View the recording: https://content.pivotal.io/webinars/the-data-warehouse-in-the-age-of-digital-transformation?utm_source-pivotalwebsite&utm-medium-email-link&utm-campaign=datawarehouse-hiredbrains-q117
In the past years of Big Data and digital transformation “euphoria”, Hadoop and Spark received most of the attention as platforms for large-scale data management and analytics. Data warehouses based on relational database technology, for a variety of reasons, came under scrutiny as perhaps no longer needed.
However, if there is anything users have learned recently it’s that the mission of data warehouses is as vital as ever. Cost and operational deficiencies can be overcome with a combination of cloud computing and open source software, and by leveraging the same economics of traditional big data projects - scale-up and scale-out at commodity pricing.
In this webinar, Neil Raden from Hired Brains Research makes the case that an evolved data warehouse implementation continues to play a vital role in the enterprise, providing unique business value that actually aids digital transformation. Attendees will learn:
- How the role of the data warehouse has evolved over time
- Why Hadoop and Spark are not replacements for the data warehouse
- How the data warehouse supports digital transformation initiatives
- Real-life examples of data warehousing in digital transformation scenarios
- Advice and best practices for evolving your own data warehouse practice
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Pivotal Data Warehouse in the Age of Digital Transformation
1. The Data Warehouse in the Age of
Digital Transformation
Guest Presenter: Neil Raden, Founder, Hired Brains Research
Overview and Comments: Jeff Kelly, Product Marketing, Pivotal
3. “Digital transformation involves making fast
and profound change in business activities,
processes, competencies, and models to
strategically leverage digital technologies
and their wide-reaching impacts.”
8. Founder, Hired Brains Research
Today’s Speaker: Neil Raden
- Founder & Principal Analyst at Hired Brains
Research
- Co-author of Smart (Enough) Systems
(Pearson Education, 2007)
- Advisory Board Member of the Boulder
Business Intelligence Brain Trust
9. Our Topics Today
1. Data warehouse and data warehousing are not the same thing
2. Data warehouses still relevant, provide needed capabilities not found
elsewhere
3. Carefully curated and reliable data, optimizers, load balancers and
massive scale
4. But data warehouses based in traditional relational databases have
not performed well
5. Big data technology has actually solved these drawbacks in scale
Copyright 2017 Neil Raden and Hired Brains Research LLC
11. Convergence is Here
1950 1960 1970 1980 1990 2000
Batch Reporting
CICS/OLTP
C/S OLTP
Y2K/ERP
4GL/PC/SS DW/BI
Convergence
2010
Operational BI
Composite Apps
BPM
Semantics
Decision
Automation
History of the Rift Between Operational and Analytical Processing
Copyright 2017 Neil Raden and Hired Brains Research LLC
12. Different Way to Visualize It
Copyright 2017 Neil Raden and Hired Brains Research LLC
13. No More Managing from Scarcity
Copyright 2017 Neil Raden and Hired Brains Research LLC
14. Digital Transformation & “Instatology”
• More work happens in
computers and networks
• How to stop for second to
see what’s going on
• The rise of Instatology. Analytical solutions, cant keep up with the
speed and volume of data generated by digital transformation.
Copyright 2017 Neil Raden and Hired Brains Research LLC
16. To Simplify the Difference
Hadoop Data Warehouse
How queried Batch Interactive
Database Schema Evolving Advanced
Query Optimizer None Powerful
Maximum data Petabytes Petabytes
Maximum processors Unlimited Unlimited
Cost Increasing Decreasing
Copyright 2017 Neil Raden and Hired Brains Research LLC
17. Virtual Data Warehouse
• Deployment agnostic including on premises, private and public cloud
• Open source
• Support any data locality (local disk, Hadoop, private and public cloud data.)
• Run new analytical workloads in-database including machine learning, geospatial, graph, text analytics, etc
• Ability to handle native data types such as spatial, time-series and/or text
• Query optimization for big data
• Complex query formation
• Massively parallel processing based on the model, not just sharding
• Workload management
• Load balancing
• Full ANSI SQL and beyond.
Copyright 2017 Neil Raden and Hired Brains Research LLC
18. “Evolved” Analytics: Demand Better Data Prep
• Abstraction of the end-to-end process
• Restful API’s
• Extreme performance
• Limitless types of data sources
• Extreme Agility
• Robust business modeling capabilities
• Statistical/quantitative modeling
“We think too small, like the frog
at the bottom of the well. He
thinks the sky is only as big as
the top of the well. If he
surfaced, he would have an
entirely different view.”
Anonymous
Copyright 2017 Neil Raden and Hired Brains Research LLC
19. Eero Saarinen
“Always design a thing by
considering it in its next
larger context – a chair in a
room, a room in a house, a
house in an environment, an
environment in a city plan.”
Copyright 2017 Neil Raden and Hired Brains Research LLC
22. What is Pivotal Greenplum?
• Open source data warehouse
• Massively parallel processing (MPP) architecture
• Built for diverse analytical use cases
• Available anywhere you need it – bare metal,
private cloud, public cloud, appliance
23. Resources and next steps
Download the companion white paper
Visit https://pivotal.io/pivotal-greenplum
Contact Pivotal at
https://pivotal.io/contact
24. Founder, Hired Brains Research
Contact Neil Raden
• Twitter: @NeilRaden
• Blog: http://hiredbrains.wordpress.com
• Website: http://www.hiredbrains.com
• Mail: nraden@hiredbrains.com