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Description

A data lake serves as a comprehensive repository designed for handling extensive data and artificial intelligence operations, accommodating both structured and unstructured data at any volume. It is essential for organizations looking to harness the power of Data Lake Formation (DLF), which simplifies the creation of a cloud-native data lake environment. DLF integrates effortlessly with various computing frameworks while enabling centralized management of metadata and robust enterprise-level permission controls. It systematically gathers structured, semi-structured, and unstructured data, ensuring substantial storage capabilities, and employs a design that decouples computing resources from storage solutions. This architecture allows for on-demand resource planning at minimal costs, significantly enhancing data processing efficiency to adapt to swiftly evolving business needs. Furthermore, DLF is capable of automatically discovering and consolidating metadata from multiple sources, effectively addressing issues related to data silos. Ultimately, this functionality streamlines data management, making it easier for organizations to leverage their data assets.

Description

Information such as instrument configurations, the most recent service date, the analyst's identity, and the duration of the experiment is currently not recorded. This results in the loss of raw data, making it nearly impossible to alter or rerun analyses without significant effort, and the absence of traceability complicates meta-analyses. The process of simply entering primary analysis outcomes can become a burden that hinders scientists’ efficiency. However, by storing raw data in the cloud and automating the analytical processes, we ensure traceability throughout. Subsequently, this data can be integrated into various platforms such as ELNs, LIMS, Excel, analysis applications, and pipelines. Moreover, we continuously develop a data lake that accumulates all this information. This means that all your raw data, processed results, metadata, and even the internal data from connected applications are securely preserved forever within a unified cloud data lake. Analyses can be executed automatically, and metadata can be appended without manual input. Additionally, results can be seamlessly transmitted to any application or pipeline, and even back to the instruments for enhanced control, thereby streamlining the entire research process. This innovative approach not only increases efficiency but also significantly improves data management.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Alibaba Cloud Yes 
Microsoft Excel No 

Integrations

Alibaba Cloud No 
Microsoft Excel Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
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 Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs No 
Webinars No 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Alibaba Cloud

Founded

2008

Country

China

Website

www.alibabacloud.com/es/product/datalake-formation

Vendor Details

Company Name

Ganymede

Country

United States

Website

www.ganymede.bio/

Product Features

Product Features

Medical Lab

Audit Trail No 
Data Analysis Auditing No 
Data Security No 
EMR Interface No 
Fax Management No 
Lab Instrument Interface No 
Multi-Location Printing No 
Online Instrumentation No 
Physician Test Panels No 
Procedure-Based Billing No 
Sample Tracking No 

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