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Hitachi VantaraPentaho Documentation
Version 6.1
Pentaho Documentation

ETL Metadata Injection

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Overview

The ETL Metadata Injection step is capable of injecting step metadata into a template transformation.

The ETL Metadata Injection step inserts metadata into a template transformation. Instead of statically entering ETL metadata in a step dialog, you pass it at run-time. This step enables you to solve repetitive ETL workloads like loading of text files, data migration, and so on.

The following steps support metadata injection:

Step Version Introduced Fields Supporting Metadata Injection
Concat Fields 5.1 All fields
CSV File Input 4.1 See CSV File Input for a list of supported fields
Data Grid 5.1 All fields
Fixed File Input 4.1 See Fixed File Input for a list of supported fields
Get Data from XML 5.0 See Get Data from XML for a list of supported fields
Google Analytics 6.1 All fields
Group By 5.0 All fields
GZIP CSV Input 5.1 All fields
JSON Output 5.2 All fields
Microsoft Access Input 5.0 See Microsoft Access Input for a list of supported fields
Microsoft Excel Input 4.1 See Microsoft Excel Input for a list of supported fields
Microsoft Excel Output 5.1 All fields (as of 6.1)
Microsoft Excel Writer 5.3 See Microsoft Excel Writer for a list of supported fields
Pentaho Reporting Output 5.0 All fields
PostgreSQL Bulk Loader 5.1 All fields
Row Denormaliser 4.2 See Row Denormaliser for a list of supported fields
Row Normaliser 4.2 See Row Normaliser for a list of supported fields
S3 CSV Input 6.1 All fields
S3 File Output 6.1 All fields
Select Values 4.1 All fields
Sort Rows 5.0 All fields
Split Field 5.0 All fields (as of 6.1)
Stream Lookup 6.1 All fields
Table Input 5.2 See Table Input for a list of supported fields
Table Output 5.1 See Table Output for a list of supported fields
Text File Input 5.0 All fields
Text File Output 5.2 All fields
User Defined Java Expression 5.2 All fields
Value Mapper 6.1 All fields
XML Output 6.1 All fields

Options

Option Description
Transformation template In this section of the dialog, you can specify the transformation to use as a template.  When you have specified a transformation, you can use the Validate and Refresh button.  The Edit button will open the specified template in a new tab in Spoon.
Template step to read from (optional) If you specify a step from the template here, then the output of the ETL Metadata Injection step will be the output from the source step.
Optional target file (KTR after injection) For debugging or transformation generation, you can save the resulting transformation filename, after metadata injection, to a file.  If you want, you can specify a file name, result.ktr for example.
Don't execute resulting transformation If you prefer to not execute the resulting transformation (after metadata injection), enable this option.
Field mapping You can select any row in the metadata tree table with your mouse, which pops up a source step and field selection dialog.

Data Streaming

Since version 5.1, this step is capable of streaming data from one transformation into another. 

To pass data from your template transformation (after injection, during execution) to your current transformation, specify Template step to read from.  You can also specify the expected output fields easily design the steps which come after the ETL Metadata Injection step.

To pass data from a source step into the template transformation (again, after injection) you can specify Streaming source step and Streaming target step in the template transformation.

Example

Metadata injection inserts data from various sources into your transformation at runtime. This insertion reduces repetitive ETL tasks for various input and out files.

For example, you might have a simple transformation to load transaction data values from a supplier’s spreadsheet, filter out specific values to examine, and output them to a text file. You can expand this simple repetitive “template” transformation with metadata injection to load data values from multiple suppliers’ spreadsheets in various folders, filter out common specific transaction values to examine, and output all of it to a single source text file.

In this example, we show you how to use metadata injection through these steps:

This example assumes a basic understanding of working with transformations and steps.

Step 1: Create Sample Data

Data files are frequently uploaded from various sources. For this step, we model a situation where two suppliers have uploaded spreadsheets into a data/in folder.

When using metadata injection, you usually want to focus on a subset of data values common to all your input files. For this example, we derive metadata for the following values:

  • Transaction date
  • Transaction invoice number
  • Net value of the transaction
  • Currency used in the transaction

The metadata for these values and the output target text file are created and maintained in the metadata folder.

  1. Create a new folder named Pentaho_Metadata_Injection_Example on your computer, and then create the following folder structure inside it.

