Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few. Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO. Agenda included: - Pizza and Networking - Joe Caserta, President, Caserta Concepts - Why are we here? - Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration - Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing - James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service - Q&A, Networking For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/

1. Datameer
and
Azure
Analyze Big Data
on the Cloud
Big Data Warehousing:
May 10, 2016
Today’s Topic:
Big Data Analytics on the Cloud
Presented by:
Presenters:
Joe Caserta
President
Caserta Concepts
Nikhil Kumar
Sr. Solutions Engineer
Datameer
Stefan Groschupf
CEO & Chairman
Datameer
James Serra
Data Platform Solution
Architect
Microsoft

2. Agenda
6:30 Networking
Grab some food and drink... Make some friends.
6:50 Joe Caserta
President, Caserta Concepts
Welcome + Intro to BDW Meetup
About the Meetup. Why MDM needs Graph now.
7:15 Nikhil Kumar
Sr. Solutions Engineer, Datameer
Solution use cases and technical demonstration
7:45 Stefan Groschupf
CEO & Chairman, Datameer
The evolving Hadoop-based analytics trends and
the role of cloud computing
8:15 James Serra
Data Platform Solution Architect, Microsoft
Benefits of the Azure Cloud Service
8:45 Q&A Ask Questions, Share your experience

3. About Caserta Concepts
• Consulting Data Innovation and Modern Data Engineering
• Award-winning company
• Internationally recognized work force
• Strategy, Architecture, Implementation, Governance
• Innovation Partner
• Strategic Consulting
• Advanced Architecture
• Build & Deploy
• Leader in Enterprise Data Solutions
• Big Data Analytics
• Data Warehousing
• Business Intelligence
• Data Science
• Cloud Computing
• Data Governance
Amazon Best Sellers
Most popular products based on sales.
Updated hourly.

4. Partners

5. Awards & Recognition

6. Caserta Innovation Lab (CIL)
• Internal laboratory established to test & develop solution concepts and ideas
• Used to accelerate client projects
• Examples:
• Search (SOLR) based BI
• Big Data Governance Toolkit / Data Quality Sub-System
• Text Analytics on Social Network Data
• Continuous Integration / End-to-end streaming
• Recommendation Engine Optimization
• MDM / Relationship Intelligence / Spark Graph

7. Speak with us about our open positions: leslie@casertaconcepts.com
We’re Hiring!
Does this word cloud excite you?
Spark
Big Data Architect
NoSQL
Cloud Computing

10. We’re Experiencing a Paradigm Shift
OLD WAY:
• Structure  Ingest  Analyze
• Fixed Capacity
• Monolith
NEW WAY:
• Ingest  Analyze  Structure
• Dynamic Capacity
• Ecosystem
RECIPE:
• Cloud
• Data Lake
• Polyglot Warehouse

11. Move to the Cloud
Existing On-Premise Solution
• Challenges with operations of Hadoop servers in Data Center
• Increasing infrastructure complexity
• Keeping up with data growth
Cloud Advantages
• Reduced upfront capital investment
• Faster speed to value
• Elasticity “Those that go out and buy expensive
infrastructure find that the problem scope
and domain shift really quickly. By the time
they get around to answering the original
question, the business has moved on.” -
Matt Wood, AWS

12. Cloud Market Share

13. Cost savings of dynamic capacity

14. Elasticity not only saves money

15. Essentially, Servers Suck
But more importantly think Infrastructure
as code
• Your servers should be API calls
• Use stateless processes
• Make all resources ephemeral
• Make everything scalable and elastic!

16. Hadoop on the the Cloud Makes Sense
Hadoop on Demand
•Bootstrap whatever processing engine makes sense
• Programmatically estimate instance type and cluster size

17. You May Need Some Persistent Servers
If at all possible they should be inherently scalable,
distributed, and elastic

18. Ingest Raw
Data
Organize, Define,
Complete
Munging, Blending
Machine Learning
Data Quality and Monitoring
Metadata, ILM , Security
Data Catalog
Data Integration
Fully Governed ( trusted)
Arbitrary/Ad-hoc Queries
and Reporting
Usage Pattern Data Governance
Metadata, ILM,
Security
The Big Data Pyramid

19. Big Data Pyramid on Azure

20. Thank You / Next up
Joe Caserta
President, Caserta Concepts
joe@casertaconcepts.com
@joe_Caserta
Coming up: