How SAP HANA Cloud Helps to Transfer Large Amounts of Data

 SAP HANA, the business analytics solution from SAP, caters to modern businesses. The SAP HANA Cloud delivers the same set of capabilities in the cloud. In the era of cloud-first, the architecture that SAP HANA offers has multiple benefits. There is flexibility in terms of deployment and cost of ownership. It facilitates real-time analytics and impressive processing performance, as well. Replication through Smart Data Integration (SDI) allows easy transfer of large volumes of data. With the in-built ETL tool, SAP HANA Cloud removes the need for installing a separate ETL application for data transfers. In a data-centric world, the ability to integrate data from different source systems with ease is key. 

 Let’s have a look at how SAP HANA Cloud works to transfer large amounts of data.

 

Setting up the SDI

 

The SDI is the Extract, Transfer, Load (ETL) feature in the SAP HANA Cloud. This is the feature that enables the transfer of large volumes of data. Therefore, setting up the SDI is the first step towards easy data transfers. In setting up the SDI, the configuration of DP Agent (Data Provisioning Agent), the creation of virtual tables, and transferring the data are the constituent steps. 

 

The configuration of the DP Agent involves downloading and running the tool. It then allows the configuration of the connection between the on-premise system and the SAP HANA Cloud. All the registrations and permissions necessary for connecting the two nodes are ready.

 

Accessing data through virtual tables

 

Once the connections are available, the creation of virtual tables begins. By creating virtual tables in the SAP HANA Cloud, one can access data from the other system. Creating a remote source and granting privileges are essential to creating virtual tables. Retrieving data becomes simple once these virtual tables and target tables are in place. 

 

Transferring the data

 

The transfer of data takes place with the help of SDI flowgraphs. By creating flowgraphs and configuring the source of data, one can prepare the base for data transfer. The data source is then added to the table. Similar steps allow the configuration of the target table.

 

Next up, the mapping of the source table and the target table becomes vital. By inserting the operator for projection between the data source and the data target, one can configure the mapping automatically.  This completes the flowgraph. Running and executing it allows the smooth transfer of data. 

 

Within the flowgraph, one does not have to load all the data at once. Creating task partitions and loading divided data brings in much-needed efficiency into the process. Creating sections for data transfer is easy with the partition configuration settings. Similarly, one also has to create partitions in the target table.

 

Conclusions

 

The above set of steps provide a basic framework for data transfer with SAP HANA Cloud. The approach allows the convenient transfer of large chunks of data. The options for optimizations of transfer available in the system can add even more efficiency. As a result, moving significant volumes of data in limited periods of time becomes feasible.