Databricks Data Intelligence PlatformDatabricks
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dbtdbt Labs
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About
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.
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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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Audience
Organizations that want all their data, analytics and AI on one unified data platform
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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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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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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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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationDatabricks
Founded: 2013
United States
databricks.com
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Company Informationdbt Labs
Founded: 2016
United States
www.getdbt.com
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Alternatives |
Alternatives |
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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
ETL Features
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
Artificial Intelligence Features
Chatbot
For eCommerce
For Healthcare
For Sales
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
Business Intelligence Features
Ad Hoc Reports
Benchmarking
Budgeting & Forecasting
Dashboard
Data Analysis
Key Performance Indicators
Natural Language Generation (NLG)
Performance Metrics
Predictive Analytics
Profitability Analysis
Strategic Planning
Trend / Problem Indicators
Visual Analytics
Dashboard Features
Annotations
Data Source Integrations
Functions / Calculations
Interactive
KPIs
OLAP
Private Dashboards
Public Dashboards
Scorecards
Themes
Visual Analytics
Widgets
Data Analysis Features
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Data Fabric Features
Data Access Management
Data Analytics
Data Collaboration
Data Lineage Tools
Data Networking / Connecting
Metadata Functionality
No Data Redundancy
Persistent Data Management
Data Governance Features
Access Control
Data Discovery
Data Mapping
Data Profiling
Deletion Management
Email Management
Policy Management
Process Management
Roles Management
Storage Management
Data Management Features
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
Data Science Features
Access Control
Advanced Modeling
Audit Logs
Data Discovery
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports
Data Visualization Features
Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery
Data Warehouse Features
Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge
Machine Learning Features
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
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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
ETL Features
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control
Data Preparation Features
Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface
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Integrations
Acryl Data
Amazon Redshift
Collate
Dagster
DataHub
Decube
Flyte
GetDot.ai
Google Cloud BigQuery
Lightdash
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Integrations
Acryl Data
Amazon Redshift
Collate
Dagster
DataHub
Decube
Flyte
GetDot.ai
Google Cloud BigQuery
Lightdash
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