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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 Azure Data Lake Analytics?

Easily construct and implement highly parallelized data transformation and processing tasks using U-SQL, R, Python, and .NET across extensive datasets. There’s no requirement to manage any infrastructure, allowing you to process data on demand, scale up in an instant, and pay only for completed jobs. Harness the power of Azure Data Lake Analytics to perform large-scale data operations in just seconds. You won’t have to worry about server management, virtual machines, or clusters that need maintenance or fine-tuning. With Azure Data Lake Analytics, you can rapidly adjust processing capabilities, measured in Azure Data Lake Analytics Units (AU), from a single unit to thousands for each job as needed. You are billed solely for the processing power used during each task. The optimized data virtualization of your relational sources, such as Azure SQL Database and Azure Synapse Analytics, allows you to interact with all your data seamlessly. Your queries benefit from automatic optimization, which brings processing closer to where the original data resides, consequently minimizing data movement, boosting performance, and reducing latency. This capability ensures that you can tackle even the most challenging data tasks with exceptional efficiency and speed, ultimately transforming the way you handle data analytics.

Media

Media

Integrations Supported

Amazon Athena
Amazon Kinesis
Amazon S3
Amazon SES
Apache Airflow
Apache Flink
Apache Kafka
Apache Spark
Astro by Astronomer
Azure Blob Storage
Azure Data Lake
Databricks Data Intelligence Platform
Google Cloud Storage
Hadoop
Jupyter Notebook
Looker
MLflow
MinIO
Openbridge
SimpleKPI

Integrations Supported

Amazon Athena
Amazon Kinesis
Amazon S3
Amazon SES
Apache Airflow
Apache Flink
Apache Kafka
Apache Spark
Astro by Astronomer
Azure Blob Storage
Azure Data Lake
Databricks Data Intelligence Platform
Google Cloud Storage
Hadoop
Jupyter Notebook
Looker
MLflow
MinIO
Openbridge
SimpleKPI

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$2 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

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/services/data-lake-analytics/

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

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