migrating paraccel/matrix workloads to the...

Migrating ParAccel/Matrix

Workloads to the Cloud

Topics

• Introductions

• The Situation

• Cloud Data Warehousing: Considerations and Case Studies

• Data Warehousing as a Service

• Q&A

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

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

Speakers

Paul Wolmering Cazena Solution Architecture Lead

Lokesh Khosla Principal and Partner, Magnus Data

The Situation For ParAccel/Matrix Shops

An unexpected, unbudgeted challenge needing fast resolution

Actian Support Ends March 30

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

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

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

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

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

Data Warehousing as a Service

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

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)

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

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

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

Next Steps and Q&A

Next Steps

• Learn more at cazena.com

• Contact us for a free migration assessment

• Validate with a one-month pilot