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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 Oracle Big Data Service?

Oracle Big Data Service makes it easy for customers to deploy Hadoop clusters by providing a variety of virtual machine configurations, from single OCPUs to dedicated bare metal options. Users have the choice between high-performance NVMe storage and more economical block storage, along with the ability to scale their clusters according to their requirements. This service enables the rapid creation of Hadoop-based data lakes that can either enhance or supplement existing data warehouses, ensuring that data remains both accessible and well-managed. Users can efficiently query, visualize, and transform their data, facilitating data scientists in building machine learning models using an integrated notebook that accommodates R, Python, and SQL. Additionally, the platform supports the conversion of customer-managed Hadoop clusters into a fully-managed cloud service, which reduces management costs and enhances resource utilization, thereby streamlining operations for businesses of varying sizes. By leveraging this service, companies can dedicate more time to extracting valuable insights from their data rather than grappling with the intricacies of managing their clusters. This ultimately leads to more efficient data-driven decision-making processes.

Media

Media

Integrations Supported

Hadoop
Amazon Athena
Amazon Kinesis
Amazon S3
Amazon SES
Amazon Web Services (AWS)
Apache Airflow
Apache Hive
Apache Kafka
Apache Spark
Astro by Astronomer
Azure Blob Storage
Databricks Data Intelligence Platform
Google Cloud Storage
Jupyter Notebook
Looker
MLflow
Oracle Cloud Infrastructure
Presto
SimpleKPI

Integrations Supported

Hadoop
Amazon Athena
Amazon Kinesis
Amazon S3
Amazon SES
Amazon Web Services (AWS)
Apache Airflow
Apache Hive
Apache Kafka
Apache Spark
Astro by Astronomer
Azure Blob Storage
Databricks Data Intelligence Platform
Google Cloud Storage
Jupyter Notebook
Looker
MLflow
Oracle Cloud Infrastructure
Presto
SimpleKPI

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$0.1344 per hour
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Treeverse

Date Founded

2020

Company Location

Israel

Company Website

lakefs.io

Company Facts

Organization Name

Oracle

Date Founded

1977

Company Location

United States

Company Website

www.oracle.com/big-data/big-data-service/

Categories and Features

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

Categories and Features

Big Data

Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates

Popular Alternatives

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