  1. Create a spreadsheet for supplier #1 in Microsoft Excel, and save it as data_format_a.xlsx in the data/in/supplier1 folder.
Tx Date and Time Check Inv Price Net VAT Gross VATP Cur
01/26/2016 07:24:00 12345 9406 1.13 62.69 11.91 74.6 19 EUR
01/26/2016 07:31:00 12346 9407 1.13 54.63 10.38 65.01 19 EUR
  1. Create a spreadsheet for supplier #2 in Microsoft Excel, and save it as data_format_b.xlsx in the data/in/supplier2 folder.
TransferDataTime Check Inv Cur VAT Gross VATP Net
01/26/2016 07:23:00 800901 3711 EUR 8.95 56.06 19 47.11
01/26/2016 07:30:00 800927 3712 EUR 0.94 5.89 19 4.95
01/26/2016 07:32:00 800990 3713 EUR 15.38 96.36 19 80.98
  1. Create two tabs in a spreadsheet of metadata fields for suppliers, and save it as metadata_suppliers.xlsx in the metadata folder.
  • The  supplier1 tab.
Name Type Length Precision Trim type Repeat Format Currency Decimal Grouping Target field
Tx Date and Time Date -1 -1 none N yyyy/MM/dd HH:mm:ss       transaction_date
Check Integer -1 -1 none N          
Inv Integer -1 -1 none N         transaction_invoice
Price BigNumber 8 2 none N     , .  
Net BigNumber 8 2 none N     , . transaction_net
VAT BigNumber 8 2 none N     , .  
Gross BigNumber 8 2 none N     , .  
VATP BigNumber 2 1 none N     , .  
Cur String 3 -1 none N         transaction_cur
  • The  supplier2 tab.
Name Type Length Precision Trim type Repeat Format Currency Decimal Grouping Target field
TransferDateTime Date     none N         transaction_date
Check Number     none N          
Inv Number     none N         transaction_invoice
Cur String     none N         transaction_cur
VAT Number     none N          
Gross Number     none N          
VATP Number     none N          
Net Number     none N         transaction_net
  1. Create a spreadsheet of metadata fields for targets, and save it as metadata_target.xlsx in the metadata folder.
Name Type Format Length Precision Currency Decimal Group Trim type Null
supplier String             both  
source_filename String             none  
source_row Integer #           none  
transaction_date Date yyyy-MM-dd           none  
transaction_invoice BigNumber # -1 -1       none  
transaction_net BigNumber #.## -1 -1       none  
transaction_cur String   3         both  

 

Step 2: Develop Your Transactions

You need to develop a transformation for the main repetitive process, which is often known as the template transform. For this example, you need a transformation (process_supplier_file) to process the transactions in each supplier’s file. Then, the metadata needs to be injected from a transformation (inject_supplier_metadata) developed with the ETL Metadata Injection step. The ETL Metadata Injection step calls the template transformation. Since this example is for inserting data from multiple files, the metadata injection transformation needs to be called from another transformation (process_all_suppliers) per each supplier file.

All total, three transformations need to be developed:

  • Template Transformation – The main repetitive transformation for processing the data per each supplier’s spreadsheet.
  • Metadata Injection Transformation – The transformation defining the structure of the metadata and how it is injected into the main transformation.
  • Transformation for All Suppliers – The transformation going through all the suppliers’ spreadsheets, calling the metadata injection transformation per each supplier, and logging the entire process (for possible troubleshooting, if needed).

Template Transformation

With metadata injection, you develop your repetitive, template transformation as you would normally. The main difference is how the settings for each step pertains to the metadata injection, instead of data values of a single specific source.

  1. Open a new transformation and save it as process_supplier_file.ktr to the transformations folder.
  2. Drag a Microsoft Excel Input step onto the canvas.
  3. Change Spread sheet type (engine) to Excel 2007 XLSX (Apache POI), and fill out the following values in the Additional output fields tab.
Field Value
Full filename field processed_filename
Sheet row nr field source_row
Short filename field source_filename
  1. Drag a Select values step onto the canvas and connect a hop from Microsoft Excel Input to Select values.
  2. Drag a Get Variables step onto the canvas and connect a hop from Select values to Get Variables, while selecting Main output of step. Also fill out the following values in the Fields table.
Field Value
Name supplier
Variable ${supplier}
Type String
Trim type none
  1. Drag a Text file output step onto the canvas and connect a hop from Get Variables to Text file output. Also fill out the following values in the File and Content tabs.
Field Value
Filename (in File tab) ${Internal.Entry.Current.Directory}/../data/out/processed_data
Include date in filename? (in File tab) enable
Add filenames to result (in File tab) disable
Append (in Content tab) enable
  1. Save your process_supplier_file.ktr file.

