Estimation of SAP HANA Replication Network Requirements in Optimized Way

The network connection between the primary and the secondary system impacts the overall performance of the SAP HANA systems if not configured correctly .For SAP HANA system replication it is critical that the network throughput (or bandwidth) and network latency requirement are sufficient to fulfill the optimal network requirements.

In this article, I am going to explain – How to measure optimal network bandwidth requirement for HANA replication setup and other important considerations for network throughput and latency.

Let’s take an example of 3-tier multi-target replication setup.

From DC1–> DC2 {SYNC | logreplay | delta_datashipping}

Using HANA DB BLOB fields to store/retrieve File Attachments using Node.JS and SAPUI5

Introduction

When using Node.JS in SAP BTP Cloud Foundry, you may have the need to store/retrieve file attachments in your NodeJS Application (backend) and SAPUI5 (front end). There are several approaches, of these the main would be:

◉ File System

◉ File Storage

◉ SAP HANA Database Storage, as BLOB (Binary Large Objects)

SAP Digital Vehicle Hub: Integration with SAP S/4HANA Vehicle Management System for Vehicle Master Data

This blog covers the scope of vehicle master data integration and how it works together with vehicle integration delivered by earlier releases of SAP Digital Vehicle Hub.

Pre-requisites:

◉ S/4HANA 2020 FPS 01 or higher

◉ SAP Digital Vehicle Hub 1.1 or higher

◉ SAP Cloud Platform Integration with JMS queue

Housekeeping of HDI containers in developer workspaces

Introduction

When ever a developer is doing local build of a db module in WebIDE for HANA, it creates an HANA service instance that name looks like <USER><GUID><MODULE_NAME>.

You can see those HANA service instances via XSA Cockpit

Flexible Billing document numbering – State wise number range (without use of FIORI App)

This blog is to handle the requirement in SAP S/4 HANA 2020 On-Premise version for different billing document number prefixes based on the state (region) and the different number range for the billing type as different billing types are used. Few billing types are GST relevant, few are VAT relevant, and others.

Solution scope based on the new concept of SAP S/4 HANA 2020 – Flexible billing document numbering.

Prefix should be defined based on the region (state) and different number range intervals will be used for configured billing types. If flexible document numbering is not configured for any particular billing type then it will pick the standard number range interval assigned to that billing type.

SAP RETAIL Allocation Rule based on Material Group

The following function demonstration is based on the system of SAP S/4 HANA 1909, which activates is-retain.

1. There are below articles of material group 101010101.

Running SAP HANA on Azure Spot Instances

What is SAP HANA?

SAP HANA is an in-memory database and application development platform used to process large volumes of information in real-time.

SAP HANA lets data analysts query sizable volumes of information in real-time. HANA’s in-memory computing means no time is wasted loading or writing data to disk, allowing analysts to perform queries much faster. 

Launch of Enhanced Financial Data Quality Check in SAP Readiness Check for SAP S/4HANA

The Financial Data Quality check within SAP Readiness Check has been enhanced and extended to provide further insights. This new version of the Financial Data Quality check includes the ability to analyze asset accounting data, in addition to general ledger data. As with general ledger data inconsistencies, it is critical to identify and resolve asset account inconsistencies before or as part of a conversion project to SAP S/4HANA.

In addition to the support of an additional area of finance, the Financial Data Quality check tile and detail pages have been enhanced to clearly display the findings from the analysis. Grouped by finance area, the detail view includes:

Manage XS Advanced via SQL

Introduction

The main tools for XS Advanced administration are the XS Command-Line Interface and the XS Advanced Cockpit.

For both of them, the prerequisites are to have an XS Advanced user with required role collections and also to be member of the organization/space you want to manage.

If you are using XSA only in the context of Data modeling (HDI Advanced Model) via WebIDE for HANA, you may face the situation that developers have XS Advanced users in development system but only basic database users in quality and production systems. For security reason, you may not want to provide them access to XSA Cockpit in Quality and Production systems.

RISE with SAP: ‘Defence in Depth’ Security Architecture with SAP S/4HANA Cloud (Public Cloud)

Introduction

The SAP S/4HANA Cloud is at the core of “Rise with SAP” offering. For a customer, it holds mission critical data used to implement business processes and data analytics. It is deployed in a public cloud as SaaS (Software-as-a-Service) offering. SAP S/4HANA Cloud is a “Multi-Tenanted” platform, delivering highly standardized content for various lines of business in an industry and for varieties industries. As digital transformation sweeps the IT industry, embracing cloud technologies is inevitable. In this context, cloud security is imperative to this transformation.

SAP S/4HANA Cloud runs on public cloud such as Google Cloud Platform (GCP), MS Azure and SAP Converged Data Centres around the world. SAP uses hyperscale providers who offer “Infrastructure-as -a-Service for greater flexibility, scalability, and security for the provisioning of cloud infrastructure. SAP S/4HANA Cloud tenant subscription includes provisioning of SAP Business Technology Platform (BTP) services such as Identity Authentication Services, Identity Provisioning Service, SAP Analytics Cloud, and In-App & Side by side via BTP / Extensibility Framework.

SHAP-explained models with Automated Predictive (APL)

To address classification and regression machine learning scenarios, APL uses the Gradient Boosting technique. As for explaining what the predictive model does, APL relies on the SHAP framework (SHapley Additive exPlanations).

In this blog we will see how to extract and visualize SHAP values to obtain a:

1. Global explanation from the APL trained model.

2. Local explanation regarding an individual prediction.

Let’s go through a classification example: Detection of Car Insurance Fraud.

Understanding Attributes ,Text, Value help in S/4 HANA ABAP CDS views

Introduction: 

This Blog post provide information and understanding on very important features in S4HANA Embedded Analytics which is Text, Attributes, and value help in CDS views.

Overview:

We face multiple requirements in Reports based on CDS views related to text, Attributes and Value help.

SAP Analytics Cloud – Replicating BW Analysis Authorizations into Dimension Read/Write Property

In this blog post, I will be tackling a much more common use case, which is the BW Analysis Authorizations to be replicated into the SAP Analytics Cloud public dimenions’ read and write properties using a HANA calculation view.

Pre-requisites

◉ At least SAP HANA 2.0 SPS 1.

◉ User IDs for each user on SAP Analytics Cloud to be the same as their BW users.

   ◉ If there is a prefix or a suffix included, it can be appended to the code, by concatenating either of them to the final UNAME.

How to create Rate Table in SAP S/4HANA Cloud?

For every transportation services there is a cost involved, we can group these costs for a specific transportation service in a table called rate table.

In the rate table you can define scales, validity and once the rate table is defined you can maintain rates.

In this blog post I will describe how we can set-up a simple rate table in SAP S/4HANA Cloud.

In order to create rate table, you need to login as “transportation_mgr” and go to the app “Manage Rate Tables“.

Explainable forecasting using HANA and APL

This is part 2 in a two-part series of blog posts on large-scale and explainable forecasting using APL. In part 1 I have outlined a way to utilize the APL library for in-database training of a regression model in HANA in order to be used together with an external Node.js inference script.

In this part of the blog post I will dive deeper into built-in functionality to retrieve insights into a trained model which is called the ‘model debrief’.

Explainable AI

Being able to explain the inner workings of a model (also “explainable AI” or “XAI”) is an important topic in applying machine learning in enterprise environments and is recently gaining much attention. The APL regression modeling algorithm uses a gradient boosting tree under the hood which is extremely well suited for this purpose.