Dimension lookup/update

Description

The Dimension Lookup/Update transform allows you to implement Ralph Kimball’s slowly changing dimension for both types: Type I (update) and Type II (insert) together with some additional functions. Not only can you use this transform to update a dimension table, it may also be used to look up values in a dimension.

Options

As a result of the lookup or update operation of this transform type, a field is added to the stream containing the technical key of the dimension. In case the field is not found, the value of the dimension entry for not found (0 or 1, based on the type of database) is returned.

A number of optional fields (in the “Fields” tab) are automatically managed by the transform. You can specify the table field name in the “Dimension Field” column. These are the optional fields:

• Date of last insert or update (without stream field as source) : adds and manges a Date field

• Date of last insert (without stream field as source) : adds and manges a Date field

• Date of last update (without stream field as source) : adds and manges a Date field

• Last version (without stream field as source) : adds and manges a Boolean field. (converted into Char(1) or boolean database data type depending on your database connection settings and availability of such data type)

• This acts as a current valid dimension entry entry indicator for the last version: So when a type II attribute changes and a new version is created (to keep track of the history) the ‘Last version’ attribute in the previous version is set to ‘False/N’ and the new record with the latest version is set to ‘True/Y’.

Functionality

As the name of the transform suggests, the functionality of the transform falls into 2 categories, Lookup and Update…​

Lookup

In read-only mode (update option is disabled), the transform only performs lookups in a slowly changing dimension. The transform will perform a lookup in the dimension table on the specified database connection and in the specified schema. To do the lookup it uses not only the specified natural keys (with an “equals” condition) but also the specified “Stream datefield” (see below). The condition that is applied is:

“Start of table date range” ⇐ “Stream datefield” AND “End of table date range” > “Stream datefield”

When no “Stream datefield” is specified we use the current system date to find the correct dimension version record.

When no row is found, the “unknown” key is returned. (The “unknown” key will be 0 or 1 depending on whether or not you selected an auto-increment field for the technical key field). Please note that we don’t make a difference between “Unknown”, “Not found”, “Empty”, “Illegal format”, etc. These nuances can be added manually however. Nothing prevents you from flushing out these types before the data hits this transform with a Filter, regular expression, etc. We suggest you manually add values -1, -2, -3, etc for these special dimension entry cases, just like you would add the specific details of the “Unknown” row prior to population of the dimension table.

• Do not use NULL values for your natural key(s). Null values can’t be compared and are not indexed by most databases. Even if we would support null values in keys (something that doesn’t make a lot of sense anyway), it would most likely cause severe lookup performance problems.

• Be aware of data conversion issues that occur if you have data types in your input streams that are different from the data types in your natural key(s). If you are have Strings in the transforms input and in the database you use an Integer for example, make sure you are capable of converting the String to number. See it as a best practice to do this before this transform to make sure it works as planned. Another typical example of problems is with floating point number comparisons. Stay away from those. We recommend you use sane data types like Integer or long integers. Stay away from Double, Decimal or catch-all data types like Oracle’s Number (without length or precision; it implicitly uses precision 38 causing us to use the slower BigNumber data type).

Update

In update mode (update option is enabled) the transform first performs a lookup of the dimension entry as described in the “Lookup” section above. The result of the lookup is different though. Not only the technical key is retrieved from the query, but also the dimension attribute fields. A field-by-field comparison then follows. The result can be one of the following situations:

• The record was not found, we insert a new record in the table.

• The record was found and any of the following is true:

  • One or more attributes were different and had an “Insert” (Kimball Type II) setting: A new dimension record version is inserted.

  • One or more attributes were different and had a “Punch through” (Kimbal Type I) setting: These attributes in all the dimension record versions are updated.

  • One or more attributes were different and had an “Update” setting: These attributes in the last dimension record version are updated.

  • All the attributes (fields) were identical: No updates or insertions are performed.

• Insertion of new rows are performed in the following transforms:

  • The current row is updated with “date_to” updated with the “Stream date field”

  • The new row is inserted where the changes in attributes are recorded according to rule in previous paragraph. “date_from” field is updated with the “Stream date field” and the “date_to” is updated with the Max date of the table range end date.

  • The version number of the new row in incremented by 1.

  • Stream date field” cannot be before the earliest start date of the currently valid rows.

  • select min(date_from) from dim_table where date_to = “2199-12-31 23:59:59.999”

  • It is important to ensure that the incoming rows are sorted by the “Stream date field”

Options

Metadata Injection Support

All fields of this transform support metadata injection. You can use this transform with ETL Metadata Injection to pass metadata to your pipeline at runtime.