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Oracle Big Data Fundamentals Ed 2 (BDFE2)

  • Overview
  • Who Should Attend
  • Certifications
  • Prerequisites
  • Objectives
  • Content
  • Schedule
Course Overview

Duration: 5 Days

Price: SGD 4,200.00

In the Oracle Big Data Fundamentals course, you learn about big data, the technologies used in processing big data and Oracle's solution to handle big data. You also learn to use Oracle Big Data Appliance to process big data, and obtain a hands-on experience in using Oracle Big Data Lite VM. You identify how to acquire the raw data from a variety of sources, and learn to use HDFS and Oracle NoSQL Database to store the data. You learn about data integration options available in Oracle Big Data. These include Oracle Big Data Connectors to move data to and from Oracle Database, Oracle Data Integrator and Oracle GoldenGate for Big Data which provide integration and synchronization capabilities for data unification of relational and Hadoop data, and Oracle Big Data SQL, which enables dynamic, integrated access for all of your data big data, whether it is stored in HDFS, NoSQL, or Oracle Database. Finally, you learn how to analyze your big data using Oracle Big Data SQL, Oracle Advance Analytics, and Oracle Big Data Spatial and Graph.

Learn To:

  • Define Big Data.
  • Describe Oracle's Integrated Big Data Solution and its components.
  • Define Cloudera's distribution of Hadoop and its core components and the Hadoop ecosystem.
  • Use the Hadoop Distributed File System (HDFS).
  • Acquire big data using the Command Line Interface, Flume, and Oracle NoSQL Database.
  • Process big data using MapReduce, YARN, Hive, Oracle XQuery for Hadoop, Solr, and Spark.
  • Integrate big data and warehouse data using Sqoop, Oracle Big Data Connectors, Copy to Hadoop, Oracle Data Integrator, and Oracle GoldenGate for big data, and Oracle Big Data SQL.
  • Analyze big data using Oracle Big Data SQL, Oracle Big Data Spatial and Graph, and Oracle Advanced Analytics technologies.
  • Use and manage Oracle Big Data Appliance.
  • Identify the key features and benefits of Oracle Big Data Cloud Service.
  • Identify the key features and benefits of Oracle Big Data Cloud Service - Compute Edition.

Benefits To You

You will benefit from this course as you define the term big data and discuss Oracle’s Big Data solution and use cases. You learn about Apache Hadoop and its core components: HDFS, YARN, and MapReduce. You will also learn about some of the major projects in the Hadoop ecosystem. You will learn how to acquire data into HDFS and Oracle NoSQL Database by using CLI, Flume, and Kafka. To process the data stored in HDFS, you run MapReduce and Spark jobs.

You also explore a range of analysis options, including Oracle Advanced Analytics (OAA) (comprised of Oracle Data Mining and Oracle R Enterprise), and Oracle Big Data Spatial and Graph.

You will learn about the Oracle Big Data Appliance, Oracle Big Data Cloud Service, and Oracle Big Data Cloud Service - Compute Edition. You will study case scenarios where Oracle Big Data stands as the perfect solution.

 

Who Should Attend

  • Application Developers
  • Database Administrators
  • Database Developers

Course Certifications

This course is part of the following Certifications:

Prerequisites

Required Prerequisites:

  • Database Basics and Administration

Suggested Prerequisites:

  • Exposure to Big Data

 

Course Objectives

  • Define Big Data
  • Describe Oracle's Integrated Big Data Solution and its components
  • Define Cloudera's distribution of Hadoop and its core components and the Hadoop ecosystem
  • Use the Hadoop Distributed File System (HDFS)
  • Acquire big data using the Command Line Interface, Flume, and Oracle NoSQL Database
  • Process big data using MapReduce, YARN, Hive, Oracle XQuery for Hadoop, Solr, and Spark
  • Integrate big data and warehouse data using Sqoop, Oracle Big Data Connectors, Copy to Hadoop, Oracle Data Integrator, and Oracle GoldenGate for big data, and Oracle Big Data SQL
  • Analyze big data using Oracle Big Data SQL, Oracle Big Data Spatial and Graph, and Oracle Advanced Analytics technologies
  • Use and manage Oracle Big Data Appliance
  • Identify the key features and benefits of Oracle Big Data Cloud Service
  • Identify the key features and benefits of Oracle Big Data Cloud Service - Compute Edition

