I'm not finding a lot of information on the internet about what is and what does WTX.
Can you give me some lights about it, and give me an example?
Also, the EAI BizTalk Server from microsoft is related to wtx?
thanks
WTX is a data transformation tool, for exmaple it will transform CSV into XML, one flavour of XML (e.g. RosettaNet) into another (e.g. an application format) etc.
It is owned and developed by IBM.
Transformations are referred to as maps and are built by dragging and dropping fields in the Eclipse based design tool.
The WTX runtime engine can be called in a variety of ways - e.g. through the Java API, the TX Launcher, IBM Integration Bus, etc.
WTX has been previously known as Mercator and DataStage TX
EAI BizTalk Server is also a tool for integrating applications and transforming data. I'm not aware of any adapters between the two tools, but you could develop one using the WTX API.
WTX is the one of the powerful data transformation tool present in the integration industry .
Generally XML data transformation is done in may ways like XSLT ect . but when it comes to EDI , stander Messages like EDFACT ASCII transformation logic will be very difficult in other tools .
WTX can easily handle these type of transformation and its very flexible tool for transformation for any type of message.
EX: Lets consider two systems communicating and system A send data in IDOC , systmem B wants in XML . You can use a WTX map in between and achieve this . Also you can handle business rules in WTX.
Related
A general question. I have used i.e. Weka classifier model functionallity in their tool. But is there a way to "call Weka" and get a model in response from a website?
It is not important that it is Weka, but I want to implement some simple classification based on a json coming from a web-site.
Thanks.
You can write a REST webservice in Java which loads your model and makes predictions using data it receives, sending back the predictions in a suitable format. There are a number of frameworks for writing such webservices (e.g., JAX-RS).
In terms of using the Weka API, check out the Use Weka in your Java code article.
We are now in process of evaluating integration solutions and comparing Mule and Boomi.
Use case is to read an Excel file, map the columns to a pre-defined set of JSON attributes and then use the JSON to insert records into a database. The mapping may vary from one Excel template to another wherein the column names in an Excel may be different from others.
How do I inject mapping information (source vs target) from outside integration flow?
Note: In Mule, I'm able to do that using a mapping variable (value is JSON) that I inject using Mule DataWeave language.
Boomi's mapping component is static in terms of structure but more versatile solutions are certainly possible.
The data processor component opens up Groovy, JavaScript, and XSLT 3.0 as options. These are Turing-complete languages that can be used to bend Boomi to almost any outcome.
You could make the Boomi UI available to those who need to write the maps in JSON. It's a pretty simple interface to learn. By using a route component, there could be one "parent" process that governs the a process for each template/process and then a map for each template. Such a solution would be pretty easy to build and run; allowing the template-specific processes to be deployed independently of the "parent".
You could map to a generic columnar structure and then dynamically alter the target
columns by writing a SQL procedure that would alter the target columns.
I've come across attempts to do what you're describing (not using either Boomi or Mulesoft) which were tragic failures: https://www.zdnet.com/article/uk-rural-payments-agency-rpa-it-failure-and-gross-incompetence-screws-farmers/ I draw your attention to the NAO's points:
ensure the system specifications retain a realistic level of flexibility
and
bespoke software is costly to develop, needs to be thoroughly tested, and takes more time to implement
The general goal for such a requirement like yours is usually to make transformation/ETL available to "non-programmers" which denies the reality that there are many more skills to delivering an outcome than "programming".
I am trying to move my informatica pipelines in PC 10.1 to Azure Data Factory/ Synapse pipelines. Other than rewriting them from scratch, is there a way to migrate them somehow.. I am not finding any tools to achieve this as well. Has anyone faced this problem. Any leads on how to proceed ahead.
Thanks
There are no out of box solutions available to complete this migration. Unfortunately, you will have to author them again.
Informatica PowerCenter pipelines are a physical implementation of an Extract Transform Load (ETL) process. Each provider has different approaches to the implementations and they do not necessarily map well from one to another. Core Azure Data Factory (ADF) is actually more suited to Extract, Load and Transform (ELT), unless of course you use Data Flows.
