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What is lakeFS?

lakeFS enables you to manage your data lake in a manner akin to source code management, promoting parallel experimentation pipelines alongside continuous integration and deployment for your data workflows. This innovative platform enhances the efficiency of engineers, data scientists, and analysts who are at the forefront of data-driven innovation. As an open-source tool, lakeFS significantly boosts the robustness and organization of data lakes built on object storage systems. With lakeFS, users can carry out dependable, atomic, and version-controlled actions on their data lakes, ranging from complex ETL workflows to sophisticated data science and analytics initiatives. It supports leading cloud storage providers such as AWS S3, Azure Blob Storage, and Google Cloud Storage (GCS), ensuring versatile compatibility. Moreover, lakeFS integrates smoothly with numerous contemporary data frameworks like Spark, Hive, AWS Athena, and Presto, facilitated by its API that aligns with S3. The platform's Git-like framework for branching and committing allows it to scale efficiently, accommodating vast amounts of data while utilizing the storage potential of S3, GCS, or Azure Blob. Additionally, lakeFS enhances team collaboration by enabling multiple users to simultaneously access and manipulate the same dataset without risk of conflict, thereby positioning itself as an essential resource for organizations that prioritize data-driven decision-making. This collaborative feature not only increases productivity but also fosters a culture of innovation within teams.

What is Ganymede?

Currently, critical information such as instrument settings, the latest service date, the identity of the analyst, and the experiment's duration remain unrecorded. This oversight leads to a loss of raw data, rendering it nearly impossible to modify or replicate analyses without considerable effort, while also complicating meta-analyses due to a lack of traceability. Consequently, the task of manually inputting primary analysis outcomes can become a significant burden that detracts from researchers' productivity. To address these challenges, we propose storing raw data in the cloud and automating analytical workflows to ensure continuous traceability. This approach allows for seamless integration of data into various platforms, including ELNs, LIMS, Excel, analytical applications, and pipelines. Furthermore, we are in the process of creating a data lake that consolidates all related information. This means that every piece of raw data, processed results, metadata, and even internal data from linked applications is securely stored in a unified cloud data lake for future access. With this system, analyses can be conducted automatically, and metadata can be added without any manual intervention. Additionally, results can be effortlessly sent to any application or pipeline, and can even be relayed back to the instruments for improved control, effectively streamlining the entire research workflow. This cutting-edge strategy not only enhances operational efficiency but also substantially elevates data management practices, ensuring that scientists can focus more on innovation rather than administrative tasks.

Media

Media

Integrations Supported

Amazon Athena
Amazon Kinesis
Amazon S3
Amazon SES
Amazon Web Services (AWS)
Apache Airflow
Apache Hive
Apache Spark
Astro by Astronomer
Azure Blob Storage
Databricks
Delta Lake
Google Cloud Storage
Hadoop
Jupyter Notebook
Looker
MLflow
MinIO
Presto

Integrations Supported

Microsoft Excel

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Online Training

Company Facts

Organization Name

Treeverse

Date Founded

2020

Company Location

Israel

Company Website

lakefs.io

Company Facts

Organization Name

Ganymede

Company Location

United States

Company Website

www.ganymede.bio/

Categories and Features

Data Management

Not specified

Categories and Features

Lab Automation

Not specified

Medical Lab

Not specified

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