How did we move one of the world’s largest SAP BW HANA landscape to Microsoft Azure?
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Coke One Services North America (CONA) wanted to lower costs, increase speed and get long term BI and analytics capabilities. They partnered with Microsoft, SAP, and Capgemini to migrate 9 different SAP landscapes to Azure.
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How did we move one of the world’s largest SAP BW HANA landscape to Microsoft Azure?
- 1. Capgemini SAPPHIRE Theater Presentation
- 2. How do you get one of the world’s largest SAP BW HANA landscape to Microsoft Azure? Rene Mensink Director Capgemini
- 3. Agenda • CONA Overview • Microsoft, SAP and Capgemini • CONA Project Scope • Challenges and Solutions • Summary of Insights
- 4. CONA (Coke One North America) • CONA at a high-level (2018) • Provider of I/T system and services for Coke Bottlers across North America • 12 bottlers in NA • $21.2B in net sales revenue • Over 400 sites deployed • Over 25,000+ named users • Approx. 1.8M ship-to customers • Strong relationship with Microsoft • Scope • Migrate 9 landscapes from a legacy hosting provider into Azure • HANA; BW; SLT; BODS, Tableau; BODS and BOBJ • Minimal user impact with improved or no change to user experience • Timelines • Project design and 2017 Q4 • Initial Go-live cut-over scheduled for May • No freezes or impact to Business projects - BAU Bottler Bottler Bottler Bottler Bottler Bottler Bottler Bottler Bottler Bottler Bottler Bottler
- 5. Microsoft and Capgemini 1997 2003 2006 2010 2013 2014 2015 2016 2017-current Formal relationship begins Capgemini a leading global systems integrator of Microsoft New alliance agreement Joint marketing, demand generation and partner solutions for enterprise clients Alliance agreement expands to help enterprises make the move to cloud Global GTM for solutions based on Microsoft Business Productivity Online Suite Cloud Early Adopter Programs and Advisory Councils Launched hybrid cloud capabilities leveraging Microsoft’s Cloud Platform System Launched Sogeti OneShare for Test, ALM and DevOps Launch of Capgemini Cloud Choice with Microsoft Winner: Microsoft Enterprise Partner Group Azure Leadership Award Microsoft Azure Specialists in Center of Excellence with IaaS, PaaS and SaaS expertise (OneDeliver) Go live for Mosaic in the US: One of the largest SAP migrations to Microsoft Azure Microsoft Partner of the Year Finalist Award for OneShare 2018 Capgemini France, Microsoft Country Partner of the Year 2018 Microsoft Partner of the Year DevOps Finalist 2018 Cloud Solution Provider (CSP) and India Center of Excellence Over 24k consultants worldwide and growing with alliance presence in five continents 2017 Azure Expert, Managed Services Provider (MSP) – pilot participant Finalist for DevOps partner of the year in Microsoft’s partner of the year awards Current Microsoft competencies: • Gold Application Development • Gold Application Integration • Gold Application Lifecycle Management • Gold Cloud Customer Relationship Mgmnt • Gold Cloud Platform • Gold Cloud Productivity • Gold Collaboration and Content • Gold Customer Relationship Management • Gold Datacenter • Gold Data Analytics • Gold Data Platform • Gold Devices and Deployment • Gold Enterprise Resource Planning • Gold Hosting Select joint accounts (externally referencable) 1997 2017-19 Mosaic (US) PostNL (NL) Enexis (NL) CONA (US) Capgemini is one of Microsoft’s Top Global SI Partners, with a 20-year record in applying innovation to the enterprise
