Data Loading Strategy for Global Data Warehousing Implementation Applies to: EDW, SAP BIW 3.5, SAP NetWeaver 7.0. For more information, visit the EDW homepage. Summary This document may help you in deciding the data loading strategy for global data warehousing implementation. Author: Nilesh Ramesh Ahir Company: IBM India Created on: 19 February 2011 Author Bio Nilesh Ahir has completed his masters in Software System from BITS Pilani. He has total 5 years of SAP experience. He has been working as SAP NW BI Application consultant for IBM India for last one year. Prior to this he was working with Intel India. He has experience in ABAP, BW3.5 / BI7.0 and Data mining. He has worked on other non SAP technologies like NLS, TIBCO and web services. 2011 SAP AG 1
Table of Contents Introduction... 3 Basics of mother Earth... 4 Time Zones... 5 Data Loading Strategy... 8 Advantages of this Type of Scheduling... 11 Additional Points... 11 System architecture requirements:... 11 How master data is handled:... 11 Alternate approaches:... 11 Disclaimer and Liability Notice... 13 2011 SAP AG 2
Introduction After realizing the advantages and benefits of single instance data warehousing implementation, most of the global companies are going for such kind of implementation. But it has been observed that a company whose business has been spread in many countries across the globe has major challenges in data availability in business warehouse for reporting purpose. This article will address same problem and will try to give you guideline for deciding the data load strategy that can be used for huge data warehouse implementation. Scenario: A multinational company wants you to decide the data load strategy for them with below inputs. Company has business in many countries across the globe Huge amount of data is getting generated due to business transaction in various part of world Reporting / decision making / analysis is region wise ASAP Data availability at end of business day for each region in EDW for reporting Single instance ERP (R/3) implementation with reusable ETL mechanism for EDW Applications Data loads to be complete within time span of 8 hours to avoid over lapping issues or inconsistencies due to partial data loads Business hours / business day 9:00 AM to 6:00 PM Single Instance SAP R/3 System Single instance SAP BW/ BI System Landscape for single instance SAP BW and R/3 2011 SAP AG 3
Basics of mother Earth Before going for designing the data load strategy for such huge implementation, let s understand the basic facts of our mother earth. Earth has been divided into 5 continents 1.ASIA 2.EUROPE 3.AFRICA 4.SOUTH AMERICA 5.NORTH AMERICA 6. ANTARTICA 7.AUSTRALIA And three regions 1. ASIA-PAC 2. EUROPE-AFRICA 3. AMERICAS 2011 SAP AG 4
Time Zones For measurement of time with respect to different regions across the globe, we have one reference line which divides globe into two equal halves. This line is known as prime meridian. The Prime Meridian is the meridian (line of longitude) at which the longitude is defined to be 0. The Prime Meridian and it s opposite the 180th meridian (at 180 longitude), which the International Date Line generally follows, form a great circle that divides the Earth into the Eastern and Western Hemispheres. An international conference in 1884 decided the modern Prime Meridian passes through Greenwich in southeast London, United Kingdom, known as the International Meridian or Greenwich Meridian, although the Prime Meridian is ultimately arbitrary unlike the parallels of latitude, which are defined by the rotational axis of the Earth with the Poles at 90 and the Equator at 0. Time of this place is known as GMT. Thus with reference to prime meridian, we can measure 12 hrs (equivalent to 180 deg) east or west direction. Also we can calculate the local time of particular location if we know the present Greenwich Mean Time i.e. GMT 2011 SAP AG 5
Below picture talks about how much time Mumbai is ahead of London. Thus if GMT = 08:00 AM, Mumbai time will be 1:39PM. Similarly below picture is giving time with reference to GMT 2011 SAP AG 6
From time clock point of view, globe has been divided into time zones Time Zone 12 11 10 9 8 7 6 5 4 3 2 1 We can divide globe into 12 time zones each equals to 2hrs equivalent to 30 deg. 2011 SAP AG 7
