להשארת פרטים
דאטה ומסדי נתונים Hadoop Architecture Fundamentals
מספר הקורס 3531 8 סה"כ שעות אקדמאיות
1 מפגשים
מעניין אותי לשמוע עוד 
המועדים הקרובים
ניתן לפתוח קורס בהתאמה אישית לארגונים במועד שיתואם עימנו
Overview
Apache Hadoop is an open-source distributed fault-tolerant system that leverages commodity hardware to achieve large-scale agile data storage and processing. Hadoop empowers applications to work with thousands of nodes and petabytes of data without exposing the complexity of clustering to the end user.
This course discusses the design principles behind Apache Hadoop and explains the architecture of its core sub-systems: HDFS and MapReduce
On Completion, Delegates will be able to
Understand the main Hadoop components and other open source software related to Hadoop. Understand how HDFS works and the concepts of map and reduce operations.
Who Should Attend
This course is intended for developers, architects and technical managers who wish to understand Hadoop’s architecture.
Module 1: Big Data Brief Overview
- Big Data Characteristics and Use-Cases
- New Data Categories
- Big Data vs. Traditional Enterprise Relational Data
Module 2: Introduction to Hadoop
- Hadoop vs. Traditional Large-Scale Data Storage and Processing
- Introduction to Hadoop Ecosystem: HDFS, MapReduce, Pig, Hive, HBase
- Hadoop Distributors: Cloudera, Hortonworks, MapR
- Available Java Runtime Environments for Hadoop
Module 3: MapReduce
- MapReduce Motivation and Core Concepts
- How Does MapReduce Work?
- Implementing Common MapReduce Patterns
- Core Hadoop API Interfaces and Classes
Module 4: The Hadoop Distributed File System (HDFS)
- How HDFS works?
- Writing a File to HDFS
- HDFS Command Line
- Hadoop Cluster Architecture overview
Module 5: Hadoop Related Projects
- Data Warehousing with Hive
- Parallel Processing with Pig
- Data Storage with HBase
- Common Utilities: Sqoop, Flume, Zookeeper, etc.
This course assumes no prior knowledge of Hadoop. Participants should be comfortable with Java code and familiar with DWH concepts