About
NoSQL is a domain-specific programming language used for accessing, managing, and manipulating non-tabular databases. A NoSQL (originally referring to "non-SQL" or "non-relational") database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 21st century, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications.NoSQL systems are also sometimes called Not only SQL to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures. Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages.
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About
SQL is a domain-specific programming language used for accessing, managing, and manipulating relational databases and relational database management systems.
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About
Simple, fast, safe, and compiled. For developing maintainable software. Simple language for building maintainable programs. You can learn the entire language by going through the documentation over a weekend, and in most cases, there's only one way to do something. This results in simple, readable, and maintainable code. This results in simple, readable, and maintainable code. Despite being simple, V gives a lot of power to the developer and can be used in pretty much every field, including systems programming, webdev, gamedev, GUI, mobile, science, embedded, tooling, etc. V is very similar to Go. If you know Go, you already know 80% of V. Bounds checking, No undefined values, no variable shadowing, immutable variables by default, immutable structs by default, option/result and mandatory error checks, sum types, generics, and immutable function args by default, mutable args have to be marked on call.
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About
dbt Labs helps data teams transform raw data into trusted, analysis-ready datasets faster. With dbt, analysts and engineers can collaborate on version-controlled SQL models, enforce testing and documentation standards, 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 reduce data debt, increase trust, and accelerate insights 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 and database admins seeking a domain-specific programming language
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Audience
Developers and database admins
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Audience
Developers interested in a language for building maintainable programs
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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
Free
Free Version
Free Trial
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Pricing
$100 per user 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 InformationNoSQL
Founded: 1996
United States
sourceforge.net/software/product/NoSQL/
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Company InformationSQL
Founded: 1974
sourceforge.net/software/product/SQL/
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Company InformationV Programming Language
United States
vlang.io
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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 Labs 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 for faster impact analysis. By embedding software engineering practices into pipeline development, dbt Labs helps data teams build reliable, production-grade pipelines — reducing data debt and accelerating time to insight. dbt Labs 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 Labs ensures that prepared datasets aren’t just quick fixes — they’re trusted, governed, and production-ready assets that scale with the business. dbt Labs modernizes the “T” in ETL. 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 Labs 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
dbForge SQL Decryptor
Brakeman
Claude Opus 4.1
Columns
Crux
DataLab
Decipad
Decube
DevKit
Gigasheet
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Integrations
dbForge SQL Decryptor
Brakeman
Claude Opus 4.1
Columns
Crux
DataLab
Decipad
Decube
DevKit
Gigasheet
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Integrations
dbForge SQL Decryptor
Brakeman
Claude Opus 4.1
Columns
Crux
DataLab
Decipad
Decube
DevKit
Gigasheet
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Integrations
dbForge SQL Decryptor
Brakeman
Claude Opus 4.1
Columns
Crux
DataLab
Decipad
Decube
DevKit
Gigasheet
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