dbt

dbt

dbt Labs
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About

SCIKIQ is a trailblazing no-code data platform that helps organizations integrate, curate, govern, and activate enterprise data across cloud, multi-cloud, hybrid, and on-premises environments. SCIKIQ works with the technology you already run, SAP, Oracle, Salesforce, databases, data lakes, warehouses, SaaS applications, AWS, Azure, and GCP, without forcing a rip-and-replace. At its heart is Contextual Intelligence. SCIKIQ understands the unique semantics of enterprise data, connecting technical metadata with business definitions, relationships, rules, quality, lineage, and governance. The result is trusted, contextualized data that people, analytics, applications, and AI can understand and use. SCIKIQ unifies the data lifecycle in one no-code platform: • Integrate — Connect structured and unstructured data across applications, databases, files, APIs, SAP, and real-time sources using 200+ connectors and no-code pipelines. • Curate — Profile, clean, transform, standardize, enrich, model, and contextualize data through automated data preparation. • Govern — Manage metadata, catalog, data quality, lineage, privacy, policies, access, and stewardship across the enterprise. • Activate — Deliver trusted data to BI, analytics, enterprise applications, data products, machine learning, AI copilots, and intelligent agents. Instead of stitching together disconnected tools, SCIKIQ brings data integration, ETL, transformation, data quality, governance, catalog, lineage, semantic models, knowledge graphs, data products, and intelligence together on one unified platform. SCIKIQ works on top of your existing data architecture, reducing engineering complexity, bridging skill gaps, and accelerating time to value. Recognized by Forrester, NASSCOM, YourStory, Inc42, and DataIQ for innovation in enterprise data and intelligence. SCIKIQ is The No-Code Data Platform That Works for Your Business.

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

Audience

BFSI, Manufacturing, Logistics, Retail, Healthcare and others

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

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Yearly License
Contract Pricing
Free Version
Free Trial

Pricing

$100 per user/ month
Free Version
Free Trial

Reviews/Ratings

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 4.8 / 5
design 4.8 / 5
support 4.2 / 5

Pros & Cons from Real Users

Pros

  • Ease of use and Features. Easy to setup, integrate, and get started quickly Less maintenance Out of the box CI/CD integration with Git Easy to learn.
  • - Credential and version management is offloaded to the cloud - Simple-to-use orchestration - Seamless state management - Integrated documentation and lineage - Collaborative development experience - Native CI/CD integration - Centralized logging and observability - Enterprise-grade access control and auditability - Easy environment management - Rapid onboarding for new users
  • We use dbt for our data transformations. It's been a game changer from a Data Engineering and Analytics Engineering standpoint. It has accelerated our migration from legacy systems and made our pipelines 80% faster. We have increased visibility in our projects, a catalog and many other data quality indicators.
  • dbt has been one of the most transformative tools in my data career. It gives teams a clean, maintainable way to translate business logic into reliable, production-grade data models. It standardizes the entire development lifecycle — modeling, testing, documentation, version control, CI/CD, and lineage — in a way that allows analytics engineers and data engineers to work with clarity and confidence. It’s the backbone of our governed analytics strategy. Exceptional developer workflow: Modular SQL, version control, built-in testing, documentation, and macros allow us to scale complex business logic with consistency and reliability. Scales with organizational change: dbt has allowed us to redesign core product and customer analytics with patterns that are resilient to future product launches and schema changes.

Cons

  • Limited product Usage metrics. Product usage insights/Metrics can be better. Metrics around AI usage by developers with in the product will help.
  • - Individual capabilities are not as robust as dedicated tools. for example, orchestration is simple to use but lacks the flexibility, customization, and advanced scheduling logic of dedicated orchestrators
  • I think that the pricing model can easily become a barrier. The cost per model run is a terrible bottleneck for us and affects our capacity to architect following best practices.
  • dbt IDE could be more flexible with Git operations. Advanced users would benefit from features like git stash, more granular branch management, and better conflict-resolution tools directly in the IDE. This would remove friction during rapid iteration or when working across multiple branches. More built-in patterns for complex incremental modeling would be helpful for teams dealing with very high data volumes and dynamic product schemas.

