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Overcoming Data Gravity in Multi-Cloud Enterprise Architectures

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Enterprise architectures never sleep because cloud-first strategies must also become multi-cloud-first strategies. Public cloud providers such as Microsoft Azure are providing compelling services and pricing. And, most enterprises now consider their own datacenter a private cloud.

This is not a one-cloud playing field and enterprise architects must develop strategies, standards, and policies about how their data is being used, moved, and created across multiple cloud infrastructures.

Join Pivotal’s Jag Mirani and Mike Stolz along with guest, Forrester Vice President and Principal Analyst, Mike Gualtieri, as they examine the trends driving multi-cloud adoption and more importantly how to architect technical solutions to make data free to roam among them safely.

Speakers:
Mike Gualtieri, VP, PRINCIPAL ANALYST, Forrester
Jag Mirani, Product Marketing, Data Services, Pivotal
Mike Stolz, Product Lead, GemFire, Pivotal

Publicado en: Tecnología
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Overcoming Data Gravity in Multi-Cloud Enterprise Architectures

  1. 1. © Copyright 2017 Pivotal Software, Inc. All rights Reserved. Version 1.0 April 3rd, 2018 Mike Gualtieri - VP, Principal Analyst, Forrester Research Mike Stolz - Lead Product Manager, Pivotal Jagdish Mirani - Principal Product Marketing Manager, Pivotal Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
  2. 2. Cover w/ Image Topics ■  What’s driving multi-cloud? ■  What is the data challenge? ■  How does design thinking change in a multi-cloud architecture? ■  What are the architectural/imperatives for multi-cloud? ■  What are some real-world multi-cloud use cases? ■  Key Pivotal solution components
  3. 3. What’s driving multi-cloud? Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
  4. 4. Business Drivers for Multi-cloud ●  Avoid vendor lock-in ●  Meet quality of service requirements (online availability and response time) using multiple distributed data centers for geographic proximity to customers and consumers ●  Organizational boundaries (ex: align the tech stack and IT operations by business unit) ●  Risk diversification / mitigation ●  Data sovereignty, laws, regulations ●  Leverage cloud provider strengths and innovation
  5. 5. 5© 2017 FORRESTER. REPRODUCTION PROHIBITED. Public Cloud big data services are at the top of the list, followed by security analytics. Base: 2106 global data and analytics technology decision makers Source: Forrester Data Global Business Technographics® Data And Analytics Survey, 2017
  6. 6. What are the Data Challenges? Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
  7. 7. Data Gravity in the Enterprise ●  New data is being generated in the cloud, outside the walls of the enterprise ●  Data sources are becoming more diverse ●  Network bandwidth and latency ●  Volume of data is still exploding ●  Data distribution vs. consistency ●  Data governance, laws, security, provenance ●  Metadata creation and accumulation ●  Failure states of the system And it’s not all internet data ...
  8. 8. Internet data Enterprise data 20% 80% Enterprise data has huge, differentiated value.
  9. 9. © 2013 Forrester Research, Inc. Reproduction Prohibited 9 Enterprise data is rich with differentiation ›  Customer transaction data ›  Supplier transaction data ›  Contract data ›  Inventory data ›  Supply chain data ›  Product/service data ›  Website data ›  ERP and manufacturing data ›  R&D data ›  Sales and CRM data ›  Marketing/advertising data ›  Human resources data ›  Finance/accounting data
  10. 10. 11001001101100 010010011 010011001101 0100 Customerdata Transactions Applications Logs Enterprises has dozens, hundreds, and thousands of data sources.
  11. 11. 11© 2017 FORRESTER. REPRODUCTION PROHIBITED. Data Lake Architectures are Prevalent, but not the Answer for Multi- Cloud Base: 2106 global data and analytics professionals Source: Forrester Data Global Business Technographics® Data And Analytics Survey, 2017
  12. 12. 12© 2017 FORRESTER. REPRODUCTION PROHIBITED. Real-time insights Operational insights Performance insights Strategic insights Insight: Shopping for furniture Action: Recommend cleaning supplies Insight: Profit lower than goal Action: Optimize price Insight: Demand forecast strong Action: Increase inventory Insight: Furniture demand high Action: Expand product line TimetoAct Perishability Sub-second to seconds Seconds to hours Days to weeks Weeks to years Sub-second to seconds Seconds to hours Hours to weeks Weeks to years Enterprises must act on a range of perishable insights to get value from data and analytics
  13. 13. 13© Copyright 2013 Pivotal. All rights reserved. 13© Copyright 2016 Pivotal. All rights reserved. DataTemperatureWarmHot GemFire/Greenplum Connector Transactional data Write behind Analytical parameters to cache GemFire and GPDB - Big Data meets Fast Data Seamlessly share data between GemFire and Greenplum Bi-directional direct connection between GemFire CacheServers and Greenplum Segment Servers
  14. 14. How does design thinking change in a multi- cloud architecture? Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
  15. 15. ●  Weigh the cost-benefit of multicloud portability for each application; prioritize accordingly. Segment applications based on primary need: redundancy vs. functional distribution. ●  Avoid the factors that contribute to lock-in ●  Design for cloud native environments, favoring modular design with contextual isolation and statelessness (12-factor apps) ●  Map the workload requirements for each application (or components of each application) to the cloud provider that provides the best-of-breed services ●  Assess the culture and appetite for formalizing a multi-cloud strategy Application Design Thinking for Multi-Cloud
