Big Data and Hadoop Revolution

Description:

The continued rise in the volume and diversity of available data presents both opportunities and challenges for businesses to source information that is relevant, targeted and timely.

Big Data uniquely enables us to see, understand and be aware of the competitive landscape in new and increasingly detailed ways. Leveraging this data can enable organizations to target markets, engage with new prospects, compete more effectively, and close sales.

One of the most prominent framework for processing big data is Apache Hadoop, which is developed for distributed processing of large data sets across clusters of computers. The framework is written in Java, but a language binding exists for most of the commonly used languages.

This seminar will provide you with introduction to Big Data Technologies and help you identify the benefits of Big Data for your business.

In addition, we’ll introduce Apache Hadoop and related projects. Learn how Apache Hadoop addresses the limitations of traditional computing, helps businesses overcome real challenges, and powers new types of big data analytics. This seminar also introduces the rest of the Apache Hadoop ecosystem and outlines how to prepare the data center and manage Hadoop in production.

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Introduction to Big Data (Heb) by Nimrod Keinan

The primary audience for this course are Architects, Technical Managers, CTOs, Engineering Managers, etc. No prior Hadoop experience is required.

Knowledge and experience with RDBMS and Information systems

Introduction & The Motivation for Hadoop

Explore the basics of Apache Hadoop, including the Hadoop Distributed File System (HDFS), MapReduce, and the anatomy of a Hadoop cluster.

Hadoop Basic Concepts

There are many components working together in the Apache Hadoop stack. By understanding how each functions, you gain more insight into Hadoop’s functionality in your own IT environment. This chapter goes beyond the motivation for Apache Hadoop and dissects the Hadoop Distributed File System (HDFS), MapReduce, and the general topology of a Hadoop cluster.

Hadoop Solutions

Learn how Apache Hadoop is used in the real world. This chapter explores ways to use Apache Hadoop to harness Big Data and solve business problems in ways never before imaginable. Explore common business challenges that can be addressed using Hadoop, the origins of Big Data, types of analyses powered by Hadoop, and industry use cases for Hadoop.

The Hadoop Ecosystem

Various projects make up the Apache Hadoop ecosystem, and each improves data storage, management, interaction, and analysis in its own unique way. This chapter reviews Hive, Pig, Impala, HBase, Flume, Sqoop, and Oozie, how they function within the stack and how they help integrate Hadoop within the production environment.

Managing Your Hadoop Solution

It is critical to understand how Apache Hadoop will affect the current setup of the data center and to plan ahead. This chapter helps you seamlessly integrate the platform into your environment. Find out what resources are required to deploy Hadoop, how to plan for cluster capacity, and how to staff for your Big Data strategy.

Demystifying Big Data 

  • History of Database Systems
  • Exploration of Data
  • The CAP Theorem
  • Replication, Clustering and Sharding
  • Cloud Computing

NoSQL

  • Key-Value Store
  • Hbase
  • Cassandra
  • DynamoDB
  • Document Store
  • MongoDB
  • CouchBase
  • Graph Databases
  • Neo4J

In-Memory Technologies

  • Redis
  • VoltDB
  • Apache Spark
  • SAP HANA
  • Oracle In-Memory Database

Search, Indexing and Log Analysis

  • ElasticSearch
  • Splunk

YesSQL!

  • Hive
  • Impala
  • Drill
  • Phoenix
  • Couchbase N1QL
  • Spark SQL

Oracle Lines Up

  • Big Data Appliance
  • Big Data SQL
  • JSON API
  • Oracle sharding
  • Spatial and Graph
ערן קורןערן הינו יועץ מוביל ובכיר בנאיה טכנולוגיות בתחום מסדי נתונים ו-Big Data. במסגרת תפקידו ערן מתמחה בניהול מסדי נתונים, בניית ארכיטקטורה, וכן בפלטפורמות NoSQL מובילות בעולם ה- Big Data.
  • על פי דרישה מועד פתיחה
  • 9:00-16:30daysימים ושעות
  • 16academic hours שעות אקדמיות
  • מתקדםcourse levelרמת הקורס
  • עברית/Englishlanguageשפת הדרכה
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