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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

A framework for distributed data integration that streamlines essential functions of Big Data integration, including data ingestion, replication, organization, and lifecycle management, is designed for both streaming and batch data environments. It operates as a standalone application on a single machine and can also function in an embedded mode. Additionally, it is capable of executing as a MapReduce application across various Hadoop versions and offers compatibility with Azkaban for initiating MapReduce jobs. In standalone cluster mode, it features primary and worker nodes, providing high availability and the flexibility to run on bare metal systems. Furthermore, it can function as an elastic cluster in the public cloud, maintaining high availability in this setup. Currently, Gobblin serves as a versatile framework for creating various data integration applications, such as ingestion and replication. Each application is usually set up as an independent job and managed through a scheduler like Azkaban, allowing for organized execution and management of data workflows. This adaptability makes Gobblin an appealing choice for organizations looking to enhance their data integration processes.

Description

Azkaban serves as a distributed Workflow Manager developed by LinkedIn to address the complexities of Hadoop job dependencies. There were instances where jobs required a specific order of execution, ranging from ETL processes to data analysis applications. Following the release of version 3.0, Azkaban offers two distinct operational modes: the standalone “solo-server” mode and the distributed multiple-executor mode. The solo-server mode utilizes an embedded H2 database, allowing both the web server and executor server to operate within the same process, making it ideal for initial experimentation or small-scale applications. In contrast, the multiple-executor mode is designed for serious production environments, requiring a MySQL database configured with a master-slave arrangement. Ideally, the web server and executor servers are hosted on separate machines to ensure that system upgrades and maintenance do not disrupt user experience. This configuration not only enhances Azkaban’s robustness but also significantly improves its scalability, making it suitable for larger, more complex workflows. By offering these two modes, Azkaban caters to a wide range of user needs, from casual experimentation to enterprise-level deployments.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Hadoop
IBM Databand
MySQL

Integrations

Hadoop
IBM Databand
MySQL

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Apache Software Foundation

Country

United States

Website

gobblin.apache.org

Vendor Details

Company Name

Azkaban

Website

azkaban.github.io

Product Features

Big Data

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

Product Features

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