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Big Data Analytics with Microsoft

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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

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Big Data Analytics with Microsoft

  1. 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. 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. 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. 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. 5. Client Portfolio Finance. Healthcare & Insurance Retail/eCommerce & Manufacturing Education & Services
  6. 6. Expertise & Offerings Strategic Roadmap / Assessment / Education / Implementation Big Data Analytics Data Warehousing/ ETL/Data Integration BI/Visualization/ Analytics
  7. 7. Partners
  8. 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. 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. 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. 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. 12. The Current BI User Experience
  13. 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. 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. 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. 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. 17. How easy should it be?
  18. 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. 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. 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. 21. Or Use Microsoft
  22. 22. Thank You Joe Caserta President, Caserta Concepts joe@casertaconcepts.com (914) 261-3648 @joe_Caserta

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