KETL

KETL is a production ready Extract, transform, and load (ETL) platform. The engine is built upon an open, multi-threaded, XML-based architecture.

KETL is designed to assist in the development and deployment of data integration efforts which require ETL and scheduling. The software allows management of complex manipulation of data quickly and efficiently while leveraging an open source data integration platform.

The KETL engine consists of a multi-threaded server that manages various job executors. Each executor performs a specific function. The job executors fall into the following categories: SQL, OS, XML, Sessionizer and Empty.

KETL 2.1.33

Price Free to download Size 9.9MB License GNU LGPL & GPL Developer Kinetic Networks, Inc Website www.ketl.org System Requirements Java 1.5 SDK Relational database (e.g. PostgreSQL, Oracle or MySQL) MySQL JDBC Connectivity Oracle JDBC connectivity Optional: Complex transformations sun.tools.jar log4j Web Services class libraries XML Parsing - saxon8 PostgreSQL thin JDBC drivers Support Sites: Documentation, SourceForge Project Page, Forum Selected Reviews:

Features include:

• Scalable, platform independent ETL engine–enables complex ETL transformations to be executed in a highly efficient manner. Supports multiple CPU’s and 64-bit servers.
• Job execution and scheduling manager–dependency-driven job execution model supports multiple job types, conditional exception handling, email notification and time-based scheduling. Job types fall into three categories, with support for additional executors via the KETL API
• SQL–executes pre-defined SQL statement via JDBC
• XML–executes XML defined jobs
• OS–executes an operating system command
• XML job definition language–allows ETL jobs to be easily defined in XML, enabling the use of widely available XML authoring tools and associated support for version control systems
• Centralized repository–supports multiple KETL instances to leverage job and parameter definitions
• Performance monitoring–collects historical and active job statistics in the repository, allowing comprehensive analysis of problematic jobs
• Comprehensive data source support–supports extracting and loading of relational, flat file and XML data sources, via JDBC and proprietary database APIs
• Scheduling engine–time-based and event-driven job execution
• Support for integration of security and data management tools
• Proven scalability across multiple servers and CPU's and any volume of data
• No additional need for third party schedule, dependency, and notification tools

Last Updated Friday, November 27 2009 @ 07:50 PM EST