- 6. SAP and Capgemini • SAP Statics • 17,500+ SAP Qualified Resources Globally • 1,300+ SAP clients worldwide • 30+Delivery and Solution Design Centers around the globe • SAP Awards • 2018 SAP Pinnacle Award in the category Customer Choice Partner of the Year – Large Enterprises • 2018 SAP Pinnacle Awards Finalist in the SAP Leonardo Partner of the Year category • SAP Certifications • Run SAP Partner, Global Certification • Run SAP Methodology, Global Certification • Application Lifecycle Management, Global Certification • SAP HANA Operations Services, Global Certification • SAP Cloud Services, Global Certification • SAP Hosting Services, Global Certification • SAP Mobile Operations Services, Global Certification • SAP Support Center of Expertise, Certification
- 7. CONA Project Summary • An intensive seven (7) month project whereby Capgemini migrated SAP BI HANA, from on-prem at two (2) outsourced datacenters to Microsoft’s Azure HLI & VLI facilities. Breaking new ground as the size, complexity and scope of this project is unprecedented per Microsoft and SAP. Microsoft and Capgemini designed and migrated what is now the world’s largest HANA landscape in the Azure Cloud. Microsoft, Capgemini, and CONA executed with a small but tightly integrated team. • It is an Infrastructure as a Service (IaaS) transition which brought performance improvements and several millions in annualized savings. • CONA consisted of 9 different landscapes • 40 HANA Large Instances (VLI’s & HLI’s) • 32 x s384 4TB nodes & 8 x s192 2TB nodes • Production consisted of a 6+1 HANA scale out config. • D/R was 100% match of production • Production HANA Database was 12+TB • Multi-Tier HANA System Replication was executed. • Prod “Site A” replicates to D/R “Site B” • “Site B” replicates to Azure site “C” via HSR 6+1 7+1 8+1 9+1 10+1 11+1 HANA Node Config Counts
- 8. CONA Landscape Overview CONA DC 1 S - D - Q T - L CONA DC 2 M - R P Hosting DC 1 S-D-Q-M T-R-P Hosting DC 2 L D/R Las Vegas San Francisco B B B B B B B B B B B B AT&T L3 9 CONA Landscapes S=Sandbox D=Development M=Maintenance L=Performance Test Q=Quality Assurance T=Dry Run R=Pre-Production P=Production D/R SAP Business Layer/Frontend Layer • ECC • CRM • HRM • SRM • SCM • PI SAP Analytics Layer • BW • HANA • BOBJ • BODS • SLT • Tableau Atlanta Sterling
- 9. Tools used for Migrations Combination of Microsoft and SAP tools were used for the CONA migrations. SAP HANA System Replication (HSR) is a tool for replicating the SAP HANA database to a secondary database or location. The secondary database is an exact copy of the primary database and can be used as the new primary database in the event of a takeover. The advantage of HSR is that it replicates the data directly from source to target. At CONA, HSR was used for all Large HANA instances running on dedicated hardware (HLI’s/VLI’s) Azure Site Recovery (ASR) replicates workloads running on physical and virtual machines (VMs) from a primary site to a secondary location. When an outage occurs at the primary site, fail over to secondary location occurs, and application access is now running from there. The benefits of ASR is its cost-effectiveness and ease of use. ASR was utilized for all non-HANA migrations
- 10. CONA Landscape Azure Landing Zones Azure Express Route Azure Express Route Azure US EAST 2 Azure US WEST Azure US EAST L HANA Phy Azure US EAST Co-Lo HANA Phy HANA Phy HANA Phy R Q T P HANA Phy D/R HANA Phy Azure US WEST Co-Lo VMs VMs VMs VMs VMs S VMs M VMs D VMs CONA DC 1 S - D - Q T - L CONA DC 2 M - R P Hosting DC 2 L D/R San Francisco B B B B B B B B B B B B AT&T L3 Internet Atlanta VMs Hosting DC 1 S-D-Q-M T-R-P Las Vegas B A C D Sterling E
