SAP R/3 Data Acquisition in Near Real Time

Naghman Waheed

Information Architecture Lead, Monsanto

Who Are We Monsanto is 100% focused on agriculture

Our Mission

We work to deliver agricultural products and solutions to:

• Meet the world’s growing food needs

• Conserve natural resources

• Protect the environment

“We succeed when farmers succeed.”

-Hugh Grant, Monsanto CEO

Monsanto Company is a leading global provider of technology-based tools and

agricultural products that improve farm productivity and food quality.

Who We Are Today We are committed to providing producers with agronomic tools that make them more efficient

and maintain their profitability in their farming operation. By helping producers be more

productive, with fewer resources and with less overall effect on the environment, we believe we

are making the world a better place.

•Headquartered in St. Louis, Missouri

•CEO Hugh Grant

• Approximately 22,000 employees

• 500 locations in 5 regions.

• $15.42 billion in annual sales

Produce with more

judicious use

of limited natural

resources.

While

improving the

lives of the

world’s

farmers.

Increase

production

to meet

needs of

a growing

population.

Meet the needs

of the present

while improving the

ability of future

generations to meet

their own needs.

• Treat data as an asset

• Eliminate data duplication

• Improve data quality

• Improve data accessibility

• Turn data into information and knowledge

Opportunities

• Develop key data models using enterprise-wide Information architecture techniques

• Key analytical data (OLAP) will be extracted and separately managed from transactional sources (OLTP)

• Key analytical and BI systems will utilize data models to support business operations

• Keep data as close to the source as possible

Objectives

Implementation

Create a centralized data

modeling repository

Establish an

integrated data

platform

Develop data models

for key functional

areas

Monsanto Information Models

Conceptual Architecture

x

x

x

Information Consumer

Transactional Systems

Web, Audio/Video, Text, Image

Data Scientists

Aggregated Information

“Big Data”

Core Data

External Partners

General Ledger

Current Assets

Expenses

Liabilities Profit /

Cost Centers

Sales Orders

Fixed Assets

Balance Sheet

Broad freedom to join all data types based on

iterative model development and the data-discovery process

Information Pro-Consumer

Monsanto Teradata Architecture

Data Staging

SAP

Salesforce

SAP Source Tables

Non-SAP Source tables

Core Data Models

Application Data Views

Reporting / Analytics Tools

Business Objects

Universes

Other Toolsets LAS

Vegetables Supply Inventory

Analyzer

Delivery Demand Planning

Material

(Cost

Simplification)

Unit costing model

Teradata

Production Planning

Process Orders

Delivery

Purchase Orders

Sales Orders

Goods Movement

Inventory

Other

2 3 BI

1

Excel

Informatica IDR

IBM Cast Iron

Teradata Labs

Sales

Billing

Grower Inventory Other

Informatica PC

Data Availability, Integration, Abstraction, Accessibility,

Uniqueness

• Timestamps do not exist on source tables in SAP systems

• Intermediate Document ( IDoc )

• Business Objects Data Services ( BODS )

• SAP Business Warehouse ( BW) – OpenHub

• ABAP Extracts

• SAP Query & SE16

• Database snapshots

• Third party tools e.g. Informatica’s Netweaver Connector

SAP data extraction - challenges

Near real time data replication architecture

System 1

Oracle

Stand Alone Oracle instance

No IDR binaries installed

System n

Initial Load

Archive

Logs

Archive

Logs

Initial Load

NFS share

Archive

Logs

NFS share

Archive

Logs

Teradata

Optimized Merge Apply Processing

IDR Binaries installed on Teradata

Server Only

Logs written

every 30 minutes

Informatica IDR

Server

• Data accessibility

> Simplified data extraction architecture

> Single data distribution architecture

• Reduced time to delivery

• 15 – 60 minute data updates

• Near real time and “snapshot” reporting

• Multipurpose platform

> Integration

> Business intelligence

Benefits

• Pooled and clustered tables – TAS 2.2

• Shift from batch processing to near real time

• New technology challenges

• Must have table recovery strategy

• Data validation strategy

• Data refresh policies

• Designing “configs” within IDR

• Preparation work with the BASIS team

Lessons Learned

PARTNERS Mobile App

InfoHub Kiosks

teradata-partners.com

Twitter: @

Email:

Naghman.Waheed@monsanto.com

nnwahe

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