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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 Dremio?

Dremio offers rapid query capabilities along with a self-service semantic layer that interacts directly with your data lake storage, eliminating the need to transfer data into exclusive data warehouses, and avoiding the use of cubes, aggregation tables, or extracts. This empowers data architects with both flexibility and control while providing data consumers with a self-service experience. By leveraging technologies such as Apache Arrow, Data Reflections, Columnar Cloud Cache (C3), and Predictive Pipelining, Dremio simplifies the process of querying data stored in your lake. An abstraction layer facilitates the application of security and business context by IT, enabling analysts and data scientists to access and explore data freely, thus allowing for the creation of new virtual datasets. Additionally, Dremio's semantic layer acts as an integrated, searchable catalog that indexes all metadata, making it easier for business users to interpret their data effectively. This semantic layer comprises virtual datasets and spaces that are both indexed and searchable, ensuring a seamless experience for users looking to derive insights from their data. Overall, Dremio not only streamlines data access but also enhances collaboration among various stakeholders within an organization.

Media

Media

Integrations Supported

Looker
Amazon Kinesis
Amazon S3
Apache Airflow
Apache Flink
Apache Spark
Azure Blob Storage
Hadoop
Jupyter Notebook
MinIO
Presto

Integrations Supported

Looker
Azure Marketplace
DashboardFox
DataClarity Unlimited Analytics
Emgage
HPE Ezmeral
Preset
PuppyGraph
Tableau
witboost

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

Not specified

Training Options

Documentation Hub
Webinars

Training Options

Not specified

Company Facts

Organization Name

Treeverse

Date Founded

2020

Company Location

Israel

Company Website

lakefs.io

Company Facts

Organization Name

Dremio

Date Founded

2015

Company Location

United States

Company Website

www.dremio.com

Categories and Features

Data Management

Not specified

Categories and Features

Big Data

Not specified

Data Engineering

Not specified

Data Lake

Not specified

Data Lineage

Not specified

Data Virtualization

Not specified

Data Warehouse

Not specified

Query Engines

Not specified

Semantic Layer

Not specified

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