- 11. Data Validation Every Step of the Way Azure Express Route Azure Express Route Azure US EAST 2 Azure US WEST Azure US EAST L HANA Phy Azure US EAST Co-Lo HANA Phy HANA Phy HANA Phy R Q T P HANA Phy D/R HANA Phy Azure US WEST Co-Lo VMs VMs VMs VMs VMs S VMs M VMs D VMs CONA DC 1 S - D - Q T - L CONA DC 2 M - R P Hosting DC 2 L D/R San Francisco B B B B B B B B B B B B AT&T L3 Internet Atlanta VMs Hosting DC 1 S-D-Q-M T-R-P Las Vegas B A C D Sterling E Validation completed every pre & post migration. Of the nine the landscapes, no two were the same. Each had it’s own set of challenges and solutions
- 12. Visually Document Documentation of Instances Taking the typical landscape inventory: Every Instance and every landscape was documented in the same manner. SAP Instances, Bolt-on instances and Infrastructure shared systems Then arranged horizontally next to each other, per landscape vlrbid001 PHN Master SEL 12 176 x 3072 123.45.67.890 123.67.45.890 890.12.34.567 S384, 384c, 4TB Legacy IP address Azure IP address Application Type Host Name SAP SID App usage type O/S & Version CPU & Memory IP Tables IP address Azure build size/SKU As -Is To-Be AS-Is To-Be App HANA HANA BW 7.4 Host vlsbid001 vlsbidt01 vlsbic001 End State SID SHN SHN SHB 1+0 Type HANA DB HANA DB BW CI O/S SEL 11 SEL 11 SEL 11 CPUxRAM 20x300 12x32 8x64 Sandbox Legacy IP: 123.45.67.123 123.45.67.890 123.45.67.890 Las Vegas NV AZURE Azure IP: 123.67.45.890 123.67.45.890 172.29.248.126 IP Table IP: N/A N/A N/A USDC03 USEAST2 Azure size m64s, 64c, 1TB m64s, 64c, 1TB E8 v3: 8c, 64GB HANA HANA BW 7.4 BI BOBJ IPS BI BOBJ IPS BODS DS SLT SLT Tableau File Server D Landscape vldbid001 vlsbidt01 vldbic001 vwdbob001 vwddsc001 vwddsd001 vldstd001 vldstc001 vwdtab001 vldfile001 Not Provision DHN DHT DHB DBO DBO DDS DLT DLT DTB File Server End State Yet HANA DB DT Node BW APP IPS SQL DB IPS APP DS SQL DB NW DB2 DB NW APP APP 8.2 File Server 1+0 SEL 11 SEL 11 SEL 11 W2K8 R2 W2K8 R2 W2K8 R2 SEL 11 SEL 11 W2K8 R2 SEL 11 8x264 8x260 4x16 2x16 4x8 8x8 4x16 4x16 8x48 4x16 Development Legacy IP: 123.45.67.123 123.45.67.890 123.45.67.890 123.45.67.123 123.45.67.890 123.45.67.890 123.45.67.123 123.45.67.890 123.45.67.890 123.45.67.890 Las Vegas NV AZURE Azure IP: 123.67.45.890 123.67.45.890 172.29.248.126 123.67.45.890 123.67.45.890 172.29.248.126 123.67.45.890 123.67.45.890 172.29.248.126 172.29.248.126 IP Table IP: N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A USDC03 USEAST2 Azure size m64s, 64c, 1TB m64s, 64c, 1TB D4 v3: 4c, 16GB E2 v3: 2c, 16GB F4: 4c, 8GB F4: 4c, 8GB D4 v3: 4c, 16GB D4 v3: 4c, 16GB E8 v3: 8c, 64GB D4 v3: 4c, 16GB HANA BW 7.4 BI BOBJ IPS BI BOBJ IPS BODS DS SLT SLT vlmbid001 vlmbic001 vwmbob001 vwmdsd001 vwmdsc001 vlmstd001 vlmstc001 End State MHN MHB MBO MDS MDS MLT MLT 1+0 HANA-DB BW CI IPS SQL DB DS SQL DB DS APP NW DB2 DB NW CI SEL 11 SEL 11 W2K8 R2 W2K8 R2 W2K8 R2 SEL 11 SEL 11 12x200 4x16 2x24 8x8 4x8 8x16 4x16 Maintenance Legacy IP: 123.45.67.123 123.45.67.890 123.45.67.890 123.45.67.123 123.45.67.890 123.45.67.890 123.45.67.123 Las Vegas NV AZURE Azure IP: 123.67.45.890 123.67.45.890 172.29.248.126 123.67.45.890 123.67.45.890 172.29.248.126 123.67.45.890 IP Table IP: N/A N/A N/A N/A N/A N/A N/A USDC03 USEAST2 Azure size m64s, 64c D4 v3: 4c, 16GB E4 v3: 4c, 32GB F4: 4c, 8GB F4: 4c, 8GB F8: 8c, 16GB D4 v3: 4c, 16GB HANA HANA HANA HANA HANA HANA BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BI BOBJ IPS BI BOBJ IPS BODS DS BODS DS SLT