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Average Ratings 0 Ratings
Description
Easily create and execute highly parallel data transformation and processing tasks using U-SQL, R, Python, and .NET across vast amounts of data. With no need to manage infrastructure, you can process data on demand, scale up instantly, and incur costs only per job. Azure Data Lake Analytics allows you to complete big data tasks in mere seconds. There’s no infrastructure to manage since there are no servers, virtual machines, or clusters that require monitoring or tuning. You can quickly adjust the processing capacity, measured in Azure Data Lake Analytics Units (AU), from one to thousands for every job. Payment is based solely on the processing used for each job. Take advantage of optimized data virtualization for your relational sources like Azure SQL Database and Azure Synapse Analytics. Your queries benefit from automatic optimization, as processing is performed close to the source data without requiring data movement, thereby enhancing performance and reducing latency. Additionally, this setup enables organizations to efficiently utilize their data resources and respond swiftly to analytical needs.
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
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
Astro by Astronomer
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
Astro by Astronomer
Yes
Pricing Details
$2 per hour
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
No
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
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
azure.microsoft.com/en-us/services/data-lake-analytics/
Vendor Details
Company Name
Treeverse
Founded
2020
Country
Israel
Website
lakefs.io
Product Features
Big Data
Collaboration
No
Data Blends
No
Data Cleansing
No
Data Mining
No
Data Visualization
No
Data Warehousing
No
High Volume Processing
No
No-Code Sandbox
No
Predictive Analytics
No
Templates
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