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

SCIKIQ
Founded: 2023
India
scikiq.com

Company Information

dbt Labs
Founded: 2016
United States
www.getdbt.com

Alternatives

Alternatives

Categories

SCIKIQ is an AI-native Agentic Data Management platform that transforms enterprise data into governed, reusable and AI-ready Data Products. At its core is the SCIKIQ Data Product Factory and Data Marketplace, built to operationalize Data-as-a-Product across the enterprise. The Data Product Factory enables teams and AI Agents to discover, create, govern, enrich and publish Data Products using trusted enterprise data, business context, semantics, quality and lineage. SCIKIQ Data Marketplace provides an internal and external marketplace to discover, share, consume and monetize Data Products, datasets, APIs, KPIs, analytics and AI-ready assets. Key capabilities include Agentic Data Management, Data Products, Data Product Factory, Data Marketplace, Data-as-a-Product, Data Mesh, Self-Service Data, Data Catalog, Data Governance, Data Quality, Data Lineage, Data Semantics, APIs and AI Agents. From raw enterprise data to governed Data Products—built for Analytics, Generative AI and

Recognized among the Top 34 AI-Augmented platforms globally by Forrester and ranked among India’s Top 10 DeepTech companies in AI & Analytics by NASSCOM, SCIKIQ is an AI-native Big Data and enterprise data platform built for the AI era. SCIKIQ connects and unifies data across SAP, databases, data warehouses, data lakes, cloud platforms, enterprise applications, and APIs without requiring organizations to replace or migrate their existing data stack. It creates a trusted, governed and AI-ready data foundation for Big Data, analytics, Business Intelligence, and enterprise AI. Key capabilities include Big Data integration, ETL/ELT, data pipelines, data transformation, data lakehouse, data preparation, data quality, metadata management, data catalog, data governance, data lineage, semantic intelligence, real-time analytics, and AI-powered analytics. With support for cloud, hybrid, and on-premise environments, SCIKIQ helps enterprises move faster to Generative AI and Agentic AI.

Recognized among the Top 34 AI-Augmented Business Intelligence platforms globally by Forrester, SCIKIQ is an AI-native Business Intelligence platform built for the next generation of enterprise decision-making. SCIKIQ brings Business Intelligence, enterprise analytics, Conversational AI, dashboards, data integration, governance, semantic intelligence, and Agentic AI together on a unified platform. It enables organizations to transform enterprise data into trusted, contextual, and AI-ready intelligence—without replacing their existing technology stack. Built for enterprise-wide BI, SCIKIQ connects data across SAP, databases, data warehouses, cloud platforms, Power BI, Tableau, and business applications to create a connected intelligence layer. Key capabilities include AI-powered Business Intelligence, Conversational Analytics, Enterprise 360, self-service BI, KPI analytics, semantic intelligence, data visualization, real-time analytics, data governance, data lineage & AI agents

SCIKIQ is an AI-native Data Catalog platform built to help enterprises discover, understand, govern and activate data for Analytics and AI. SCIKIQ combines Data Catalog, Data Discovery, Metadata Management, Data Lineage, Data Quality, Business Glossary and Data Semantics in one intelligent platform. Automatically catalog data across SAP, databases, data warehouses, data lakes, cloud platforms, APIs and enterprise applications. Search and discover datasets, tables, columns, metadata, business terms, KPIs, owners, relationships and lineage from a unified enterprise data catalog. Key capabilities include Automated Data Cataloging, Metadata Discovery, Metadata Management, Business Glossary, Data Classification, Data Profiling, Data Search, Data Lineage, Data Governance, Data Quality and Semantic Layer. SCIKIQ connects technical metadata with business meaning and context, creating a trusted catalog for Data Management, Business Intelligence, Generative AI and Agentic AI.