SLT SLT PORTAL PORTAL PORTAL IP Tables vllbid001 vllbid002 vllbid003 vllbid004 vllbid005 vllbid006 vllbic001 vllbia001 vllbia002 vllbia003 vllbia004 vllbia005 vllbia006 vwlbob001 vwlbob002 vwldsd001 vwldsc001 vllstd001 vllstc001 vllsta001 vllpoc001 vllpoa001 vllpoa002 cldspr0045 End State LHN LHN LHN LHN LHN LHN LHB LHB LHB LHB LHB LHB LHB LBO LBO LDS LDS LLT LLT LLT LHL LHL LHL IP TAB 5+0 Master Slave Slave Slave Slave Temp Slave BW CI BW APP1 BW APP2 BW APP3 BW APP4 BW APP5 BW APP6 IPS SQL DB IPS APP DS SQL DB DS APP SLT DB2 DB SLT APP1 SLT APP2 Portal CI Portal App1 Portal APP2 IP Table Srvr SEL 12 SEL 12 SEL 12 SEL 12 SEL 12 SEL 12 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 W2K8 R2 W2K8 R2 W2K8 R2 W2K8 R2 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 12 176x3072 176x3072 176x3072 176x3072 176x3072 176x3072 1X4 8X64 8X64 8X64 8X64 8X64 8X64 4x32 4x16 4x32 4x32 8x32 12x32 12x32 8x32 4x32 4x32 Performance/DR Legacy IP: 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 San Francisco CA AZURE Azure IP: 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 IP Table IP: 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 USDC02 USEAST Azure size s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB D1 v2: 1c, 3.5GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB D4 v3: 4c, 16GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB D8 v3: 8c, 32GB F16: 16c, 32GB F16: 16c, 32GB D8 v3: 8c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB HANA HANA HANA HANA HANA BW 7.4 BI BOBJ IPS BODS DS BODS DS PORTAL Tableau IP Tables End State vlqbid001 vlqbid002 vlqbid003 vlqbid004 vlqbid005 vlqbic001 vwqbob001 vwqdsd001 vwqdsc001 vlqpoc001 vwmtab001 clqspr005 5+0 QHN QHN QHN QHN QHN QHB QBO QDS QDS QHL QTB IP TAB Master Slave Slave NET NEW NET NEW BW APP IPS SQL DB DS SQL DB DS APP Ent Portal APP 8.2 IP Table Srvr SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 W2K8 R2 W2K8 R2 W2K8 R2 SEL 11 W2K8 R2 SEL 12 40x1024 40x1024 40x1024 40x1024 40x1024 8x64 2x24 8x16 4x16 8x32 8x48 Q/A Legacy IP: 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 Las Vegas NV AZURE Azure IP: 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 IP Table IP: 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 USDC03 USEAST Azure size s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB E8 v3: 8c, 64GB E4 v3: 4c, 32GB D4 v3: 4c, 16GB F8: 8c, 16GB E8 v3: 8c, 64GB D8 v3: 8c, 32GB ? HANA HANA HANA HANA HANA BW 7.4 BI BOBJ IPS BI BOBJ IPS BODS DS BODS DS PORTAL SLT SLT IP Tables vltbid001 vltbid002 vltbid003 vltbid005 vltbidxxx vltbic001 vwtbob001 vwtbob002 vwtdsd001 vwtdsc001 vltpoc001 vlmstd001 vlmstc001 clrspr004 End State THN THN THN THN QHN THB TBO TBO TDS TDS THL MLT MLT IP TAB 5+0 Master Slave Slave Slave NET NEW BW APP IPS SQL DB IPS APP DS SQL DB DS APP Portal NW DB2 DB NW CI IP Table Srvr SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 W2K8 R2 W2K8 R2 W2K8 R2 W2K8 R2 SEL 11 SEL 11 SEL 11 SEL 12 40x1024 40x1024 40x1024 40x1024 40x1024 8x64 4x32 4x16 8x32 4x32 8x32 8x16 4x16 Dry-Run Legacy IP: 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 Las Vegas NV AZURE Azure IP: 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 IP Table IP: 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 USDC03 USEAST Azure size s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB E8 v3: 8c, 64GB E4 v3: 4c, 32GB D4 v3: 4c, 16GB D8 v3: 8c, 32GB E4 v3: 4c, 32GB D8 v3: 8c, 32GB F8: 8c, 16GB D4 v3: 4c, 16GB D4 v3: 4c, 16GB s192 s192 s192 s192 s192 s192 s192 s192 s384 HANA HANA HANA HANA HANA HANA HANA HANA HANA