We proudly present the production release CloverETL 3.3.0. It packs all its milestone features and fixes: jobflows for workflow management, the integrated Data Profiler, advanced support for Web Services, support for complex data structures, and lots of usability improvements – including unrivaled Excel spreadsheet capabilities.
This release combines all its milestone releases (3.3.0-M1, -M2 and -M3) into a full production release, making it a replacement for any previous milestone. Thus, it’s an ideal candidate for an upgrade from older production releases too.
CloverETL Jobflow is a new module that adds workflow management capabilities to CloverETL. You can now build and control complex processes involving data transformations and their surrounding environment - like data exchange, cleanup, error handling and reporting or monitoring.
Using a visual design approach, you can now build and control the whole data integration process involving the data transformations and their surrounding processes - e.g. data exchange, cleanup, error handling and reporting or monitoring
Jobflows module introduces a new jobflow graph (.jbf file) which, just like a transformation graph, consists of components and edges. Components perform workflow actions (steps) and communicate with each other by exchanging tokens over edges. Token is a form of a message that can contain user data - either parameters for an action or results (e.g. source and target paths for file operations, graph name to execute, etc.).
It has been a while since we introduced a complementary tool for data quality - CloverETL Data Profiler. Now we are taking the data quality toolset further by integrating everything together. With the integrated offering, you can embed data quality checks into your ETL transformations, or when combined with the jobflows functionality, you can create brand new use cases where Data Profiler will be part of a robust process ensuring data quality.
CloverETL brings an improved toolset for working with Web Services. Instead of having dedicated connectors vulnerable to interface and format changes, we take the Unix-style approach - giving you a powerful generic tool that you can use for any number of Web Services and operations.
Check out our Salesforce.com demo example in our Desktop Trial
CloverETL also supports working with data formats used in Web Services, mostly JSON and XML. New JSONReader/Writer components allow working either directly with JSON formatted files or JSON data coming in and out of WS components.
CloverETL loves Excel. We know it is a number one choice for most business users throught its power and simplicity. With the newly redesigned Excel capability in CloverETL you can read and write both simple and complex data with ease. To help you with that we have introduced a visual mapping editor which joins the worlds of CloverETL and Excel.
This is just one step that CloverETL is taking to enhance its support for both rich data structures and unstructured data. A field in metadata can now be declared as a list of values or as a map – a pair of a key and a value. This enhancement eases the previously rigid metadata model, which required each processed data record to be exactly described to the last item. With this new version, data coming from systems where the structure of record varies from record to record (as are CRMs, social networking systems) can now be easily processed in CloverETL by utilizing the variable length lists or even more flexible maps.
Support for lists and maps now spans across the whole platform: from the metadata editor, through CTL, to passing lists and maps over to a dictionary. Lists and maps now may become handy in processing XML, to name one use case.