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What is PHEMI Health DataLab?

In contrast to many conventional data management systems, PHEMI Health DataLab is designed with Privacy-by-Design principles integral to its foundation, rather than as an additional feature. This foundational approach offers significant benefits, including: It allows analysts to engage with data while adhering to strict privacy standards. It incorporates a vast and adaptable library of de-identification techniques that can conceal, mask, truncate, group, and anonymize data effectively. It facilitates the creation of both dataset-specific and system-wide pseudonyms, enabling the linking and sharing of information without the risk of data leaks. It gathers audit logs that detail not only modifications made to the PHEMI system but also patterns of data access. It automatically produces de-identification reports that are accessible to both humans and machines, ensuring compliance with enterprise governance risk management. Instead of having individual policies for each data access point, PHEMI provides the benefit of a unified policy that governs all access methods, including Spark, ODBC, REST, exports, and beyond, streamlining data governance in a comprehensive manner. This integrated approach not only enhances privacy protection but also fosters a culture of trust and accountability within the organization.

What is Google Cloud Datalab?

Cloud Datalab serves as an intuitive interactive platform tailored for data exploration, analysis, visualization, and machine learning. This powerful tool, created for the Google Cloud Platform, empowers users to investigate, transform, and visualize their data while efficiently developing machine learning models. Utilizing Compute Engine, it seamlessly integrates with a variety of cloud services, allowing you to focus entirely on your data science initiatives without unnecessary interruptions. Constructed on the foundation of Jupyter (formerly IPython), Cloud Datalab enjoys the advantages of a dynamic ecosystem filled with modules and an extensive repository of knowledge. It facilitates the analysis of data across BigQuery, AI Platform, Compute Engine, and Cloud Storage, using Python, SQL, and JavaScript for user-defined functions in BigQuery. Whether your data is in the megabytes or terabytes, Cloud Datalab is adept at addressing your requirements. You can easily execute queries on vast datasets in BigQuery, analyze local samples of data, and run training jobs on large datasets within the AI Platform without any hindrances. This remarkable flexibility makes Cloud Datalab an indispensable tool for data scientists who seek to optimize their workflows and boost their productivity, ultimately leading to more insightful data-driven decisions.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Apache Hive
Apache Ranger
Apache Spark
Cloudera
Hadoop
Microsoft Azure
Microsoft Power BI
Tableau

Integrations Supported

DataLab
Google Cloud Platform
Google Workspace
Jupyter Notebook

API Availability

Has API

API Availability

Pricing Information

Contact PHEMI to learn about our value-based pricing.

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

PHEMI Systems

Date Founded

2013

Company Location

Canada

Company Website

www.phemi.com

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

cloud.google.com/datalab

Categories and Features

Big Data

Data Warehousing
High Volume Processing
Predictive Analytics

Data Governance

Access Control
Data Discovery
Data Mapping
Data Profiling
Policy Management
Process Management
Roles Management
Storage Management

Data Lineage

Not specified

Data Management

Data Analysis
Data Integration
Data Security
Information Governance

Data Masking

Not specified

Data Privacy Management

Access Control
CCPA Compliance
Consent Management
Data Mapping
GDPR Compliance
Policy Management
Risk Management
Sensitive Data Identification

Data Security

Data Classification
Data Discovery
Data Masking
Data-Centric Security
Encryption
Identity / Access Management
Policy Management
Sensitive Data Compliance

Hybrid Cloud Storage

Not specified

Categories and Features

Data Analysis

Not specified

Data Visualization

Not specified

Machine Learning

Not specified

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