Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
The contemporary data workspace transforms the accessibility of your data assets, making everything from data tables to BI reports easily discoverable. With our robust search algorithms and user-friendly browsing experience, locating the right asset becomes effortless. Atlan simplifies the identification of poor-quality data through the automatic generation of data quality profiles. This includes features like variable type detection, frequency distribution analysis, missing value identification, and outlier detection, ensuring you have comprehensive support. By alleviating the challenges associated with governing and managing your data ecosystem, Atlan streamlines the entire process. Additionally, Atlan’s intelligent bots analyze SQL query history to automatically construct data lineage and identify PII data, enabling you to establish dynamic access policies and implement top-notch governance. Even those without technical expertise can easily perform queries across various data lakes, warehouses, and databases using our intuitive query builder that resembles Excel. Furthermore, seamless integrations with platforms such as Tableau and Jupyter enhance collaborative efforts around data, fostering a more connected analytical environment. Thus, Atlan not only simplifies data management but also empowers users to leverage data effectively in their decision-making processes.
Description
In response to the stringent quality requirements set by the automotive sector, semiconductor manufacturers are increasingly adopting Part Average Testing (PAT) to bolster the reliability of their products. This method focuses on identifying and eliminating "outlier" components that may pass conventional testing yet display unusual traits, thereby mitigating long-term quality and reliability concerns. By performing statistical analyses on a range of devices and modifying the pass/fail thresholds, PAT enables the early detection of these problematic parts, ensuring that only the highest quality components are included in production shipments. While Part Average Testing (PAT), as outlined in the Automotive Electronics Council AEC-Q001-Rev C specifications, primarily addresses DPM techniques for normal (Gaussian) distributions, many real-world scenarios involve distributions that do not conform to this norm. Consequently, it is essential to employ tailored PAT outlier detection strategies to prevent significant yield losses or erroneous identifications of outliers. To meet these challenges, PAT-Man emerges as a robust solution for implementing effective Part Average Testing (PAT). This innovative tool not only enhances the reliability of semiconductor components but also streamlines the testing process, ultimately benefiting manufacturers and consumers alike.
API Access
Has API
No
API Access
Has API
No
Integrations
Anomalo
Yes
Apigene
Yes
Colrows
Yes
Google Data Studio
Yes
MCPTotal
Yes
Mindfuel
Yes
STRM
Yes
Integrations
Anomalo
No
Apigene
No
Colrows
No
Google Data Studio
No
MCPTotal
No
Mindfuel
No
STRM
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
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
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)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Atlan
Founded
2018
Country
India
Website
atlan.com
Vendor Details
Company Name
Galaxy
Country
United States
Website
www.galaxysemi.com/products/pat-man
Product Features
Big Data
Collaboration
Yes
Data Blends
No
Data Cleansing
No
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
Yes
No-Code Sandbox
No
Predictive Analytics
No
Templates
No
Data Discovery
Contextual Search
Yes
Data Classification
Yes
Data Matching
Yes
False Positives Reduction
No
Self Service Data Preparation
Yes
Sensitive Data Identification
Yes
Visual Analytics
No
Data Fabric
Data Access Management
No
Data Analytics
No
Data Collaboration
No
Data Lineage Tools
No
Data Networking / Connecting
No
Metadata Functionality
No
No Data Redundancy
No
Persistent Data Management
No
Data Governance
Access Control
Yes
Data Discovery
Yes
Data Mapping
Yes
Data Profiling
Yes
Deletion Management
No
Email Management
No
Policy Management
Yes
Process Management
No
Roles Management
Yes
Storage Management
No
Data Lineage
Database Change Impact Analysis
No
Filter Lineage Links
No
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 Management
Customer Data
No
Data Analysis
No
Data Capture
No
Data Integration
No
Data Migration
No
Data Quality Control
Yes
Data Security
No
Information Governance
Yes
Master Data Management
Yes
Match & Merge
No
Data Quality
Address Validation
No
Data Deduplication
No
Data Discovery
Yes
Data Profililng
Yes
Master Data Management
Yes
Match & Merge
No
Metadata Management
Yes
ETL
Data Analysis
No
Data Filtering
No
Data Quality Control
Yes
Job Scheduling
Yes
Match & Merge
No
Metadata Management
Yes
Non-Relational Transformations
No
Version Control
Yes
Master Data Management
Data Governance
Yes
Data Masking
Yes
Data Source Integrations
Yes
Hierarchy Management
No
Match & Merge
No
Metadata Management
No
Multi-Domain
No
Process Management
No
Relationship Mapping
No
Visualization
No
PIM
Content Syndication
No
Data Modeling
No
Data Quality Control
Yes
Digital Asset Management
No
Documentation Management
Yes
Master Record Management
No
Version Control
Yes
Product Features
Engineering
2D Drawing
No
3D Modeling
No
Chemical Engineering
No
Civil Engineering
No
Collaboration
No
Design Analysis
No
Design Export
No
Document Management
No
Electrical Engineering
No
Mechanical Engineering
No
Mechatronics
No
Presentation Tools
No
Structural Engineering
No