Data Loading Strategy To make data available in data warehouse at the earliest possible, we have to extract data from R/3 and load it into BW at end of business day for each region rather than waiting for end of calendar day by having region wise data load schedule. As we know that there are three regions ASIA-PAC, EUROPE-AFRICA and AMERICAS. Therefore we can have below three schedules. Region ASIA-PAC EUROPE-AFRICA AMERICAS Schedule AP EA AM Time zones 1,2,3,4 5,6,7,8 9,10,11,12 As in our scenario, business day ends at 6PM (local time), we can start loading data at 7:00PM (Keeping buffer of 1 hr) into Data warehouse. But here we are talking about local time of last time zone of particular region. That means if we want to start data loads for Asia-Pac region, then we have to start it at 7:00pm (local) of time zone 4. Thus in each schedule, data for four time zones are getting loaded. Schedule AM (Americas) EA (Europe-Africa) AP (Asia-Pac) 7:00PM 7:00PM 7:00PM 2011 SAP AG 8
Difference between two schedules is 8 hours; therefore we have to optimize our ETL process to load data of particular region within time span of 8 hours to avoid overlapping issues. Now check the location of country in which company has business and identify the time zone and corresponding schedule. If organization has multiple companies registered in a country then name the company codes using country code followed by two digit sequence number. Schedule Region Schedule Time Local Schedule Time - IST Schedule Time - GMT AP Asia-Pac 07:00PM 08:30PM 03:00PM EA Europe-Africa 07:00PM 04:30AM 11:00PM AM Americas 07:00PM 12:30PM 07:00AM 8Hrs AM AP 8Hrs 8Hrs EA 2011 SAP AG 9
Company code = <ISO country code> + ( _ ) underscore + <Time Zone 2 digit> + ( _ ) underscore + <sequence number 2 digit> Example : for Australia comp code = AU_01_01, AU_02_01,.. etc. Complete list of country codes http://www.nationsonline.org/oneworld/countrycodes.htm Few examples for showing the relationships - Country Country code Company code Time zone Region Schedule Australia AU AU_01_01 1 Asia-Pac AP Australia AU AU_02_01 2 Asia-Pac AP Australia AU AU_03_01 3 Asia-Pac AP China CN CN_01_01 3 Asia-Pac AP China CN CN_02_01 4 Asia-Pac AP India IN IN_01_01 4 Asia-Pac AP South Africa ZA ZA_01_01 5 Europe-Africa EA South Africa ZA ZA_02_01 6 Europe-Africa EA Switzerland CH CH_01_01 6 Europe-Africa EA Spain ES ES_01_01 7 Europe-Africa EA Iceland IS IS_01_01 8 Europe-Africa EA Argentina AR AR_01_01 9 Americas AM United States US US_01_01 10 Americas AM United States US US_02_01 11 Americas AM United States- Alaska United States- Alaska US US_03_01 12 Americas AM US US_03_02 12 Americas AM 2011 SAP AG 10
Advantages of this Type of Scheduling Lesser system load due to data distribution improved system performance Less time required for data loads Faster Data availability as compare to end-of-calendar-day scheduling approach Advantage over market competitors if followed below business model Region Location of Decision makers Time Zone Asia-Pac Americas 9 Europe-Africa Asia-Pac 1 Americas Europe-Africa 5 Additional Points System architecture requirements: Strategy will be very effective if you have used LSA (Layered Scalable Architecture). This can be used even if you do not have LSA implemented and you have BW implementation at region level. These regional BW boxes are further supplying data to EDW for global level reporting. In this case as well, this strategy will be helpful. In general, this strategy is useful if data is getting generated in different time zones across the globe. How master data is handled: Master data should be loaded before loading transaction data in each schedule. Alternate approaches: If company business is limited to particular region then end-of-calendar-day approach loading will be simple and effective. Having regional data warehousing systems providing data to centralize global data warehouse, but this is very costly solution. 2011 SAP AG 11
Related Content Complete list of country codes For more information, visit the EDW homepage 2011 SAP AG 12
Disclaimer and Liability Notice This document may discuss sample coding or other information that does not include SAP official interfaces and therefore is not supported by SAP. Changes made based on this information are not supported and can be overwritten during an upgrade. SAP will not be held liable for any damages caused by using or misusing the information, code or methods suggested in this document, and anyone using these methods does so at his/her own risk. SAP offers no guarantees and assumes no responsibility or liability of any type with respect to the content of this technical article or code sample, including any liability resulting from incompatibility between the content within this document and the materials and services offered by SAP. You agree that you will not hold, or seek to hold, SAP responsible or liable with respect to the content of this document. 2011 SAP AG 13