Slides from a recent Big Data Warehousing Meetup titled, Big Data Analytics with Microsoft. See Power Pivot/ Power Query/ Power View/ Power Maps and Azure Machine Learning be used to analyze Big Data. One challenge of dealing with Big Data project is to acquire both structured and instructed information in order to find the right correlation. During the event, we explained all the steps to build your model and enhance your existing data through Microsoft's Power BI. We had an in-depth discussion about the innovations built into the latest stack of Microsoft Business Intelligence, and practical tips from Technology Specialist’s from Microsoft. The session also featured demos to help you see the technology as an end-to-end solution. For more information, visit www.casertaconcepts.com

1. See Power Pivot
Power Query
Power View
Power Maps
and
Azure
Machine Learning
used to analyze
Big Data
Presenters:
Joe Caserta
President
Caserta Concepts
Laurent Banon
Technology Specialist
Microsoft
Rajesh Raghunathan
Technology Specialist
Microsoft
Big Data Warehousing:
September 17, 2014
Today’s Topic:
Big Data Analytics with Microsoft
Presented by:

2. Agenda
7:00 Networking
Grab some food and drink... Make some friends.
7:15 Joe Caserta
President
Caserta Concepts
Welcome + Intro
About the Meetup, about Caserta Concepts
Our vision for the future of Business Intelligence
7:35 Laurent Banon
Microsoft
Technology Speacialist
In-depth discussion about the innovations built
into the latest stack of Microsoft Business
Intelligence
8:00 Rajesh Raghunathan
Microsoft
Technology Specialist
Demonstration of Power BI to help you see the
technology as an end-to-end solution.
8:45 Q&A, More Networking
Tell us what you’re up to…

3. Joe Caserta Timeline
Top 20 Data Analytics
Consulting by CIO Review
Launched Big Data practice
2014
2013 Launched Big Data Warehousing
Formalized Alliances / Partnerships –
System Integrators
Co-author, with Ralph Kimball, The
Data Warehouse ETL Toolkit (Wiley)
Dedicated to Data Warehousing,
Business Intelligence since 1996
Began consulting database
programing and data modeling
25+ years hands-on experience
building database solutions
Founded Caserta Concepts in NYC
Web log analytics solution published
in Intelligent Enterprise
Partnered with Big Data vendors IBM,
Cloudera, Hortonworks, more…
Launched Training practice, teaching
data concepts world-wide
Laser focus on extending Data
Warehouses with Big Data solutions
2010
2009
2004
2001
1996
1986
Meetup in NYC – 1,180+ Members
2012
Dedicated to Data Governance
Techniques Innovation on Big Data
Established best practices for data
analytics ecosystem implementation
– Higher Education

4. About Caserta Concepts
• Technology services company with expertise in data analysis:
• Big Data Solutions
• Data Warehousing
• Business Intelligence
• Data Science & Analytics
• Data on the Cloud
• Data Interaction & Visualization
• Core focus in the following industries:
• eCommerce / Retail / Marketing
• Financial Services / Insurance
• Healthcare / Ad Tech / Higher Ed
• Established in 2001:
• Increased growth year-over-year
• Industry recognized work force
• Strategy, Implementation
• Writing, Education, Mentoring

5. Client Portfolio
Finance. Healthcare
& Insurance
Retail/eCommerce
& Manufacturing
Education
& Services

6. Expertise & Offerings
Strategic Roadmap /
Assessment / Education /
Implementation
Big Data
Analytics
Data Warehousing/
ETL/Data Integration
BI/Visualization/
Analytics

7. Partners

8. Help Wanted
Does this word cloud excite you?
Storm
Cassandra
Big Data Architect Hbase
Speak with us about our open positions: leslie@casertaconcepts.com

9. About the BDW Meetup
• Big Data is a complex, rapidly changing
landscape
• We want to share our stories and hear about
yours
• Great networking opportunity for like minded
data nerds
• Opportunities to collaborate on exciting
projects
• Founded by Caserta Concepts, DW, BI & Big
Data Analytics Consulting
• Next BDW Meetup: October 21, 2014
• Hadoop as a Service with Altiscale
• Doing Big Data ETL with Python (PETL)
Twitter: #BDWmeetup
@CasertaConcepts
@hortonworks

