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Current Trends in Big Data Analytics: What are employers looking for?

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Caserta Concepts President Joe Caserta Featured Speaker at the New Jersey Big Data Alliance (NJBDA) Event “Current Trends in Big Data Analytics” Hosted by Montclair State University’s School of Business

The New Jersey Big Data Alliance (NJBDA), a partnership of the state’s higher education institutions dedicated to furthering NJ’s reputation as a leader in innovation and cutting edge technology, is holding a Big Data Conference on the campus of Montclair State University. “Current Trends in Big Data Analytics :What are Employers Looking For?” features Joe Caserta, president of consulting and technology innovation firm Caserta Concepts, and other thought-leaders in the big data analytics field.

For more information on the event, click here: http://ow.ly/Fk32K

For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.

Published in: Technology
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Current Trends in Big Data Analytics: What are employers looking for?

  1. 1. Current Trends in Big Data Analytics What are Employers Looking For? Joe Caserta President, Caserta Concepts
  2. 2. Joe Caserta Timeline Top 20 Big Data Consulting - CIO Review Launched Big Data practicex Formalized Alliances / Partnerships – System Integrators Co-author, with Ralph Kimball, The Data Warehouse ETL Toolkit (Wiley) Dedicated to Data Warehousing, Business Intelligence since 1996 2014 2013 2010 2009 2004 2001 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 Cloudera, HortonWorks, Datameer, more… Launched Training practice, teaching data concepts world-wide Laser focus on extending Data Warehouses with Big Data solutions 1986 Launched Big Data Warehousing Meetup in NYC – 1,500 Members 2012 Established best practices for big data ecosystem implementation – Healthcare, Finance, Insurance Top 20 Most Powerful Big Data consulting firms Dedicated to Data Governance Techniques on Big Data (Innovation)
  3. 3. Allow your Passion to become your Purpose, and it will someday become your Profession. -Gabrielle Bernstein Why are we here?
  4. 4. Do these Words Excite You? Engineer Architect Scientist
  5. 5. Why is Data Analytics so important? Sales Marketing Finance ETL Big Data Analytics Ad-Hoc Query Big Data Cluster Traditional BI Horizontally Scalable Environment - Optimized for Analytics Canned Reporting NoSQL Databases ETL Ad-Hoc/Canned Reporting Spark MapReduce Pig/Hive N1 N2 N3 N4 N5 Hadoop Distributed File System (HDFS) Others… Data Science Enterprise Data Warehouse ETL
  6. 6. Innovation is the only sustainable competitive advantage a company can have.
  7. 7. • Data is coming in so fast, how do we monitor it? • Real real-time analytics • Relevance engines, financial fraud sensors, early warning sensor Challenges With Big Data • Dealing with sparse, incomplete, volatile, and highly manufactured data •Agile to adapt quickly to changing business • Wider breadth of datasets and sources in scope requires larger data repositories • Most of world’s data is unstructured, semi-structured or multi-structured • Data volume is growing so processes must be more reliant on programmatic administration • Less people/process dependence Volume Variety Veracity Velocity
  8. 8. What to Learn in Big Data Hadoop Distribution: Cloudera, Hortonworks, Pivotal-HD, AWS  Tools:  Hive: Map data to structures and use SQL-like queries  Pig: Data transformation language for big data, from Yahoo  Spark: General-purpose cluster computing framework  Storm: Real-time ETL NoSQL: Document: MongoDB, CouchDB Graph: Neo4j, Titan Key Value: Riak, Redis Columnar: Cassandra, Hbase  Languages: SQL, Python, SciPy, Java, Scala  Predictive Modeling: R, SAS, SPSS, MLlib
  9. 9. For the Non-Tech: People, Process, Organization • This is the ‘people’ part. Establishing Enterprise Data Council, Data Stewards, etc. Organization •Definitions, lineage (where does this data come from), business definitions, technical metadata Metadata • Identify and control Privacy/Security sensitive data, regulatory compliance •Data must be complete and correct. Measure, improve, certify Data Quality and Monitoring Business Process Integration •Policies around data frequency, source availability, etc. •Ensure consistent business critical data i.e. Customers, Products, Employees, etc. Master Data Management •Data retention, purge schedule, storage/archiving Information Lifecycle Management (ILM)
  10. 10. The Big Data Warehouse Big Data Warehouse Data Science Workspace Fully Data Governed ( trusted) Metadata  Catalog ILM  who has access, how long do we “manage it” Data Lake – Integrated Sandbox Agile business insight through data-munging, machine learning, blending with external data, development of to-be BDW facts Data Quality and Monitoring  Monitoring of completeness of data Landing Area – Source Data in “Full Fidelity” Metadata  Catalog ILM  who has access, how long do we “manage it” Data is ready to be turned into information: organized, well defined, complete. Raw machine data collection, collect everything Metadata  Catalog ILM  who has access, how long do we “manage it” Data Quality and Monitoring  Monitoring of completeness of data User community arbitrary queries and reporting
  11. 11. Big Data Jobs • In the U.S. alone, McKinsey & Company projects a shortfall between 140,000 and 190,000 "deep analytical" big data professionals by 2018 • People with highly technical skills in machine learning, statistics, and/or computer science, • The actual hands-on big data people that know how to crunch huge data sets into meaningful information. • The 8 Most In-Demand Big Data Roles Role Expert Level Salary Avg. Rate/Hr. ETL Developers $110,000 - $130,000 $63.02 Hadoop Developers $150,000 - $175,000 $70.00 Visualization Developers $150,000 - $175,000 $57.50 Data Scientist $125,000 - $140,000 $40.04 OLAP Developers $ 97,900 - $115,550 $55.00 Data Warehouse Specialist $ 97,950 - $123,600 $57.50 Predictive Analytics Developer $103,700 - $129,000 $60.00 Information Architect $113,750 - $135,350 $65.00 Source: Richard Hein , CIO, January 15, 2014
  12. 12. What does a Data Scientist Do, Anyway?  The Sexiest job of the century!  Writes really cool and sophisticated algorithms that impacts the way the business runs.  NOT  Much of the time of a Data Scientist is spent:  Searching for the data they need  Making sense of the data  Figuring why the data looks the way is does and assessing its validity  Cleaning up all the garbage within the data so it represents true business  Combining events with Reference data to give it context  Correlating event data with other events  Finally, they write algorithms to perform mining, clustering and predictive analytics – the sexy stuff.
  13. 13. Some Data Analytics Use Cases Recommendation Engines “You chose… you might also like…” eCommerce Cross-channel customer linking to improve the customer experience and increase sales Advertising Real time interactive queries on massive audience datasets in the cloud Real-Time Aggregation, Monitoring & Alerting on events at extremely high message rates… ~1M msgs/sec Big Data Warehouse Extending EDW with Hadoop Governing data from the “lake” to the EDW Personal/Commercial Banking Investment/Trading Bank Quick Service Restaurant (QSR) Cable Television Audience-based Advertising
  14. 14. Recommended Reading
  15. 15. Closing Thoughts – Enable the Future
  16. 16. Thank You Joe Caserta President, Caserta Concepts joe@casertaconcepts.com (914) 261-3648 @joe_Caserta

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