BW 7.4 BI BOBJ IPS BI BOBJ IPS BODS DS BODS DS PORTAL File Server IP Tables vlrbid001 vlrbid002 vlrbid003 vlrbid004 vlrbid005 vlrbid006 vlrbid007 vlrbid008 vlrbic001 vwrbob001 vwrbob002 vwrdsd001 vwrdsc001 vlrpoc001 vlrnfs001 CLRSPR001 RHN RHN RHN RHN RHN RHN RHN RHN RHN RHB RBO RBO RDS RDS RHL RHN IP TAB End State Master Slave Slave Slave Slave Slave Slave Slave Slave BW APP IPS SQL DB IPS APP DS SQL DB DS APP Portal File Server IP Table Srvr 5+0 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 W2K8 R2 W2K8 R2 W2K8 R2 W2K8 R2 SEL 11 W2K8 R2 SEL 12 60x1024 60x1024 60x1024 60x1024 60x1024 60x1024 60x1024 60x1024 60x1024 8x64 4x32 4x16 8x16 4x16 8x32 4x16 Pre-Prod Legacy IP: 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 Las Vegas NV AZURE Azure IP: 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 IP Table IP: 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 USDC03 USEAST Azure size s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB E8 v3: 8c, 64GB E4 v3: 4c, 32GB D4 v3: 4c, 16GB F8: 8c, 16GB D4 v3: 4c, 16GB D8 v3: 8c, 32GB D8 v3: 8c, 32GB D8 v3: 8c, 32GB HANA HANA HANA HANA HANA HANA HANA BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BI BOBJ IPS BI BOBJ IPS BODS DS BODS DS BODS TS BODS FS SLT SLT SLT PORTAL PORTAL PORTAL Tableau Tableau Tableau Tableau Tableau WEB DSP vlpbid001 vlpbid002 vlpbid003 vlpbid004 vlpbid005 vlpbid006 vlpbid007 vlpbic001 vlpbia001 vlpbia002 vlpbia003 vlpbia004 vlpbia005 vlpbia006 vwpbob001 vwpbob002 vwpdsd001 vwpdsc001 vwprds001 vlpfile001 vlpstd001 vlpsta001 vlpstc001 vlppoc001 vlppoa001 vlppoa002 vwptab001 vwptab002 vwptab003 vwptab004 vwptab005 vspdisp001 PHN PHN PHN PHN PHN PHN PHN PHB PHB PHB PHB PHB PHB PHB PBO PBO PDS PDS BODS BODS PLT PLT PLT PHL PHL PHL PTB TTB TTB TTB TTB WEB DSP End State Master Slave Slave Slave Slave Slave Not-in use BW CI BW APP1 BW APP2 BW APP3 BW APP4 BW APP5 BW APP6 IPS SQL DB IPS APP DS SQL DB DS APP Term server File Server SLT DB2 DB SLT APP1 SLT APP2 Portal CI Portal App1 Portal APP2 Tableau Primary Backup Worker 1 Worker 2 Web Dispatch 5+1 SEL 12 SEL 12 SEL 12 SEL 12 SEL 12 SEL 12 SEL 12 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 W2K8 R2 W2K8 R2 W2K8 R2 W2K8 R2 W2K8 R2 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 W2K12 R2 W2K12 R2 W2K12 R2 W2K12 R2 W2K12 R2 W2K8 R2 6+1 176x3072 176x3072 176x3072 176x3072 176x3072 176x3072 176x3072 1X4 8X64 8X64 8X64 8X64 8X64 8X64 4x32 4x16 4x32 4x32 4x16 4x32 8x32 12x32 12x32 8x32 4x32 4x32 8x64 4x8 4x8 8x64 8x64 2x8 Production Legacy IP: 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 Las Vegas AZURE Azure IP: 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 IP Table IP: 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 USDC03 USEAST Azure size s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB D1 v2: 1c, 3.5GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E4 v3: 4c, 32GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E4 v3: 4c, 32GB D4 v3: 4c, 16GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB D8 v3: 8c, 32GB F16: 16c, 32GB F16: 16c, 32GB D8 v3: 8c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB D2 v3: 2c, 8GB HANA HANA HANA HANA HANA HANA HANA BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BW 7.4 BI BOBJ IPS BI BOBJ IPS BODS DS BODS DS BODS TS BODS FS SLT SLT SLT PORTAL PORTAL PORTAL Tableau Tableau Tableau Tableau Tableau WEB DSP vlpbid001 vlpbid002 vlpbid003 vlpbid004 vlpbid005 vlpbid006 vlpbid007 vlpbic001 vlpbia001 vlpbia002 vlpbia003 vlpbia004 vlpbia005 vlpbia006 vwpbob001 vwpbob002 vwpdsd001 vwpdsc001 vwprds001 