Course Content

Introduction

  • Questions About You
  • Course Objectives
  • Course Road Map
  • Oracle Big Data Lite (BDLite) Virtual Machine (VM) Home Page
  • Starting the Oracle BDLite VM and accessing the Practice Files
  • Reviewing the Available Big Data Documentation, Tutorials, and Other Resources

Introducing Oracle Big Data Strategy

  • Characteristics of Big Data
  • Importance of Big Data
  • Big Data Opportunities: Some Examples
  • Big Data Challenges
  • Big Data implementation examples
  • Oracle strategy for Big Data: combining Big Data Processing Engines: Hadoop / NoSQL / RDBMS

Using Oracle Big Data Lite Virtual Machine and Movieplex Application

  • Oracle Big Data Lite VM Used in this Course
  • Oracle Big Data Lite VM Home Page Sections
  • Reviewing the Deployment Guide
  • Downloading and installing Oracle VM VirtualBox and its Extension Pack
  • Downloading and Running 7-zip Files to create Virtual Box Appliance File
  • Importing the Appliance File
  • Staring the Big Data Lite VM and Starting and Stopping Services
  • Introducing the Oracle Movieplex Case Study

Introduction to the Big Data Ecosystem

  • Computer Clusters and Distributed Computing
  • Apache Hadoop
  • Types of Analysis That Use Hadoop
  • Types of Data Generated
  • Apache Hadoop Core Components: HDFS, MapReduce (MR1), and YARN (MR2)
  • Apache Hadoop Ecosystem
  • Cloudera’s Distribution Including Apache Hadoop (CDH)
  • CDH Architecture and Components

Introduction to the Hadoop Distributed File System

  • Hadoop Distributed Filesystem (HDFS) Design Principles, Characteristics, and Key Definitions
  • Sample Hadoop High Availability (HA) Cluster
  • HDFS Files and Blocks
  • Active and Standby Daemons (Services) Functions
  • DataNodes (DN) Daemons Functions
  • Writing a File to HDFS: Example
  • Interacting With Data Stored in HDFS: Hue, Hadoop Client, WebHDFS, and HttpFS

Acquire Data using CLI, Fuse, Flume, and Kafka

  • Reviewing the Command Line Interface (CLI)
  • Viewing File System Contents Using the CLI
  • FS Shell Commands
  • Loading Data Using the CLI
  • Overview of FuseDFS
  • What is Flume?
  • Kafka topics
  • Additional Resources

Acquire and Access Data Using Oracle NoSQL Database

  • What is a NoSQL Database
  • RDBMS Compared to NoSQL
  • HDFS Compared to NoSQL
  • Define Oracle NoSQL Database
  • Oracle NoSQL models: Key-Value and Table
  • Acquiring and Accessing Data in a NoSQL DB
  • Accessing the CLIs (Data, Admin, SQL)
  • Accessing the KVStore

Introduction to MapReduce and YARN Processing Frameworks

  • MapReduce Framework Features, Benefits, and Jobs
  • Parallel Processing with MapReduce
  • Word Count Examples
  • Data Locality Optimization in Hadoop
  • Submitting and Monitoring a MapReduce Job
  • YARN Architecture, Features, and Daemons
  • YARN Application Workflow
  • Hadoop Basic Cluster: MapReduce 1 Versus YARN (MR 2)

Resource Management Using Yarn

  • Job Scheduling in YARN
  • First In, First Out (FIFO) Scheduler, Capacity Scheduler, and Fair Scheduler
  • Cloudera Manager Resource Management Features
  • Static Service Pools
  • Working with the Fair Scheduler
  • Cloudera Manager Dynamic Resource Management: Example
  • Submitting and Monitoring a MapReduce Job Using YARN
  • Using the YARN application Command

Overview of Apache Spark

  • Benefits of Using Spark
  • Spark Architecture
  • Spark Application Components: Driver, Master, Cluster Manager, and Executors
  • Running a Spark Application on YARN (yarn-cluster Mode)
  • Resilient Distributed Dataset (RDD)
  • Spark Interactive Shells: spark-shell and pyspark
  • Word Count Example by Using Interactive Scala
  • Monitoring Spark Jobs Using YARN's ResourceManager Web UI