So what you have to do is:
map out physically what your current pipeline is doing, if you don't have that documentation already. A simple spreadsheet template mapping out the components of the existing pipeline, tracking source, target plus any transformations will suffice
logically map out what the pipeline is doing; ie without using PowerCenter- specific terminology lay out what the "as is" pipeline is doing. A data flow diagram is a great way to do this
logically map out what the "to be" pipeline should do; ie without using any ADF-specific terminology, attempt to refine the "as is" pipeline to its simplest form
using expert knowledge of the ADF components (eg Copy, Lookup, Notebook, Stored Proc to name but a few) map from the logical "to be" to the physical (in the loosest sense of the word, it's all cloud now right : ), eg move data from place to place with the Copy activity, transform data in a SQL database using the Stored Proc activity, a repeated activity might use a For Each loop (bear in mind these execute in parallel), do sophisticated transformations or processing using Databricks notebooks if required and so on. If you require a low-code approach, consider Data Flows.
So you can see it's just a few simple steps. Good luck!
We are going to be moving some data around that will have some standard fields as well as some key value pairs which will vary between data items. Obviously we could code something in JSON or XML to do this and write our own marshalling/unmarshalling code however I was hoping for a standards based solution that has some or all of the following:
Marshalling/unmarshalling for SharePoint lists/.Net
Marshalling/unmarshalling for Java
Service definitions and semantics for operating on the data across an integration boundary
Security semantics
We are currently looking at the OData protocol to perform this task: http://www.odata.org/
Presumably you've made your decision long ago now, but for anyone else who ends up here, and is perhaps interested in something at a lower level than OData, here's what I'm using for C# to Java data interchange:
Google Protocol buffers as the interchange format:
https://developers.google.com/protocol-buffers/
Marc Gravell's protobuf-net at the C# end:
http://code.google.com/p/protobuf-net/
A program called called protostuff at the Java end:
http://code.google.com/p/protostuff/
(I prefer protostuff to the official Google Java implementation of protocol buffers due to Google's implementation being based on the Java objects being immutable.)
Actually, I'm not using pure protocol buffers as the interchange format - I prefix the data with the name of the (outermost) class being transmitted. This makes the data self-identifying for deserializing at the other end.
Their documentations are simple and professional.
But they don't mention too much about the relationship between these open source projects.
When should I use which one? And which one is suitable for what scenario?
If you are a GIS developer who is familiar with these projects, can you explain?
The fundamental common denominator of all of the three software packages is that they all are data access abstractions. In particular, they provide access to geospatial data. In general, they all follow similar convention:
- define collection of types and objects
- define low-level data sources implemented in form of a set of drivers (as named in GDAL/OGR) or providers (as named in FDO)
FDO, GDAL and OGR are all implemented in C++ programming language.
Along with similarities, there are many differences. GDAL/OGR gives access to data stored in enormous number of geospatial formats, lots of data processing algorithms and operators. FDO provides those features too (interestingly, thanks to integration with GDAL/OGR in some places like FDO Provider for GDAL) but it feels more like a framework, whereas GDAL/OGR feels more like a library.
Anyhow, it is not possible to provide you with definitive answer which one fits where better.
You may find Matthew Perry's blog and following discussion helpful: FDO, GDAL/OGR and FME?
Note, GDAL and OGR are bundled together under the umbrella of common software project called simply GDAL. Both names seem to be acronyms and are explained in GDAL FAQ, check the following Q&A:
What is GDAL?
What does GDAL stands for?
What is this OGR stuff?
What does OGR stands for?
In basic terms, GDAL is used for reading, writing and transforming raster data, while OGR can do the same with vector data. I am not as familiar with FDO, but it appears to be an API used to access (from database sources), manipulate and analyze all kinds of geospatial data, and relies on GDAL and OGR for those purposes.