10. Why Big Data?
Enrollments
Claims
Finance
ETL
Big Data Analytics
Ad-Hoc Query
Traditional
EDW
Big Data Cluster
Traditional BI
Horizontally Scalable Environment - Optimized for Analytics
Canned Reporting
NoSQL
Databases
ETL
Ad-Hoc/Canned
Reporting
Mahout MapReduce Pig/Hive
N1 N2 N3 N4 N5
Hadoop Distributed File System (HDFS)
Others…
Data Science

11. Why BI Must Grow Up/Catch Up
• Business Intelligence (BI)
• Evolved from legacy Decision Support Systems (DSS), born in the 1980’s
• Made to make querying relational data simpler
• For non/semi-technical business users
• GUI tries to insulate users from the complexities of relational databases,
• Technical knowledge still needed
• Frustrating for non-technical users
• Thirty years, vendors tried to make useful to tools for business world
The reality: The technical skills required
for effectiveness repeatedly hinders real
adoption.

12. The Current BI User Experience

13. Why Now?
• Big Data movement breaks the relational database barrier
• Enables analysis on massive amounts of structured and unstructured data.
• NoSQL puts the value of SQL based relational databases into question.
• This disruption is forging a new road for the progress and advancement of
scalable data analytics.
• Let’s question he value of legacy Business Intelligence tools
• Rather than forcing data users to become technologists, it makes data analysis
available for the masses.

14. Who does BI?
• The role of the ‘Business Analyst’, the primary user of the BI tool, is
being replaced or expanded by two types of data users:
1. Highly technical Data Scientists
2. Non-technical Business Persons
• New analytics (BI) platforms must be created to accommodate the
new users. We see these very discrete users using very different
technologies.
• Perhaps legacy BI tools will not go away, but the market is absolutely
about to be disrupted.

15. Empowering the Data Scientist
• Data Scientists have deep technical knowledge
• They enjoy writing code and mining data – ‘data munging’ is what
makes the propeller on their hat spin!
The best way to serve a data scientist is to provide access to raw data
and then get out of their way!

16. Empowering the Business Person
• Business users don’t have, and don’t want to have, technical ability
to interact with ‘data’.
• “We have a business to run! Programming should be done by people in
rooms with no windows.”
• “I need information at my fingertips and I should not need a PhD in SQL to
get it.”
• “It’s a myth that BI tools will solve my problems, I still need IT to get new
reports. This is unacceptable.”
• Every business professional on the planet knows how to search for
needed information via a Google search bar.
Business people want to be able to ‘Google’ their corporate data for
the information they need.

17. How easy should it be?

18. Which Graph/Chart and Why?
• During normal BI
implementations, much
time is spent/wasted on
selecting the best way to
graphically represent a
set of metrics.
• Algorithms that are
statistically proven to
best represent
information depending
on the type of question
being asked.
• The user should be able
to preview and change
from the default graphic
as easy as clicking ‘next’
on a Yahoo! Slideshow.

19. How should it look?
Lady gaga sales by state by customer age Go! joe@casertaconcepts.com
Region
Northeast
Midwest
South
West
Product
Records
Perfume
Clothes
Performances
Dates
2009 to 2013
DOWNLOAD
TO EXCEL

20. •Modern web application framework
• Developed and supported by Google
• Bootstrap used for Mobile
Build it yourself?
Angular
• JavaScript library for data visualization.
• Exposes full capability CSS3, HTML5 and SVG. Is extremely fast
• Support large datasets and dynamic behaviors for interaction
D3.js
(Or Banana)
• The “glue” that brings other components together
• The ‘engine’ that transforms search strings into queries.
• Integrated with the Customer Metadata repository
Python
• Full-text and faceted-search engine and database
• This is the backbone of the application Solr
• Customer Metadata repository. Stores all business rules (default
facets, etc) and user preferences (default graph types, etc)
• Cassandra may not be ultimate selection
Cassandra
• Amazon Web Services
• Can be a zero-footprint cloud based solution
• User experience is same as Googling info
AWS
The majority of the
important fun
lives here

21. Or Use Microsoft

22. Thank You
Joe Caserta
President, Caserta Concepts
joe@casertaconcepts.com
(914) 261-3648
@joe_Caserta