SCIKIQ is an AI-native Data Discovery platform that helps enterprises find, understand, classify and trust data across complex data environments. SCIKIQ combines Data Discovery, Data Catalog, Metadata Management, Data Search, Data Lineage, Data Profiling, Data Classification and Data Semantics in one unified platform. Automatically discover data across SAP, databases, data warehouses, data lakes, cloud platforms, APIs and enterprise applications. Search and explore datasets, tables, columns, metadata, business terms, KPIs and relationships through intelligent enterprise data discovery. SCIKIQ combines automated metadata discovery with best-in-class Data Lineage, Data Quality and a world-leading Data Semantics practice to provide business context behind enterprise data. Built for Data Discovery, Data Governance, Data Management, Data Cataloging, Business Intelligence, Analytics, Generative AI and Agentic AI.

Recognized among the Top 34 AI-Augmented platforms globally by Forrester and India’s Top 10 DeepTech companies in AI & Analytics by NASSCOM, SCIKIQ is an AI-native Enterprise Data Fabric built ground-up for AI. SCIKIQ creates a unified, intelligent data layer across SAP, databases, data warehouses, data lakes, cloud, APIs and enterprise applications—without migration, replatforming or replacing the existing data stack. SCIKIQ Data Fabric combines Data Integration, ETL/ELT, Data Pipelines, Data Quality, Data Governance, Data Catalog, Metadata Management, Data Lineage, Master Data Management, Data Observability and Data Semantics. Its world-leading Data Semantics practice connects technical data with business context, KPIs, relationships and meaning—creating an AI-ready enterprise data foundation. Built for Data Fabric, Data Management, Business Intelligence, Analytics, Generative AI and Agentic AI.

SCIKIQ is ranked alongside leading Data Governance platforms globally, with best-in-class Data Lineage, Data Quality and a world-leading Data Semantics practice. SCIKIQ delivers enterprise Data Governance, automated Data Lineage, Data Quality, Data Catalog, Metadata Management, Business Glossary, Data Discovery, Data Classification, Data Profiling, Data Observability, PII Management, Policy Management, Data Compliance and AI Governance in one platform. Track end-to-end Data Lineage across SAP, databases, data warehouses, data lakes, cloud, ETL pipelines, BI dashboards and enterprise applications. Improve Data Quality through automated profiling, validation, monitoring and quality rules. SCIKIQ Data Semantics connects metadata, business terms, KPIs, relationships and enterprise context to create trusted, AI-ready data. Built for Data Governance, Data Management, Regulatory Compliance, Business Intelligence, Generative AI and Agentic AI.

SCIKIQ Data Hub is AI-native Data Management Platform built ground-up for AI and designed to be the fastest path from enterprise data to Enterprise AI. Recognized among the Top 34 AI-Augmented platforms globally by Forrester and India’s Top 10 DeepTech companies in AI & Analytics by NASSCOM, SCIKIQ connects, governs and activates data across the enterprise. Integrate data from SAP, databases, data warehouses, data lakes, cloud platforms, APIs and enterprise applications without replatforming or disrupting your existing data stack. Built-in Data Governance, Data Quality, Data Catalog, Metadata Management and Data Lineage create trusted data by design. ETL/ELT, Data Integration, Data Pipelines, Data Transformation, Data Preparation and Semantic Intelligence turn fragmented data into a connected, AI-ready foundation. Built for Analytics, Business Intelligence, Generative AI and Agentic AI, SCIKIQ helps enterprises move from siloed data to trusted intelligence in weeks not years.

SCIKIQ delivers best-in-class Data Quality for enterprises that need trusted, accurate and AI-ready data. Built ground-up for AI, SCIKIQ combines Data Quality Management, Data Profiling, Data Cleansing, Data Validation, Data Monitoring and Data Observability across complex enterprise data environments. SCIKIQ provides automated Data Quality Rules, Data Validation, Data Standardization, Data Matching, Deduplication, Data Enrichment, Data Completeness, Accuracy Checks, Consistency Checks, Anomaly Detection and Data Quality Monitoring. Continuously measure and improve data quality across SAP, databases, data warehouses, data lakes, cloud platforms, ETL pipelines and enterprise applications. Integrated Data Lineage, Data Governance, Metadata Management and Data Semantics help identify where quality issues originate and understand their business impact. Trusted Data. Better Analytics. Reliable AI.