  16. 16. What are some real-world multi-cloud use cases? Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
  17. 17. Common Use Cases for Multi-cloud 1.  Disaster Recovery 2.  Public cloud as an extension of the datacenter 3.  Active/Active WAN Replication across Foundations, across Clouds
  18. 18. Disaster Recovery (DR) Restoration Pattern ●  Recovery site brought online as needed ●  Multiple foundations can share a recovery site ●  Recovery site can reside on-premises, in a co-location facility, or the public cloud ●  Recovery site includes an operational foundation, with only the most critical apps ●  Primary site’s data is replicated to recovery site via Pivotal Cloud Cache’s WAN replication ●  Can be used in conjunction with other methods
  19. 19. Public Cloud as Extension of the Datacenter ●  For short periods of time to offload spikes in traffic ●  Often in support of major business events (product launch, marketing campaign, or surge in seasonal traffic) ●  Pay for extra resources only when they are needed ●  Requires a high-speed, dedicated connection ●  WAN replication propagates data changes in both directions
  20. 20. Active - Active Deployment ●  Global traffic manager directs traffic from clients ●  Users can be routed to the PCF foundation physically closest to them ●  Other routing policies: round-robin, weight-based, latency-based, geolocation, and session affinity (cookie-based or client IP) ●  PCC Wan replication propagates data changes in both directions WAN Replication
  21. 21. What are the Architectural Imperatives for Multi-Cloud? Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
  22. 22. 22© 2017 FORRESTER. REPRODUCTION PROHIBITED. Companies are looking to improve data quality and consistency Base: 3378 global data and analytics professionals Source: Forrester Data Global Business Technographics® Data And Analytics Survey, 2017
  23. 23. Conflict Resolution in Active/Active Setup 23 ●  PCC automatically detects conflicts and retains the latest data ○  Local timestamps and conflict detection algorithms ●  Can use custom code for conflict resolution ●  Alternative: design the system to avoid conflicts ...
  24. 24. Design Principles for Active-Active Patterns ●  Exchange pattern ●  Realm manager pattern ●  Follow-the-sun pattern ●  Inventory allocation pattern ●  Apology based computing
  25. 25. Multi-site Active-Active Design Patterns 1. Exchange Pattern NYSE LSE LSE TSE NYSE, TSE Read--only LSE, TSE Read--only NYSE, LSE Read--only Client connects to all exchanges it needs for writing, uses local copy for read only access.
  26. 26. Multi-site Active-Active Design Patterns 2. The "Realm Manager" Pattern: Use the “Command” pattern to request that an action be performed on your behalf. Request gets forwarded to all distributed systems but only the one with the right permission actually takes the action. Read Only For This Customer Read Only For This Customer Write Permission For This Customer
  27. 27. Multi-Site Active-Active Design Patterns 3. Follow the Sun Pattern: This is the "Global book" pattern common in Financial Services. The token is here
  28. 28. Multi-Site Active-Active Design Patterns 4. Inventory Allocation Pattern: This pattern is commonly used when there are multiple trading venues and selling short is not allowed. Partial Inventory Partial Inventory Partial Inventory Partial Inventory
  29. 29. Multi-Site Active-Active Design Patterns 5. Apology based computing: This is the pattern that Max Feingold refers to when he says: “At global scale, getting the truth is really really expensive.”
  30. 30. Key Pivotal Solution Components Overcoming Data Gravity in Multi-Cloud Enterprise Architectures
  31. 31. Pivotal Cloud Foundry: Multi-Cloud with BOSH + CPI
  32. 32. Pivotal Cloud Cache ●  Cross DC data sharing ●  Dev can push server-side code to save data to backing store ●  Support persistence w/Regions ●  Support multi-WAN connected cluster
  33. 33. Pivotal Cloud Foundry Marketplace •  Easy accessibility through Marketplace •  Instant Provisioning •  Bind to apps through easy to use interface •  Lifecycle management •  Common access control and audit trails across services MySQL New Relic Single Sign- On RabbitMQ Config Server Service Directory Circuit Breaker Signal Sciences Crunchy PostgreSQL AND MORE Services Marketplace Pivotal Cloud Cache Dynatrace Extending the Pivotal Cloud Foundry Platform for Microservices Architectures
  34. 34. Multi-Cloud is Inevitable ●  Enables flexibility and choice ○  Go in with a well considered multicloud strategy and plan, rather than ad-hoc ●  Map cost-benefit back to business drivers: business continuity, portability and the absence of lock-in, opportunistic use case placement and future-proofing, ...
  35. 35. Summary: Assessing Your Choices ●  Option 1: Build directly on top of an IaaS ○  Prepare (cross train) staff on all identified cloud providers ○  Choose native management tools and operational processes for each cloud ○  Maintain diligence towards avoiding lock-in ●  Option 2: Build on top of a PaaS like Pivotal Cloud Foundry ○  Platform, tools, and methodology that mask the differences between IaaS ○  Continuous and rapid provisioning of apps and services ○  Automated ‘day 2’ operations
  36. 36. © Copyright 2017 Pivotal Software, Inc. All rights Reserved. Version 1.0 April 3rd, 2018 Mike Gualtieri - VP, Principal Analyst, Forrester Research Mike Stolz - Lead Product Manager, Pivotal Jagdish Mirani - Principal Product Marketing Manager, Pivotal Overcoming Data Gravity in Multi-Cloud Enterprise Architectures

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