I am interested in importing a Fixed Width file using Pentaho PDI.
I have used its main GUI tool that sets the widths graphically in Spoon.
BUT if the number of fields is very large, like a few hundred fields, it would be prone to error and take a lot of time.
In other ETL tools, I am able to import a meta-file that describes the column properties, such as name, size etc.
I see that pentaho has this thing calls Meta-Data Injection, but there is not much tutorials at all, just a couple, and either the use cases are really complex and make use of Javascript for scripting or they describe it in very abstract ways.
So hope someone who is familiar can explain my particular use case of Fixed Width files.
Yes, You can use metadata injection step for applying dynamic properties like filename, fields, length datatype etc..
For that,
You need to create one transformation with file input step.
Create another transformation with the metadata injection step and where
you can add a transformation that created in step1.
In inject Metadata tab of metadata injection step you can add length from the input step.
Related
I have some doubts. I'm doing a BI for my company, and I needed to develop a data converter in ETL because the database to which it's connected (PostgreSQL) is bringing me some negative values within the time CSV. It doesn't make much sense to bring from the database (to which we don't have many accesses) negative data like this:
The solution I found so that I don't need to rely exclusively on dealing directly with the database would be to perform a conversion within the cloudconnect. I verified in my researches that the one that most contemplates would be the normalizer, but there are not many explanations available. Could you give me a hand? because I couldn't parameterize how I could convert this data from 00:00:-50 to 00:00:50 with the normalizer.
It might help you to review our CC documentation: https://help.gooddata.com/cloudconnect/manual/normalizer.html
However, I am not sure if normalizer would be able to process timestamps.
Normalizer is basically a generic transform component with a normalization template. You might as well use reformat component that is more universal.
However, what you are trying to do would require some very custom transform script, written in CTL (CloudConnect transformation language) or java.
You can find some templates and examples in the documentation: https://help.gooddata.com/cloudconnect/manual/ctl-templates-for-transformers.html
Want to migrate bulk files (e.g VSAM) from Mainframe to Azure in the beginning of the Project, how that can be achieved ?
Any utility or do we need to write own scripts?
I suspect there are some utilities out there but I suspect they are most / all priced products. Since VSAM datasets are not defined using a language construct like DDL you will likely have to do most of the heavy lifting. Either writing your own programs or custom scripts. You didn’t mention operating system but I assume you’re working on z/OS.
Here are some things to consider:
The structure of the VSAM dataset is basically record oriented. There are three basic types you’ll run into that host application data:
Key Sequenced Datasets (KSDS)
Entry Sequenced Datasets (ESDS)
Relative Record datasets (RRDS)
Familiarize yourself with the means of defining the datasets as it will give you some insight into the dataset specifics. DFSMS Access Method Services Commands will show the utilities used to create them and get information like Keylength and offest of the key. DEFINE CLUSTER is the command to create the dataset. You mentioned you are moving the data toi Azure but this will help you understand the characteristics of the data you are moving.
Since there is no DDL for VSAM datasets you will generally find the structure in the programs that manipulate them like COBOL Copybooks, HLASM DSECTs and similar constructs. This is the long pole in the tent for you.
Consider what are the semantics of accessing the data. VSAM as an access method does have some ability to control read/write access on a macro level using a DEFINE CLUSTER option called SHAREOPTIONS. The SHAREOPTIONS instruct the operating system how to handle the VSAM buffers in terms of reading and writing so that multiple processes can access the same data. Its primitive if compared to sahred files systems like NFS. VSAM allows the application to control access (or serialization) using ENQ / DEQ functions. These enable applications to express intent in the cluster about a VSAM file and coordinate their own activities.
You might find that converting a VSAM file to a relational form like Db2 is better for you. Again, you’ll have to create the DDL to describe the tables, data formats and the like.
Another consideration is data conversion. You’ll find there is character data that is most likely in EBCDIC and needs to be converted to a new code page. Numeric data can be in Packed Decimal, Binary, or even text and will need to be converted.
The short answer is there isn’t an “Easy Button” to do what you want. Consider the data is only one of the questions that needs to be answered. Serialization and access to the data, codepage conversion, if you are moving some data but not others will you need to be able to map some of the converted data back to data on the mainframe.
Consider exploring IBM CDC classic replication. You can achieve it with click of buttons.
I have not done for Azure. So not sure about support.
I have to investigate taking a domino database to an alternative database - probably SQL Server or Oracle. How do I investigate and output a complete easily readable report on the domino database's meta-data, including all data field types and imbedded objects, such as other files, imbedded text and images etc?
I have looked at creating the database synopsis but I need something that doesn't contain all the unnecessary information etc.
You can write your own tool using the NotesNoteCollection class and the NotesDXLExporter class, parsing out whatever parts you consider necessary and leaving out the parts you think are unnecessary.
If my source table keeps getting one column added to it at a time, how do I map the new column to my query/source?
It is different from slowly changing dimension, as it is not records that are changing, but the number of columns itself, i.e. the schema.
How do I design a job to do this? Any solution is fine, even if it requires custom functions, scripts, etc.
From my perspective it is not possible. This is a use case of sql injection (i.e. somehow you have to play with the ATL or repository metadata) which SAP I bet would never suggest. I think Pentaho and Talend Integration does indeed support this functionality.
According to me by using template tables it can be possible.
Thanks.
With the load data option that Liquibase provides, one can specify seed data in a CSV format. Is there a way I can provide say, a JSON or XML file with data that Liquibase would understand?
The use case is we are trying to put in some sample data which is hierarchical. E.g. Category - Subcategory relation which would require putting in parent id for all related categories. If there is a way to avoid including the ids in the seed data via say, JSON.
{
"MainCat1": ["SubCat11", "SubCat12"],
"MainCat2": ["SubCat21", "SubCat22"]
}
Very likely to have this as not supported (couldn't make Google help me) but is there a way to write a plugin or something that does this? Pointer to a guide (if any) would help.
NOTE: This is not about specifying the change log in that format.
This not currently supported and supporting it robustly would be pretty difficult. The main difficultly lies in the fact that Liquibase is designed to be database-platform agnostic, combined with the design goal of being able to generate the SQL required to do an operation without actually doing the operation live.
Inserting data like you want without knowing the keys and just generating SQL that could be run later is going to be very difficult, perhaps even impossible. I would suggest approaching Nathan, who is the main developer for Liquibase, more directly. The best way to do that might be through the JIRA bug database for Liquibase.
If you want to have a crack at implementing it, you could start by looking at the code for the LoadDataChange class (source in Github), which is where the CSV support currently lives.