This blog starts with a very simple example to schedule a Python file on Cloud Foundry, just to introduce the most important steps. That concept is then extended to schedule a Python file, which applies a trained Machine Learning model in SAP HANA.
We would like to schedule a Python file, not a Jupyter Notebook. Hence use your preferred local Python IDE or editor to run this simple file, helloworld.py.
If you are a user of APL time series, you probably have seen models fitting a linear trend or a quadratic trend to your data. With version 2113 the Automated Predictive Library introduces an additional method called Piecewise Linear that can detect breakpoints in your series. You don’t have to do anything new to take advantage of this functionality, the trend is detected automatically as shown in the example below.
For SAP Analytics Cloud users, note that Piecewise Linear Trend is coming with the 2021.Q3 QRC (August release).
In this blog we will create an Azure file share and use it as a destination for SAP SDI FileAdapter.
Prerequisites
1. Azure Account
2. SAP HANA Data Provisioning Agent server enabled
3. SAP HANA Data Provisioning Agent installed
Let’s start by creating the Azure file share. Login to the Azure portal and create a premium storage account as depicted in the below screenshot
There are various ways of deploying the XS Advanced runtime. Before deploying the XSA many scenarios need to considered that will have impact on your landscape maintenance activity .For example the system refresh scenarios where you will need to refresh only a certain tenants instead of complete system this deployment plays a key role . I will go through the limitations in detail in backup restore section. In general, The following additional services run where XSA is deployed:
◉ xscontroller: Central management component of XSA. It has a view on all deployed and/or running applications, and persists configuration and status information in the database
Running SAP on the HANA database is not only having multiple advantages, but also few cons as well. The hardware cost to run the HANA database is one of them. Since HANA is an in-memory database, customers must provision higher-size RAM and CPU to run their SAP on HANA. Even in hyper scaler environments like AZURE/AWS, HANA VM (M-Series) cost is huge comparatively and concern for most of the customers.
Keeping this in mind, designing a disaster recovery solution for HANA is a very challenging one.&To address this design challenge, we can consider multiple cost-optimized options like
With the release of SAP S/4HANA 2020 Feature Pack 1 (onPremise), DMEEX receives one of the biggest updates in its lifetime. We implemented a tool that opens and clarifies DMEEX Engine processing. In this tool, you can see and analyze how the DMEEX tree was processed in detail, without the need to debug its code. We call this tool the DMEEX Trace Tool.
SAP HANA System Replication is a reliable high availability and disaster recovery solution that provides continuous synchronization of a HANA database to a secondary location either in the same data center or remote site as a disaster recovery site.
More Info: SAP HANA Certification
In this article fI have tried to keep it simple on how to setup a system replication when you want to replicate single primary to multiple target which may be in same data center or at remote site.
For better understanding let us assume below.