Metadata Injection Transformation

For this example, our metadata values are maintained in separate spreadsheet files. You need to create a transformation to extract in these values, prepare them for the injection, and then insert them into the template transformation through the ETL Metadata Injection step, as shown in the following figure:

Extract the Metadata

For this example, you need to define the input files, access the metadata, and structure the output based on this metadata.

  1. Open a new transformation and save it as inject_supplier_metadata.ktr to the transformations folder.
  2. Drag a Get row from result step to the canvas, and fill out the following values in the Fields table.
# Fieldname Type Length
1 supplier String 500
2 folderName String 500
  1. Drag a Microsoft Excel Input step onto the canvas, name the step Metadata Suppliers.
  2. Browse to Add the ${Internal.Entry.Current.Directory}/../metadata/metadata_suppliers.xlsx file in the Files tab. After adding the file, you can verify its path by clicking Show filenames...
  3. Change Spread sheet type (engine) to Excel 2007 XLSX (Apache POI), and fill out the following values in the !Fields tab table.
# Name Type Length Precision Trim type Repeat
1 Name String -1 -1 none N
2 Type String -1 -1 none N
3 Length Integer -1 -1 none N
4 Precision Integer -1 -1 none N
5 Trim type String -1 -1 none N
6 Repeat String -1 -1 none N
7 Format String -1 -1 none N
8 Currency String -1 -1 none N
9 Decimal String -1 -1 none N
10 Grouping String -1 -1 none N
11 Target field String -1 -1 none N
  1. In the Additional output fields tab, set Sheetname field to metadata_supplier.
  2. Drag another Microsoft Excel Input step onto the canvas, name the step Metadata Target.
  3. Add the ${Internal.Entry.Current.Directory}/../metadata/metadata_target.xlsx file in the Files tab. After adding the file, you can verify its path by clicking Show filenames...
  4. Change Spread sheet type (engine) to Excel 2007 XLSX (Apache POI), and fill out the following values in the !Fields tab table.
# Name Type Length Precision Trim type Repeat
1 Name String -1 -1 none N
2 Type String -1 -1 none N
3 Format String -1 -1 none N
4 Length Integer -1 -1 none N
5 Precision Integer -1 -1 none N
6 Currency String -1 -1 none N
7 Decimal String -1 -1 none N
8 Group String -1 -1 none N
9 Trim type String -1 -1 none N
10 Null String -1 -1 none N
  1. In the Additional output fields tab, set Sheetname field to metadata_supplier.

Prepare the Metadata

With the supplier files specified and the metadata accessed, you need to prepare this information to be injected into the main transformation.

  1. Drag a Join Rows step to the canvas, and connect a hop from the Get rows from result step and Join Rows. Also connect a hop from the Metadata Suppliers step to Join Rows.
  2. Set The condition in Join Rows to metadata_supplier = supplier.
  3. Drag an Add constants step to the canvas, and connect a hop from Get rows from result to Add constants. When the Warning dialog appears, click Copy.
  4. Fill out the Fields table of Add constants with the following values.
# Name Type Value Set empty string?
1 wildcard_include String .* N
2 wildcard_exclude String   N
  1. Drag a Get file names step to the canvas, and connect a hop from Add constants to Get file names. Also fill out the following values in the File tab.
Field Value
Filename is defined in a field? enable
Get filename from field folderName
Get wildcard from field (RegExp) wildcard_include
Exclude wildcard field wildcard_exclude

Inject the Metadata

With the metadata prepared, you need to associate it with the main transformation for it to be inserted at runtime.

