Configuration steps¶

The process of setting up an SAP HANA Database Data Intake Process involves several key actions. The different sections of the input form are organized into these main steps.

Step 1. Configure Data Intake Process¶

Please see on the common Sidra connector plugins section (point 1) about the parameters needed to configure a Data Intake Process.

Step 2. Configure Provider¶

Please see on the common Sidra connector plugins section (point 2) about the parameters needed to create a new Provider.

Step 3. Configure Data Source¶

The data source represents the connection to the source database. A Data Source abstracts the details of creating a Linked Service in Azure Data Factory.

Some key aspects to take into account in this section are:

• The fields required are the Integration Runtime and the connection string to the database.

• Sidra SAP HANA connector plugin will register this new data source in Sidra Metadata and deploy a Linked Service in Azure Data factory with this connection.

• The created Linked Service will use Key Vault in Sidra Core to store the connection string to the database.

The information that needs to be provided is the following:

• Integration Runtime: this is the ADF integration runtime in charge of executing the connection between the data origin, and is associated with a deployed Linked Service. Select the Self-Hosted IR.
• Server Name: name of the server to connect to.
• Port: port used to connect to the server.
• Username: user with access to the database.
• Password: user password.

Step 4. Configure Metadata Extraction¶

Sidra SAP HANA Database connector plugin replicates the schema and tables from the source database, by querying the TABLES System View.

The extracted schema is replicated to the Sidra Metadata model hierarchy. Tables in the SAP HANA Database source system are replicated in Sidra as Entities, and columns of these tables, as Attributes.

The Entity table in Sidra metadata model contains data about the format of the Entity generated, as well as information about how this Entity should be handled by the system. Attributes contain data about the columns of the tables. Sidra adds additional system-level Attributes to convey system-handling information at the level of the Entity (or table).

The metadata extraction represents in which way the origin will be consumed in order to obtain its metadata. The information fields required to fill in the Metadata extraction section will be used for three main purposes internally in Sidra:

• Create and populate the Sidra Core metadata structures (Entities and Attributes).
• Create, deploy and execute an Azure Data Factory pipeline for the metadata extraction steps (see below steps).
• Execute the create tables and DSU Ingestion scripts, including all the steps for storing the optimized data in the data lake storage.

These are the metadata extractor configuration parameters to be input for this version of connector plugin. All of these parameters are optional:

• Number of Tables per Batch: this is used internally in the metadata extractor pipeline in order to specify the number of tables that will be consumed per batch.
• Types of Objects to Load: rows, column or/and virtual.
• Object Restriction Mode: there can be three options here:
  • Include all objects: all objects from all databases will be imported.
  • Include some objects: to specify a list of objects to include.
  • Exclude some objects: to specify a list of objects to exclude.
• Enabling the metadata to be refreshed automatically with each data extraction is available. Once the metadata extractor pipeline has been created, it is executed. This execution could take a while (from 5 to 15 minutes) depending on the load of Data Factory and the status and load of the associated Databricks cluster. Sidra exposes API endpoints as well to manually execute the Metadata extractor pipeline.

Step 5. Configure Trigger¶

Please see on the common Sidra connector plugins section about the parameters needed to set up a trigger.