Overview of Apache Hive

  • What is Hive?
  • Use Case: Storing Clickstream Data
  • Hadoop Architecture
  • How is Data Stored in HDFS?
  • Organizing and Describing Data With Hive
  • Big Data SQL on Top of Hive Data
  • Defining Tables Over HDFS
  • Hive Queries

Overview of Cloudera Impala

  • Overview of Cloudera Impala
  • Hadoop: Some Data Access/Processing Options
  • Cloudera Impala
  • Cloudera Impala: Key Features
  • Cloudera Impala: Supported Data Formats
  • Cloudera Impala: Programming Interfaces
  • How Impala Fits Into the Hadoop Ecosystem
  • How Impala Works with Hive

Using Oracle XQuery for Hadoop

  • XML Review
  • Oracle XQuery for Hadoop (OXH)
  • OXH Features
  • OXH Data Flow
  • Using OXH: Installation, Functions, Adapters, and Configuration Properties
  • Running an OXH Query
  • XQuery Transformation and Basic Filtering
  • Viewing the Completed Query in YARN's ResourceManager

Overview of Solr

  • Overview of Solr
  • Apache Solr (Cloudera Search)
  • Cloudera Search: Key Capabilities
  • Cloudera Search: Features
  • Cloudera Search Tasks
  • Indexing in Cloudera Search
  • Types of Indexing
  • The solrctl Command

Integrating Your Big Data

  • Unifying Data: A Typical Requirement
  • Comparing Big Data Processing Engines
  • Introducing Data Unification Options
  • When To Use These Options?

Batch Loading Options

  • Apache Sqoop
  • Oracle Loader for Hadoop
  • Oracle Copy to Hadoop

Using Oracle SQL Connector for HDFS

  • Batch and Dynamic Loading: Oracle SQL Connector for HDFS
  • OSCH Architecture
  • Using OSCH
  • Features
  • Parallelism and Performance
  • Performance Tuning
  • Key Benefits
  • Loading: Choosing a Connector

Using Oracle Data Integrator and Oracle GoldenGate for Big Data

  • ETL and Synchronization: Oracle Data Integrator
  • ODI’s Declarative Design
  • ODI Knowledge Modules (KMs)Simpler Physical Design / Shorter Implementation Time
  • Using ODI with Big Data Heterogeneous Integration with Hadoop Environments
  • Using ODI Studio
  • ODI Studio Components: Overview
  • ODI Studio: Big Data Knowledge Modules
  • Oracle GoldenGate for Big Data

Using Oracle Big Data SQL

  • Barriers to Effective Big Data Adoption
  • Overcoming Big Data Barriers
  • Oracle Big Data SQL: The Hybrid Solution
  • Benefits: Virtualizes data access across Oracle Database, Hadoop and NoSQL stores
  • Using Oracle Big Data SQL
  • Query Performance Overview
  • Deployment Options

Using Oracle Big Data Spatial and Graph

  • Graph and Spatial Analysis: All About Relationships
  • What is Oracle Big Data Spatial and Graph (BDSG)?
  • Strategy (supported platforms, etc)
  • BDSG: Graph Analysis
  • Oracle BDSG: Spatial Analysis
  • Multimedia Analytics Framework
  • Deployment Options for Oracle BDSG
  • Additional Resources

Using Oracle Advanced Analytics

  • Oracle Advanced Analytics (OAA)
  • OAA: Oracle Data Mining
  • OAA: Oracle R Enterprise

Oracle Big Data Deployment Options

  • Introduction to the Oracle Big Data Appliance
  • Running the Oracle BDA Configuration Generation Utility
  • Oracle BDA Mammoth Software Deployment Bundle
  • Using the Oracle BDA mammoth Utility
  • BDA Hardware and Integrated and Optional Software
  • Administering and Securing the Oracle BDA
  • Introduction to the Oracle Big Data Cloud Service
  • Introduction to the Oracle Big Data Cloud Service – Compute Edition

 

Course ID: BDFE2


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