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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.

Description

lakeFS allows you to control your data lake similarly to how you manage your source code, facilitating parallel pipelines for experimentation as well as continuous integration and deployment for your data. This platform streamlines the workflows of engineers, data scientists, and analysts who are driving innovation through data. As an open-source solution, lakeFS enhances the resilience and manageability of object-storage-based data lakes. With lakeFS, you can execute reliable, atomic, and versioned operations on your data lake, encompassing everything from intricate ETL processes to advanced data science and analytics tasks. It is compatible with major cloud storage options, including AWS S3, Azure Blob Storage, and Google Cloud Storage (GCS). Furthermore, lakeFS seamlessly integrates with a variety of modern data frameworks such as Spark, Hive, AWS Athena, and Presto, thanks to its API compatibility with S3. The platform features a Git-like model for branching and committing that can efficiently scale to handle exabytes of data while leveraging the storage capabilities of S3, GCS, or Azure Blob. In addition, lakeFS empowers teams to collaborate more effectively by allowing multiple users to work on the same dataset without conflicts, making it an invaluable tool for data-driven organizations.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon Athena No 
Amazon Kinesis No 
Amazon S3 No 
Amazon SES No 
Amazon Web Services (AWS) No 
Apache Airflow No 
Apache Flink No 
Apache Hive No 
Apache Kafka No 
Apache Spark No 
Astro by Astronomer No 
Azure Blob Storage No 
Databricks No 
Delta Lake No 
Google Cloud Storage No 
Hadoop No 
Jupyter Notebook No 
MLflow No 
Microsoft Excel Yes 
Presto No 

Integrations

Amazon Athena Yes 
Amazon Kinesis Yes 
Amazon S3 Yes 
Amazon SES Yes 
Amazon Web Services (AWS) Yes 
Apache Airflow Yes 
Apache Flink Yes 
Apache Hive Yes 
Apache Kafka Yes 
Apache Spark Yes 
Astro by Astronomer Yes 
Azure Blob Storage Yes 
Databricks Yes 
Delta Lake Yes 
Google Cloud Storage Yes 
Hadoop Yes 
Jupyter Notebook Yes 
MLflow Yes 
Microsoft Excel No 
Presto 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 No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

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

Types of Training

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

Vendor Details

Company Name

Ganymede

Country

United States

Website

www.ganymede.bio/

Vendor Details

Company Name

Treeverse

Founded

2020

Country

Israel

Website

lakefs.io

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 

Product Features

Data Management

Customer Data No 
Data Analysis No 
Data Capture No 
Data Integration No 
Data Migration No 
Data Quality Control No 
Data Security No 
Information Governance No 
Master Data Management No 
Match & Merge No 

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