ETL

SCIKIQ is an AI-native ETL and ELT platform for fast, scalable enterprise Data Integration and Data Transformation. Build, automate and manage ETL pipelines across cloud, on-premise and hybrid data environments with no-code and AI-assisted automation. SCIKIQ combines ETL, ELT, Data Pipelines, Data Integration, Data Ingestion, Data Extraction, Data Transformation, Data Loading, Data Mapping, Data Migration, Data Replication, Change Data Capture (CDC), Batch Processing and Real-Time Data Integration. Connect SAP, ERP, CRM, databases, data warehouses, data lakes, SaaS applications, APIs, files and streaming data through 200+ pre-built connectors. Built-in Data Quality, Data Governance, Data Lineage, Metadata Management and Data Observability help create trusted pipelines and AI-ready data. From traditional ETL to modern ELT, real-time pipelines and AI-powered integration, SCIKIQ provides one platform to move, transform and activate enterprise data.

SCIKIQ is an AI-native Data Integration platform built to connect, move and transform enterprise data across any system, cloud or environment. From ETL/ELT and real-time data pipelines to SAP Integration and API Integration, SCIKIQ provides a unified platform for modern enterprise data integration. Connect SAP S/4HANA, SAP ECC, databases, data warehouses, data lakes, SaaS applications, APIs, files and streaming sources using 200+ pre-built connectors. Build no-code data pipelines for batch, micro-batch, real-time streaming and Change Data Capture (CDC) across cloud, on-premise and hybrid environments. SCIKIQ also includes an API Hub to create, manage, govern and reuse enterprise APIs, helping organizations connect applications, data and AI services through a common integration layer. With No-code platform & built-in Data Quality, Data Governance, Data Lineage, Observability and AI-assisted automation, SCIKIQ goes beyond traditional ETL software to deliver trusted, AI-ready data.

SCIKIQ is an AI-native Master Data Management (MDM) platform built to create trusted, unified and AI-ready master data across the enterprise. SCIKIQ combines Master Data Management, MDM, Data Quality, Data Governance, Data Integration and Data Semantics in one platform. Create a trusted Golden Record and Single Source of Truth across customers, products, suppliers, vendors, employees and other business entities. SCIKIQ supports Customer 360, Product 360, Supplier 360, Multi-Domain MDM, Reference Data Management and Hierarchy Management. Key capabilities include Entity Resolution, Data Matching, Deduplication, Data Cleansing, Data Standardization, Data Validation, Data Enrichment, Data Profiling, Data Stewardship, Metadata Management and Data Lineage. Connect master data across SAP, ERP, CRM, databases, data warehouses, cloud and enterprise applications to power Analytics, Business Intelligence, Generative AI and Agentic AI.

Categories

dbt 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.

ETL

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.

Big Data Features

Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates

Data Quality Features

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

ETL Features

Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control

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

Data Discovery Features

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual 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

Integration Features

Dashboard
ETL - Extract / Transform / Load
Metadata Management
Multiple Data Sources
Web Services

Master Data Management Features

Data Governance
Data Masking
Data Source Integrations
Hierarchy Management
Match & Merge
Metadata Management
Multi-Domain
Process Management
Relationship Mapping
Visualization

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

Snowflake
AccessOwl
Blotout
Cuckoo
Dagster
Datafold
Flyte
Hex
HubSpot CRM
Matia
Metaphor
Metaplane
Mode
MySQL
OpenMetadata
Openbridge
Snowflake Cortex AI
TROCCO
VeloDB
nao

Integrations

Snowflake
AccessOwl
Blotout
Cuckoo
Dagster
Datafold
Flyte
Hex
HubSpot CRM
Matia
Metaphor
Metaplane
Mode
MySQL
OpenMetadata
Openbridge
Snowflake Cortex AI
TROCCO
VeloDB
nao
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Claim dbt and update features and information