Data Dictionary

Transaction

/DVD/GL11

Glue Data Dictionary(DDIC) module represents the central component of Glue. It provides the structural information necessary for data to be understood(structured). DDIC specifies transparent data and enables manipulation with it. Whether you will read, insert, transform or virtualize data, all these processes always heavily rely on GLUE DDIC module.

Metaobject

Glue table

The module itself offers data modeling and maintenance of Glue tables similar to standard SAP SE11 transaction thus the transaction /DVD/GL11. It allows you to create/edit/display/delete the main Glue metaobject - the Glue table. The Glue tables are pushed to a predefined Storage and can be created in a transparent way, as well.

Tutorials

(GLUE-1806) Create Glue table

(GLUE-1806) Create from external(link Glue table)

(GLUE-1806) Display Glue table

(GLUE-1806) Change Glue table

(GLUE-1806) Deactivate Glue table

(GLUE-1806) Delete Glue table

(GLUE-1806) Where used

Requirements

None

Glue DDIC provides a UI that enables us to create a Glue table and its metadata which represent data structure and data destination of the table.

Glue table

Is a Glue metadata object, holding information about data destination, data structure, and other settings.
Destination is helping us to locate the data in given storages e.g. all kinds of databases. Storage ID specifies more precisely the location of the data. For more information about how to set up a Storage ID see the (GLUE-1806) Storage Management guide.
Structure is defined by the user directly. The creation process is transparent, describing each structural component one by one directly in the SAP data types.
Other settings hold information about the parameters of a Glue table

Other settings of a Glue table

By the creation of a Glue table, you should specify further settings (table description, package name, Use meaningful fields option, etc.) which support proper handling and behavior of the Glue table functionality.
Glue serves as both, link to the already existing table in storage, or as a tool capable of creating such tables.

System events

DDIC stands on the top of the hierarchy of GLUE metaobjects. For this reason, other metaobjects are directly dependent on it.

Activation of a Glue table

This step does not influence other Glue components.

Deactivation of a Glue table

This step affects Glue extractors, InfoProviders and BPL. If a table is deactivated, all relevant extractors, InfoProviders and BPL are deactivated, too.

Deletion of a Glue table

This step is similar to deactivation of a Glue table but with an extra pop-up which deletes the relevant objects.

Alterations to a Glue table

This step does not influence other Glue components.

After alternation is for correct functionality of other Glue metaobjects recommended their reactivation.

Glue Dictionary screens

Initial screen

Glue dictionary maintains Glue tables. You can create, display or change desired Glue tables. Its initial screen is an entry for more complicated screens with details.

Here you can:

Create a Glue table: Metadata object along with an external table in a defined storage
Create a Glue table as a link to an existing table in a defined storage
Activate a table
Delete a table
Display a list of glue objects where a particular table is used

Attributes screen

Here you set up the Package (only virtual, no real object can be seen in SE80), storage and choose additional features, for example, Use meaningful values (see more detailed info bellow).

Fields screen

Defines a structure of the fields.

You can set up the field name and set it as a key field, define its type (with a data element or direct ABAP type), add a description or set up a partitioning for a table.

Please be aware that if you use Hive or Impala storage, you mustn't select all the fields in the column Partitioning. Selecting too granular data for partitioning may decrease performance and stability of the whole cluster.

Import Fields creates a Glue table structure based on either existing SAP table structure, InfoProvider or from a file.

Please be cautious when you change the content of table after fields import.

You can also define customized fields using button and filling in data element attributes.

Please note that the following data types are not supported when creating a Glue table:

LRAW, RAW, RSTR, VARC

Please note that the total number of characters by primary keys is restricted to 338.

Features:

Meaningful fields

SAP table fields tend to have field names that are not self-explanatory. An unskilled SAP-user doesn't understand the meaning of these fields. This can be solved by setting the Use meaningful fields checkbox during the creation of the table.

Example:

Here is a comparison of 2 tables, both created from SAP SFLIGHT table. The first table was created with original fields names, the second using Meaningful fields.

Original field names	Meaningful field names

Meaningful values

Data inserted into the DB does not always have values which can be easily understood. The values are often represented by their simplified or shortened versions. To display the more explicit versions of the values select the checkbox Use meaningful values in the Attributes tab.

Please note: After the installation of Glue it is necessary to execute the report '/DVD/GL_BPL_SPKFL_CONTENT_GEN' and '/DVD/GL_DD_SPKFL_CONTENT_GEN' which generate some of the meaningful values.

Example:

Table: SFLIGHT: field CARRID (Airline Code):

value in SAP table = translation to meaningful value

AA = American Airlines
AB = Air Berlin
AC = Air Canada
AF = Air France
AZ = Alitalia
BA = British Airways

As a result, instead of the shortened values such as AA, AB, AC e.t.c., the descriptive terms: American Airlines, Air Berlin, and Air Canada are inserted into the Glue table.

Data Enrichment

Data enrichment (or denormalization) includes data from SAP master data into transactional data, e.g. by inserting the text of a G/L account into the line items, or the name of a vendor into a purchase order.

During the ingestion of data, basic de-normalization of data from the original relational database model is performed. Such de-normalization is referred to as data enrichment. It includes additional fields in the structure of tables when generating them on external storage. Such fields are for example used to store the name of a customer or the text of a material number and include this data in the transactional data.

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