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Description
Rivery’s ETL platform consolidates, transforms, and manages all of a company’s internal and external data sources in the cloud.
Key Features:
Pre-built Data Models: Rivery comes with an extensive library of pre-built data models that enable data teams to instantly create powerful data pipelines.
Fully managed: A no-code, auto-scalable, and hassle-free platform. Rivery takes care of the back end, allowing teams to spend time on mission-critical priorities rather than maintenance.
Multiple Environments: Rivery enables teams to construct and clone custom environments for specific teams or projects.
Reverse ETL: Allows companies to automatically send data from cloud warehouses to business applications, marketing clouds, CPD’s, and more.
Description
dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use.
With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Amazon Redshift
Yes
Databricks
Yes
Google Cloud BigQuery
Yes
Orchestra
Yes
Snowflake
Yes
Acryl Data
No
Adjust
Yes
Colrows
No
Docebo
Yes
Firebolt
Yes
Integrations
Amazon Redshift
Yes
Databricks
Yes
Google Cloud BigQuery
Yes
Orchestra
Yes
Snowflake
Yes
Acryl Data
Yes
Adjust
No
Colrows
Yes
Docebo
No
Firebolt
No
Pricing Details
$0.75 Per Credit
Rivery has pricing plans ranging from self service to enterprise. All plans include unlimited data sources, unlimited destinations, transformations, and full workflow orchestration features. Rivery’s usage-based pricing is based on pipeline executions, not number of rows or compute hours. Enjoy simple, flexible plans that scale as your business grows.
Free Trial
Yes
Free Version
No
Pricing Details
$100 per user/ month
Free Trial
Yes
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Rivery
Founded
2017
Country
United States
Website
rivery.io
Vendor Details
Company Name
dbt Labs
Founded
2016
Country
United States
Website
www.getdbt.com
Product Features
Data Extraction
Disparate Data Collection
Yes
Document Extraction
No
Email Address Extraction
No
IP Address Extraction
No
Image Extraction
No
Phone Number Extraction
No
Pricing Extraction
No
Web Data Extraction
Yes
Data Management
Customer Data
Yes
Data Analysis
Yes
Data Capture
Yes
Data Integration
Yes
Data Migration
Yes
Data Quality Control
Yes
Data Security
Yes
Information Governance
Yes
Master Data Management
Yes
Match & Merge
Yes
Data Management Platforms (DMP)
Ad Network Integration
No
Analytics / ROI Tracking
Yes
Audience Targeting
No
Behavioral Analytics
No
CRM
No
Campaign Management
No
Competitive Analysis
No
Customer Journey Mapping
No
Data Capture / Transfer
Yes
Data Classification
No
Data Visualization
Yes
Data Preparation
Collaboration Tools
No
Data Access
Yes
Data Blending
Yes
Data Cleansing
Yes
Data Governance
Yes
Data Mashup
Yes
Data Modeling
Yes
Data Transformation
Yes
Machine Learning
No
Visual User Interface
Yes
ETL
Data Analysis
Yes
Data Filtering
Yes
Data Quality Control
Yes
Job Scheduling
Yes
Match & Merge
Yes
Metadata Management
Yes
Non-Relational Transformations
Yes
Version Control
Yes
Integration
Dashboard
Yes
ETL - Extract / Transform / Load
Yes
Metadata Management
Yes
Multiple Data Sources
Yes
Web Services
Yes
Product Features
Big Data
Your knowledge is based on information available until October 2023.
Collaboration
Yes
Data Blends
No
Data Cleansing
Yes
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
No
No-Code Sandbox
No
Predictive Analytics
No
Templates
No
Data Lineage
Database Change Impact Analysis
Yes
Filter Lineage Links
Yes
Implicit Connection Discovery
No
Lineage Object Filtering
No
Object Lineage Tracing
No
Point-in-Time Visibility
No
User/Client/Target Connection Visibility
No
Visual & Text Lineage View
No
Data Pipeline
dbt serves as the backbone for the transformation segment of contemporary data pipelines. After data is brought into a warehouse or lakehouse, dbt empowers teams to refine, structure, and document it, making it suitable for analytics and artificial intelligence applications. With dbt, teams can: - Scale the transformation of unrefined data using SQL and Jinja. - Manage workflows with integrated dependency tracking and scheduling capabilities. - Build trust through automated testing and ongoing integration processes. - Map data lineage across models and columns for quicker impact assessments. By incorporating software engineering methodologies into pipeline development, dbt assists data teams in creating dependable, production-ready pipelines that expedite the journey to insights and provide data primed for AI utilization.
Data Preparation
dbt enhances data preparation by providing a structured and scalable approach for teams to clean, transform, and organize raw data within the warehouse environment. Rather than relying on isolated spreadsheets or manual processes, dbt leverages SQL alongside established software engineering practices to ensure that data preparation is consistent, dependable, and collaborative. Utilizing dbt allows teams to: - Clean and standardize their data through reusable models that are version-controlled. - Implement business logic uniformly across all data sets. - Conduct automated tests to validate outputs prior to making data available to analysts. - Document findings and share relevant context, ensuring that every prepared dataset includes lineage and definitions. By treating data preparation as a coding process, dbt guarantees that the datasets created are not merely temporary solutions but are reliable, governed assets that are ready for production and can grow alongside the business.
Collaboration Tools
Yes
Data Access
No
Data Blending
Yes
Data Cleansing
Yes
Data Governance
No
Data Mashup
No
Data Modeling
No
Data Transformation
No
Machine Learning
No
Visual User Interface
No
Data Quality
Your knowledge is based on information available until October 2023.
Address Validation
No
Data Deduplication
No
Data Discovery
No
Data Profililng
No
Master Data Management
No
Match & Merge
No
Metadata Management
No
ETL
dbt revolutionizes the transformation aspect of ETL processes. By moving away from outdated pipelines and opaque transformations, dbt enables data teams to create, validate, and document their transformations directly within their data warehouse or lakehouse. With dbt, teams are equipped to: - Convert raw data into analytics-ready models utilizing SQL and Jinja. - Maintain data integrity through integrated testing, version control, and continuous integration/continuous deployment (CI/CD). - Streamline workflows across teams by using reusable models and centralized documentation. - Utilize contemporary platforms such as Snowflake, Databricks, BigQuery, and Redshift for efficient and scalable transformations. By prioritizing the transformation layer, dbt allows organizations to accelerate the development of data pipelines, minimize data liabilities, and provide reliable insights more swiftly—complementing the ingestion and loading components of a modern ELT architecture.
Data Analysis
No
Data Filtering
Yes
Data Quality Control
Yes
Job Scheduling
No
Match & Merge
No
Metadata Management
No
Non-Relational Transformations
No
Version Control
No
