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I’m so happy to find time to start SAP blogging, this is my first blog and first is always the special one 🙂

When we use various SAP cloud products then we require a solution to manage our data and integration part. SAP has classified its solution/products in various categories and each product is responsible for certain use cases. Few products have some features in common but the selection of tool entirely depends on our requirement.

I found one SAP product to be very useful hence decided to start with it.

What is SAP Data Intelligence?

SAP Data Intelligence is a comprehensive data management solution. As the data orchestration layer of SAP’s Business Technology Platform, it transforms distributed data sprawls into vital data insights, delivering innovation at scale.

This product is known to be one of the robust product under SAP Integration suite. Provides Data Integration, Data Management & Data Processing –A complete solution.

Architectural view of SAP Data Intelligence

(Image source: SAP® Integration Solution Advisory Methodology(Published by SAP))


Highlights of Product:

  • SAP Data Intelligence is a comprehensive data management solution.

  • Provides Data Integration, Data Management & Data Processing –A complete solution.

  • Launched in 2019 and known to be as combination of two products "SAP Data Hub" & "SAP Leonardo Machine Learning Foundation" with some additional connectivity features.

  • SAP Data Intelligence is available as a BYOL (bring your own license) model where it can be deployed on-premise in your own data center, on any hyperscaler public (AWS, Google, Microsoft) or private cloud.

  • Suppose if it is hosted on AWS then it has EC2 instance running behind it –Scalable according to the requirement.

  • In the DI backend we have SAP HANA, Application layer is AAS(ABAP), front end is combination of SAP Fiori.

Connectivity of SAP Data Intelligence with On-premise can be done in two ways only:

  1. Cloud Connector

  2. Site to Site VPN

Detailed feature of SAP Data Intelligence

When we say it as "Data Management solution", first thing that comes to our mind is that what all things it supports. Selection of the tool majorly depends on the our requirements. Below image will show you the glimpse of all forms of connectivity whether it's with SAP products or with third party products. The center box of the image shows all types of data related capabilities which SAP DI can provide.

  • Features & Connections with various sources (Shown in below image)

(Image source: SAP® Integration Solution Advisory Methodology(Published by SAP))


(Image source: SAP® Integration Solution Advisory Methodology(Published by SAP))


Below are data related terms - Common across various platforms (These terms relate to SAP Data Intelligence features as mentioned in above image)

Data Discovery - It is a term used to describe the process for collecting data from various sources by detecting patterns.

Data Profiling - It is a detailed analysis of source data. It tries to understand the structure, quality, and content of source data and its relationships with other data.

Metadata Cataloging - It 's collection of all the data about your data. Metadata can include the data source, origin, owner, and other attributes of a data set.

Data Ingestion - It is a process by which data is moved from one or more sources to a destination where it can be stored and further analyzed.

Data Processing -Data processing occurs when data is collected and translated into usable information.

Data Enrichment - It is a general term that applies to the process of enhancing, refining, and improving raw data.

SAP Data Intelligence has very broad number of connectors and built in adapters, a simple example of SAP DI pipeline has been shown below:

(Image source: created by me)


This blog provides you introduction of SAP data intelligence and some of the features to start with the product.

SAP Data Intelligence helps us to understand business data with comprehensive metadata management capabilities. These include business rules, data lineage, a business glossary, and a rules dashboard to help ensure that everyone is on the same page. It's Centralized access to multiple data sources makes it easier for diverse teams – including data engineers and architects, machine learning and data developers, and core IT teams – to collaborate.

In addition, you can use machine learning (ML) to create and deploy ML models.

Important References:

Get started with trial edition to get an idea of this tool:

Thanks for reading. Do comment your views, I believe in regular feedback so that I can refine blogs for you all.

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