  1. Drag the ETL Metadata Injection step to the canvas, and connect three hops:
    • From Join Rows to ETL Metadata Injection
    • From Get File Names to ETL Metadata Injection
    • From Metadata Target to ETL Metadata Injection
  2. In the File tab of the step properties, set ${Internal.Entry.Current.Directory}/process_supplier_file.ktr to Use a file for the transformation template.
  3. Click Validate and Refresh.
  4. In the Inject Metadata tab, set the Source step and Source field for the following fields.
Target injection step, key Source step Source field
Microsoft Excel Input > FIELDS > NAME Join Rows Name  
Microsoft Excel Input > FIELDS > LENGTH Join Rows Length
Microsoft Excel Input > FIELDS > PRECISION Join Rows Precision
Microsoft Excel Input > FIELDS > FORMAT Join Rows Format
Microsoft Excel Input > FIELDS > CURRENCY Join Rows Currency
Microsoft Excel Input > FIELDS > DECIMAL Join Rows Decimal
Microsoft Excel Input > FIELDS > GROUP Join Rows Grouping
Microsoft Excel Input > FIELDS > REPEAT Join Rows Repeat
Microsoft Excel Input > FIELDS > TYPE Join Rows Type
Microsoft Excel Input > FILENAME_LINES > FILENAME Get File Names filename
Select values > METAS > META_NAME Join Rows Name
Select values > METAS > RENAME Join Rows Target field  
Text file output > OUTPUT_FIELDS > OUTPUT_FIELDNAME Metadata Target Name  
Text file output > OUTPUT_FIELDS > OUTPUT_FORMAT Metadata Target Format
Text file output > OUTPUT_FIELDS > OUTPUT_LENGTH Metadata Target Length
Text file output > OUTPUT_FIELDS > OUTPUT_PRECISION Metadata Target Precision
Text file output > OUTPUT_FIELDS > OUTPUT_CURRENCY Metadata Target Currency
Text file output > OUTPUT_FIELDS > OUTPUT_DECIMAL Metadata Target Decimal
Text file output > OUTPUT_FIELDS > OUTPUT_GROUP Metadata Target Group
Text file output > OUTPUT_FIELDS > OUTPUT_NULL Metadata Target Null
Text file output > OUTPUT_FIELDS > OUTPUT_TYPE Metadata Target Type
Text file output > OUTPUT_FIELDS > OUTPUT_TRIM Metadata Target Trim type
  1. Save your inject_supplier_metadata.ktr file.

Transformation for All Suppliers

Since we have multiple input sources, we need a transformation to run through each source and inject the metadata. Each input source is specified through a variable in a Transformation Executor step, which calls to the metadata injection transformation.

  1. Open a new transformation and save it as process_all_suppliers.ktr to the transformations folder.
  2. Drag a Get Subfolder names step to the canvas, and add the ${Internal.Entry.Current.Directory}/../data/in folder in the Folder tab.
  3. Drag a Select values step to the canvas, and connect a hop from Get Subfolder names to Select values.
  4. Fill out the following values in the Fields table of the Select & Alter tab.
# Fieldname Rename to
1 folderName  
2 short_folderName supplier
  1. Drag a Text file output step to the canvas.
  2. Fill out the following values in the File tab.
Field Value
Filename ${Internal.Entry.Current.Directory}/../logging/log
Include date in filename? enable
Include time in filename? enable
  1. Drag a Transformation Executor step to the canvas, connect a hop from Select values to Transformation Executor, then select Main output of step in the context menu.
  2. Also, connect a hop from Transformation Executor to Text file output, then select This output will contain the execution results in the context menu.
  3. Fill out the following values in the Parameters and Execution results tabs.
Field Value
File name ${Internal.Entry.Current.Directory}/inject_supplier_metadata.ktr
Variable / Parameter name (in Parameters tab) supplier
Field to use (in Parameters tab) supplier
The target step for the execution results (in the Execution Results tab) Text file output
  1. Save your process_all_suppliers.ktr file.

Step 3: Run and Examine Results

You run the entire process for all the supplier file by running the process_all_suppliers transformation, which runs the inject_supplier_metadata transformation for each supplier input file. The inject_supplier_metadata transformation then runs the template process_supplier_file transformation.

These transformations create a single source text output file in the data/out folder. The logs generated by the process_all_suppliers transformation are in the logging folder.

  1. Run the process_all_suppliers transformation.
  2. Examine the processed_data_{today’s date}.txt file in the data/out folder and the log_{timestamp}.txt file in the logging folder.