![]() ![]() It uses the Spark engine and a set of rules to combine all extracted records together without any additional activity from your side. This is where the Mapping Data Flow comes into place. Otherwise, you’ll end up with inconsistencies: duplicated rows (for records updated in SAP) or lines that should not exist any longer (for records deleted from the SAP system). Plain inserts are insufficient – you must take care of all CRUD operations and respectively update the target storage. Whenever you extract new and changed information, you must carefully analyse how to treat it in relation to the target datastore. The new SAP CDC connector uses the Mapping Data Flow functionality to automatically merge subsequent delta extractions into a consistent data store. Such an architecture allows replicating the data no matter where your SAP system is deployed – on-premises or in any cloud. It goes without saying that network connectivity between the Self-Hosted Integration Runtime and the SAP system is essential for data extraction to work. It needs a Self-Hosted Integration Runtime (SHIR) installed on a local server, ideally close to the source SAP system, that provides compute resources to replicate data. Azure Data Factory is a cloud service that orchestrates the extraction process but can’t connect directly to the data source. You can find more information in following SAP Notes:īefore we dive deep into the configuration, let’s quickly walk through the solution’s architecture and all required components. The SAP Operational Data Provisioning framework is included in every modern SAP NetWeaver release, but you may need to install some notes or support packages. The SAP Operational Data Provisioning supports both scenarios, but accessing information stored directly in tables requires SAP SLT that uses trigger-based replication to track changes. Instead, you can use Extractors and CDS Views that already provide the data in a multidimensional format and are widely used in SAP Business Warehouse. In such cases, you require additional processing to transform the schema of the data. Tables provide highly normalized data, but they are not so efficient when it comes to analytical scenarios. We can distinguish two types of data sources in transactional systems, like SAP S/4HANA or SAP ERP. It’s like outsourcing complex challenges to the source system. The logic of correct selecting data is already built-in into these objects, so instead of re-creating it, you can focus on delivering value to your business. The framework works with a set of source SAP objects, including extractors, CDS Views and SLT and manage the extraction process end-to-end. ![]() With SAP Operational Data Provisioning, you don’t have to worry about it at all. Comparing creation and changed dates was highly unreliable moreover, some of the most frequently pulled tables did not even contain such fields. So far, if you wanted to provide such capability in your Azure Data Factory pipeline, you had to create a complex logic that uses watermarks to select relevant data. In addition, it takes care of identifying new and changed information in the source objects. The improved communication layer ensures the data transfer is reliable and performant. ![]() One of its core functionalities is simplifying the data extraction and replication processes. The SAP Operational Data Provisioning (SAP ODP) framework consists of function modules and reports. And what I like the most, it also automatically merges all delta extracts into a consistent target data store. It’s an actual game changer – the new connector is robust, performant and reliable. ![]() The new SAP CDC connector, released a couple of months ago and currently available in Public Preview, uses the SAP Operational Data Provisioning API to extract data. Therefore I’m super excited that Azure Data Factory has a new family member. SAP Table connector, one of the most frequently used by SAP customers, heavily suffered from these challenges. Transactional tables often contain millions or even billions of rows, and as the extraction process resource-intensive operation, the daily processing of the entire dataset is usually impossible. Today I cover something BIG! If you have ever extracted data from an SAP system, you know it can quickly become a complex process. I’m interested mainly in application and data integration, so whenever new functionality is available, I want to test it and share it with you. From time to time, there are updates to Azure services that excite me more than others. ![]()
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