migrating paraccel/matrix workloads to the...
TRANSCRIPT
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Migrating ParAccel/Matrix
Workloads to the Cloud
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Topics
• Introductions
• The Situation
• Cloud Data Warehousing: Considerations and Case Studies
• Data Warehousing as a Service
• Q&A
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About Cazena
Enterprise Heritage
Founded by former
Netezza leaders
Created enterprise data
warehousing appliance
category, 800+ customers
Started Cazena in 2013,
launched 2015
Strong Backing Founding Vision
Big Data On Demand
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About MagnusData
Enterprise
Big Data Experts
Served Customers in
Wealth Management,
Digital Marketing, and
Social Media
MIT
Mission Vision
Big Data Cloud Services
Data That Creates Value
-
We Believe in Making
Technology an Asset
For
Business Partners
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Speakers
Paul Wolmering Cazena Solution Architecture Lead
Lokesh Khosla Principal and Partner, Magnus Data
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The Situation For ParAccel/Matrix Shops
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An unexpected, unbudgeted challenge needing fast resolution
Actian Support Ends March 30
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What do Organizations Need to Do?
• Equal or better performance
• Minimal impact on infrastructure
• No impact on users
• Cost-effective
• Safe and supported
Required: move to a platform that minimizes cost and risk
Possible: also lay a foundation for evolution and innovation
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What Options Exist?
Pros Cons
Stay on ParAccel/Matrix • Cost-effective
• Leverage existing skills
• No disruption
• Risk and lack of sleep
• Lack of effective support
• Lack of investment
Move to new EDW on-
prem
• Leverage existing skills
• Too slow
• Expensive
New Hadoop • Cost-effective for some
workloads
• Not be suitable for all
workloads
• Not something to be rushed
Migrate DW to Cloud • Cost-effective
• Speed
• Flexibility
• New skills, development
required, or a partner
• Security and other concerns
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On-Premise vs. Cloud Based Infrastructure
On-Premise Infrastructure Cloud Based Infrastructure
• Costly
• Hinders innovation
• Lacks support for
business growth
• Requires backups
• Business contingency
plans in case of
disasters
• Speed
• Agility
• Elasticity
• Focus
• Lower TCO
• Secure
• Shared responsibility
• Global access
• Innovation friendly
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HPC for Big Data on AWS
• Customer challenge:
– Unable to scale use of AWS tools to support business growth
• Magnus solution:
– Helped with architecting an HPC Big Data system using AWS tools
• Customer benefits:
– Faster than the 5 second SLA for analyst queries
– Aligned with IT governance standards
– Improved security
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Netezza Migration to AWS
• Customer challenge:
– Existing system lacked flexibility
– Incurring huge cost
• Magnus solution:
– Moved Netezza workloads to Hadoop on AWS
• Customer benefits:
– Reduced costs 80%
– Exceeded performance standards
– Increased flexibility
Marketing Analytics Company
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Data Warehousing as a Service
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Requirements for Data Warehousing in the Cloud
Security and Compliance Implement cloud security controls, compliance, governance
Production Operations, Monitoring & Support Develop processes for monitoring, maintenance, upgrades, support
Data Movement Deploy tools and process to securely move data to/from the cloud
Analytic Platform and Infrastructure Provision and deploy multi-cloud multi-database support
Enterprise Integration Securely connect cloud to existing enterprise tools, systems
Production development and operations requirements
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Workload Engine
Benchmarking
Cloud Infrastructure
Workload
Engines
Security, Compliance
SLA Optimization,
Operations
3
Cazena
Gateway Data
Movers
Cloud Sockets
How Cazena Works
Data
Sources,
ETL & Warehouses
BI /
Analytics
Tools & Apps
External or
Cloud
Data Sources
Partner or
Customer
Analytics Tools
Enterprise Datacenter(s)
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All-in-One Service
“As a Service” Includes:
• Cazena Gateway
• Workload Engine Licenses
• Cloud Infrastructure Fees
• Operations and Upgrades
• Monitoring and Security
• SLA Optimization
• Fast Managed Setup
• 24 X 7 Support – One Call
Data Lake
as a Service
Data Mart
as a Service
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How Cazena Fits In
Data Mart/Warehouse (MPP SQL)
as a Service
Futures in evaluation by
R&D team: Search, etc.
Data Movement, Integration with Data Flow
Operations, Monitoring, Backup/Recovery
Security, Governance, Encryption
Data Lake (Hadoop or Spark)
as a Service
Best-of-Breed Workload Engines, Benchmarking
Infrastructure (Amazon Web Services, Microsoft Azure)
Data Science / Analytics BI / Visualization Applications
Data Architecture | ETL/MDM | Modeling
Cazena
Big Data
as a
Service
Enterprise/
Partner
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Cazena: Pre-Built and Production-Ready
Do it Yourself
~200+ tasks, many components,
Multiple vendors, interfaces, SKUs
Must integrate with enterprise
3 – 12+ month deployment
Multiple support organizations
Ongoing R&D and optimization
Cazena Big Data as a Service
One platform, one interface
One vendor, one interface, one SKU
Easy enterprise integration
2 week deployment
Single support contact (24 x 7)
Optimized, future proof
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Next Steps and Q&A
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Next Steps
• Learn more at cazena.com
• Contact us for a free migration assessment
• Validate with a one-month pilot
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