Bigdata Bootcamp is on January 16th, 17th and 18th 2015. Global Big Data Conference is offering 3 day extensive bootcamp on Big Data. This is a fast paced, vendor agnostic, technical overview of the Big Data landscape. No prior knowledge of databases or programming is assumed. Big Data Bootcamp is targeted towards both technical and non-technical people who want to understand the emerging world of Big Data, with a specific focus on Hadoop, NoSQL & Machine Learning. Attendees will experience real Hadoop clusters and the latest Hadoop distributions.

1. Spark Santa Clara Bootcamp
Global Big Data Conference is offering 3 day
extensive bootcamp on Big Data.
 Date: January 16th , 17th & 18th
 Venue : Santa Clara Convention Center

2. Who Should Attend
Engineers
Developers
Networking specialists
Managers
Executives
Students
Professional Services
Architects
Data Analyst
BI Developer/Architect
QA
Performance Engineers
PM

3. Click Below Url to Register
http://globalbigdataconference.com/43/santa-clara/big-data-
bootcamp/attendee-registration.html

4. Agenda
Days Hadoop Track
Day1
January – 16th (7:30AM – 8:00PM)
•Registration
•Introduction to Big Data
•Introduction to Hadoop 2.0
•HDFS Deep Dive, HDFS Lab
•Use Case
•MapReduce Deep Dive, MapReduce Lab
•Hadoop: Amazon EC2 and CDH Setup
•Use Case

5. Agenda
Days NoSQL Track
Day2
January 17th
(8:00AM – 8:00PM)
• Succeeding with Polyglot Persistence
•HBase workshop
•Apache Tez
•Cassandra Workshop
•Cassandra Workshop
•Breaking Into Big Data
•Hive Workshop

6. Agenda
Days Data Science Track
Day3
January – 18th
(8:00AM – 6:00PM)
• Data Science & Machine Learning , R Workshop
•Big Data Trends and Startups
•Neo4j Workshop

7. Agenda
Days Big Data Track II
Day2
January – 17th
(8:00AM – 8:00PM)
• Succeeding with Polyglot Persistence
• MongoDB Workshop
• Apache Tez
• Pig Workshop

8. Agenda
Days Big Data Track II
(Big Data Analytics with Spark(Full day workshop))
Day3
January – 18th
(8:00AM – 6:00PM)
•Spark Core
•Spark SQL
•Spark Streaming
•Machine Learning and MLlib
•Scala
•Advanced Spark
•Use Case
•Scala Lab
•Spark Core Lab
•Spark SQL Lab
•MLib Lab Spark Streaming lab