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If you completed the setup successful you will get the following prompt.

Next, enter your username and password and choose OK. Then on System DNS choose Add and select HDBODBC.Įnter the Data Source Name, Description and Server:Port and press Connect. After completing the installation open the ODBC Data Source Desktop app. Please follow the steps in this guide to unpack the. In case you do not have the HANA ODBC client installed, you can download the client from the SAP Support Launchpad. To connect to any SAP HANA system, you will need the ODBC client. Enable the power of the in-memory database SAP HANA for data preparationįirst, we need to connect to our SAP HANA using DBI::dbConnect() through ODBC.Generate SQL queries directly in RStudio through the dbplyr package.Connect to your SAP HANA through the odbc package.

This means you can continue to use the functions out of the dplyr package with which you are familiar with. The goal of the package dbplyr is to automatically generate certain SQL statements for you, focusing on select statements. The R package dbplyr brings both worlds together and is designed to work with database tables as if they were local data frames in R. Hence, I want to be more flexible but still use the power of SAP HANA. For example, after the first modeling phase I may have to go back into the data preparation phase to engineer new features. But as a huge R fan I want to stay in my used environment and not switch back and forth.

To work on a database like SAP HANA you usually work with the Structured Query Language (SQL) which is over 40 years old. Often, the data does not just reside locally in a csv or excel file on our laptop but originally lies in a database like SAP HANA. This is by my experience one of the crucial parts during a Machine Learning use case and usually takes most of the time. Hence, before the fun part the cooking and eating can start, we need to acquire, organize and structure our data. Imagen before cooking your favorite meal you didn’t know if the supermarket is open or if the food is even edible. While we can usually simply go to our local supermarket and acquire our raw ingredients, it is often not that easy for a data scientist. Data science is quite similar to cooking and making your favorite meal.