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Description
Google Cloud's Dataplex serves as an advanced data fabric that empowers organizations to efficiently discover, manage, monitor, and govern their data across various platforms, including data lakes, warehouses, and marts, while maintaining uniform controls that ensure access to reliable data and facilitate large-scale analytics and AI initiatives. By offering a cohesive interface for data management, Dataplex streamlines processes like data discovery, classification, and metadata enhancement for diverse data types, whether structured, semi-structured, or unstructured, both within Google Cloud and external environments. It organizes data logically into business-relevant domains through lakes and data zones, making data curation, tiering, and archiving more straightforward. With its centralized security and governance features, Dataplex supports effective policy management, robust monitoring, and thorough auditing across fragmented data silos, thereby promoting distributed data ownership while ensuring global oversight. Furthermore, the platform includes automated data quality assessments and lineage tracking, which enhance the reliability and traceability of data, ensuring organizations can trust their data-driven decisions. By integrating these functionalities, Dataplex not only simplifies data management but also enhances collaboration within teams focused on analytics and AI.
Description
Version control, quality assurance, documentation, and modularity enable data teams to work together similarly to software engineering teams. It is crucial to address analytics errors with the same urgency as one would for bugs in a live product. A significant portion of the analytic workflow is still performed manually. Therefore, we advocate for workflows to be designed for execution with a single command. Data teams leverage dbt to encapsulate business logic, making it readily available across the organization for various purposes including reporting, machine learning modeling, and operational tasks. The integration of continuous integration and continuous deployment (CI/CD) ensures that modifications to data models progress smoothly through the development, staging, and production phases. Additionally, dbt Cloud guarantees uptime and offers tailored service level agreements (SLAs) to meet organizational needs. This comprehensive approach fosters a culture of reliability and efficiency within data operations.
API Access
Has API
API Access
Has API
Integrations
Google Cloud BigQuery
Azure Marketplace
Blotout
Cuckoo
DataHub
DataOps.live
Datafold
Decube
Flyte
Gemini
Integrations
Google Cloud BigQuery
Azure Marketplace
Blotout
Cuckoo
DataHub
DataOps.live
Datafold
Decube
Flyte
Gemini
Pricing Details
$0.060 per hour
Free Trial
Free Version
Pricing Details
$50 per user per month
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Founded
1998
Country
United States
Website
cloud.google.com/dataplex
Vendor Details
Company Name
dbt Labs
Founded
2016
Country
United States
Website
www.getdbt.com
Product Features
Data Governance
Access Control
Data Discovery
Data Mapping
Data Profiling
Deletion Management
Email Management
Policy Management
Process Management
Roles Management
Storage Management
Data Lineage
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 Management
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
Product Features
ETL
Data Analysis
Data Filtering
Data Quality Control
Job Scheduling
Match & Merge
Metadata Management
Non-Relational Transformations
Version Control