vlpfile001 vlpstd001 vlpsta001 vlpstc001 vlppoc001 vlppoa001 vlppoa002 vwptab001 vwptab002 vwptab003 vwptab004 vwptab005 vspdisp001 PHN PHN PHN PHN PHN PHN PHN PHB PHB PHB PHB PHB PHB PHB PBO PBO PDS PDS BODS BODS PLT PLT PLT PHL PHL PHL PTB TTB TTB TTB TTB WEB DSP End State Master Slave Slave Slave Slave Slave Not-in use BW CI BW APP1 BW APP2 BW APP3 BW APP4 BW APP5 BW APP6 IPS SQL DB IPS APP DS SQL DB DS APP Term server File Server SLT DB2 DB SLT APP1 SLT APP2 Portal CI Portal App1 Portal APP2 Tableau Primary Backup Worker 1 Worker 2 Web Dispatch 5+1 SEL 12 SEL 12 SEL 12 SEL 12 SEL 12 SEL 12 SEL 12 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 W2K8 R2 W2K8 R2 W2K8 R2 W2K8 R2 W2K8 R2 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 SEL 11 W2K12 R2 W2K12 R2 W2K12 R2 W2K12 R2 W2K12 R2 W2K8 R2 6+1 176x3072 176x3072 176x3072 176x3072 176x3072 176x3072 176x3072 1X4 8X64 8X64 8X64 8X64 8X64 8X64 4x32 4x16 4x32 4x32 4x16 4x32 8x32 12x32 12x32 8x32 4x32 4x32 8x64 4x8 4x8 8x64 8x64 2x8 Legacy IP: 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 123.45.67.123 Azure Azure IP: 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 123.67.45.890 IP Table IP: 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 123.89.45.670 USWEST Azure size s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB s384, 384c, 4TB D1 v2: 1c, 3.5GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E4 v3: 4c, 32GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E4 v3: 4c, 32GB D4 v3: 4c, 16GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB D8 v3: 8c, 32GB F16: 16c, 32GB F16: 16c, 32GB D8 v3: 8c, 32GB E4 v3: 4c, 32GB E4 v3: 4c, 32GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB E8 v3: 8c, 64GB D2 v3: 2c, 8GB D/R T M L S D Q R P
- 13. Visualizing the As-is and the To-Be Legacy Hosting Provider
- 14. CONA HANA BACKUP SOLUTION One of the biggest challenges HANA has in Azure is a cost effective efficient backup solution HLI Instance NFS attached storage Backup done locally Retention= 3 days Time= 12 TB in 3hrs. Backup done via AzCopy Retention= 30 days Time= 12 TB in 4hrs. 10 Gbit/s connection to Blob storage via AzCopy Azure Blob storage 10 01 Read Access Global Redundant Storage Automated Region–to-Region copy 10 01 RA-GRS storage Backup done via AzCopy Retention= 30 days Time= Near Real- time Region 1 Region 2
- 15. Network Validation Every Step of the Way Azure Express Route Azure Express Route Azure US EAST 2 Azure US WEST Azure US EAST L HANA Phy Azure US EAST Co-Lo HANA Phy HANA Phy HANA Phy R Q T P HANA Phy D/R HANA Phy Azure US WEST Co-Lo VMs VMs VMs VMs VMs S VMs M VMs D VMs CONA DC 1 S - D - Q T - L CONA DC 2 M - R P Hosting DC 2 L D/R San Francisco B B B B B B B B B B B B AT&T L3 Internet Atlanta VMs Hosting DC 1 S-D-Q-M T-R-P Las Vegas Sterling Validate Network performance 1 GB 1 GB 1 GB 1 GB
- 16. SAP Maxattension • What is Maxattention • SAP’s premier support plan, with an embedded support team, tailored for customers specific requirements • Develop detailed concept of the technical infrastructure and data migration • Performance tuning to achieve high performance and high scalability • Analyze functional design and architecture and validate functional gaps • Impact on CONA Migrations • Improved migration time by following suggested changes from SAP’s Maxattention reports • Detection of HANA AO and HANA Studio connectivity issue • Over-all improved performance on HANA run jobs • Impact on Partnerships • Enable troubleshooting and problem solving from all directions • CONA from the business side • SAP from the functional side • Microsoft from the infrastructure side
