want to add scripting ability to their projects. FastScript can be used in Delphi 7-XE8, C++Builder 2005-XE8, Embarcadero RAD Studio 11 and Lazarus. FastScript is written entirely in 100% Object Pascal.
A unique feature of FastScript is its ability to use several languages (PascalScript, C++Script, BasicScript, and JScript). This allows you to write scripts using your favorite language. FastScript does not use the Microsoft Scripting Host so it can be used in both the Windows and Linux environments and also in Mac OS.
FastScript offers a wide range of features, including cross-platform scripting, fast code execution, a small footprint, a large variety of tools, and excellent scaling options. Use FastScript to make your applications really flexible and powerful!
|
About
Enjoy the highest performance and unlimited possibilities when working with SQLite. SQLite Data Access Components (LiteDAC) is a library of components that provides native connectivity to SQLite from Delphi and C++Builder including Community Edition, as well as Lazarus (and Free Pascal) on Windows, Linux, macOS, iOS, and Android for both 32-bit and 64-bit platforms. LiteDAC is designed for programmers to develop truly cross-platform desktop and mobile SQLite database applications with no need to deploy any additional libraries. LiteDAC-based DB applications are easy to deploy and do not require the installation of other data provider layers (such as BDE or ODBC), and that's why they can work faster than the ones based on standard Delphi data connectivity solutions. Moreover, LiteDAC provides an additional opportunity to work with SQLite in Delphi and C++Builder directly by linking the client library statically in your application.
|
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.
|
|||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||
Audience
Enterprises and data teams looking for a unified, scalable platform to manage data consistency, improve analytics capabilities, and make data AI-ready
|
Audience
Software Developers, System Integrators
|
Audience
Developer teams and anyone seeking a solution providing a library of components to build cross-platform databases
|
Audience
SQL users looking for a ETL solution to engineer data transformations
|
|||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||
API
Offers API
|
API
Offers API
|
API
Offers API
|
API
Offers API
|
|||
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
|||
Pricing
No information available.
Free Version
Free Trial
|
Pricing
$79.00/developer
Free Version
Free Trial
|
Pricing
$169.95 per year
Free Version
Free Trial
|
Pricing
$100 per user/ month
Free Version
Free Trial
|
|||
Reviews/
|
Reviews/
|
Reviews/
|
Reviews/
|
|||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||
Company InformationCube Dev
Founded: 2016
United States
cube.dev/
|
Company InformationFast Reports
Founded: 1998
United States
www.fast-report.com/en/product/fast-script/
|
Company InformationDevart
Founded: 1997
Czech Republic
www.devart.com/litedac/
|
Company Informationdbt Labs
Founded: 2016
United States
www.getdbt.com
|
|||
Alternatives |
Alternatives |
Alternatives |
Alternatives |
|||
|
|
||||||
|
|
||||||
|
|
|
|
||||
|
|
||||||
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. |
|||
Application Development Features
Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development
|
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
|
|||||
Integrations
Amazon Redshift
C++Builder
Cake AI
Cargo
DQOps
DataOps.live
Datafold
GetDot.ai
Grouparoo
Metaphor
|
Integrations
Amazon Redshift
C++Builder
Cake AI
Cargo
DQOps
DataOps.live
Datafold
GetDot.ai
Grouparoo
Metaphor
|
Integrations
Amazon Redshift
C++Builder
Cake AI
Cargo
DQOps
DataOps.live
Datafold
GetDot.ai
Grouparoo
Metaphor
|
Integrations
Amazon Redshift
C++Builder
Cake AI
Cargo
DQOps
DataOps.live
Datafold
GetDot.ai
Grouparoo
Metaphor
|
|||
|
|
|
|