- 17. Summary of Insights • Validation Insights • Validate and document the landscape and licensing – create a single common view • Validate the order of the migrations – start small and simple • Validate the data pre & post migration – document every migration along the way • Microsoft/Infrastructure Insights • Understand network end-to-end, use simple tools • Always review backup architecture design – changes to project may have direct impacts • Continually Confirm sizing and configuration – Sizing never ends • Performance – looks everywhere, network, disk performance, CPU and Memory – changed to Premium disks on all Azure instances • Physical HANA HSR migrations must be like for like in Master and slave node counts • SAP Insights • Engage SAP’s Maxattention as early as possible • Include Maxattention in all test results Data and Infrastructure • Select most frequently used transactions as well as the longest running transactions • Compare every migration against the next – timings, issues and all solutions
- 18. CokeOne – OneTeam NA “We have learned a lot from the work with CONA and Capgemini. This goes beyond deploying SAP HANA on large bare metal machines to meet their mission critical requirements. We also worked closely with the customer and Capgemini to improve the D/R configuration, accelerate the network connectivity, and resolve concerns around backup latency.” Corey Sanders - Corporate Vice President for Azure Compute Microsoft
Editor's Notes
- What is not mentioned in a bullet is what separates CONA from most other companies, that is the their customers (the bottlers) are also on the Board of directors So keeping your customers happy takes on a whole new meaning and presents additional pressure Due to multiple challenges the May date was move to July
- Why did Microsoft Choose to Partner why Capgemini ? There is a long history with Microsoft. We are 1 of their top Global SI partners Microsoft and Capgemini partnership began over 20 years ago (22 to be exact) Another reason MS partnered with Cap is trust (go to next slide)
- TRUST is the reason why Microsoft chose Capgemini to Partner with. Microsoft knew Capgemini was a leader in the SAP market space with years of SAP delivery experience around the world Both Gartner and Forrester have named Capgemini as a leader in SAP
- There were Multiple datacenters involved in the Legacy CONA solution The Analytics layer being hosted in the Legacy Hosting providers DC’s (Las Vegas and San Francisco) The Analytics Engine included BW, HANA, BOBJ, BODS, and Tableau The Frontend systems that the Analytics systems connected to were hosted in CONA’s datacenters Atlanta GA, and Sterling VA The Frontend systems included ECC, SRM, CRM, HRM, SCM, PI and other bolt-on’s Also a thing to note is that HANA connection also used AO and HANA Studio Yes there are 8 landscapes listed, but we separated L and D/R into their own instances creating the 9th
- The two migration tools that were used at CONA were SAP’s HSR And Microsoft’s ASR. The main HANA migrations were a 3 step process site A to B then B to C This method enable the team to complete multiple iterations of testing with little or no impact to the business HSR can difficult over a network that has high utilization, however the HANA stand-by node did help overcome some replication failures. Microsoft’s ASR was simple to set-up, simple to configure and simple to run. It was a very stable way to migrate the non-HANA workloads. It was a good choice for the bolt-on’s.
- The Migration involved 5 datacenters 1) Decision on where to put the instances – which Azure DC would they go into 2) Which instances were to be virtualized, and which instances were going bare metal (Physical node count) ASR used for migration of all non-HANA instances – going across the internet connection. HSR was used to Migrate all HANA Physical instances There were multiple paths to move the data to get to the end point. Depending on landscape origination Some migration went: Some migration went from Datacenter A to datacenter E Others went from A to B, B to C then C to D Lesson learned: Start small, take plenty of notes, test network to Azure, test network from Bottlers In the case where you have more than the normal 3 landscapes (Dev, QA, Prod) Select landscape order wisely Ensure sure there is coordination of Business calendar and Migration Calendar (CONA continue to have upgrade and updates to software through the migration schedule The build of D/R was also used as a D/R test.
- A big challenge for HANA 1.0 migration using HSR is in the data validation. (there is no automated way to confirm data integrity post migration) CONA was very rigorous validating pre-migration, once interfaces were stopped and landscape was quiet and checkpoint or baseline was establish. Post migration, after the Basis team complete the technical checks. The systems were handover to the testing teams to ensure data integrity.
- Every project starts with an SAP Inventory. System and Bolt. CPU and Memory, OS and datacenter By taking that excel inventory and changing into “SIM” (System Instance Map) was a very powerful tool at our disposal *after animation” This view provided Leadership teams for CONA, Microsoft, SAP, and Capgemini the 1 page image of the entire project This gave us which datacenters came from and where they where going The HANA node config Then the details on every system within a landscape, all on the same page *Actual IP address will be removed/changed
- Having completed the diagram has shown on the previous slide – creating the As-to-Be is no longer a challenge Identifying new instances, dropping or reducing instances, identifying gaps *Lesson Learned: You cannot HSR into a small footprint. Has to be like-for like (i.e. Prod6+1 to 5+1 cant be done) This method was also helpful in License counts and explaining why counts changed
- Biggest challenge the team faced was how do you backup large HANA DB’s in an acceptable timeframe We looked at multiple options, from creating a Ceph Clusters, customized tools, to utilizing Azure backup What we found was using a combination of NFS storage, Blog Storage and RA-GRS Storage Backup done locally & keep for 3 days (approx. 4 TB per hour) Then ships to Azure Blob storage using AZCOPY this method was the fastest and lowest price solution we could find (approx. 3TB per hour) The last level of protection was a region to region copy the is setup in near real-time also with the same retention policy Because if a roll off date occurs in region 1 – the same will happen in region 2 – at the same time
- Validating the Network performance is key. Every connection needs to provide the maximum bandwidth as provided Understand who actually manages the edge routers and who can actually check performance setting is key CONA had multiple vendors managing multiple network routes. CONA did not manage the bottlers network, some were very clean and some were not We needed to follow SAP & HANA transactions end to end to find the bottlenecks or pass the information on to SAP Maxattention We had Bottlers that need help why the same transaction was taking x-times longer then another bottler Story: One morning Microsoft and Capgemini resources coming out of elevator to come into the office. CONA Program director David South walking into elevator asking if we care to join him to visit the Atlanta bottler (happens to be one of the larger bottlers) who was experiencing performance problems in testing. Microsoft, Capgemini and CONA, spent all day stepping through HANA transactions whiteboarding the entire network landscape to help pin point where the issues where occurring. This was a normal day on the project.
- Every day CokeOne North American with their Partners came together to create OneTeam North America Everyday was a white-boarding session finding new solutions. There was no play book for an Azure project of this magnitude This team had to write that book and “Team” was always defined as CONA-Microsoft-SAP and CAPGEMINI – it was never 4 different teams *NOTE bottom left photo is CONA’s COE and Capgemini’s Daniel Stiegler looking at network traffic the night of go-live looking at bottlenecks