Oracle Big Data Fundamentals Ed 2 Certification Training Course Overview

In the Oracle Big Data Fundamentals 5 day’s course, students will learn about big data, the technologies used in processing big data and Oracle's solution for handle huge information. Students will also figure out how to use Oracle Big Data Appliance to process big data & get a hands-on experience in using Oracle Big Data Lite VM. In this course, you will learn how to identify raw data from a variety of sources and learn to use HDFS and Oracle NoSQL Database to store the data. Finally, you figure out how to examine your big data using Oracle Big Data SQL, Oracle Advance Analytics, Oracle Big Data Spatial and Graph.
  • Application Developers 
  • Database Administrators 
  • Database Developers


Oracle Big Data Fundamentals Ed 2 (40 Hours) Download Course Contents

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Course Modules

Module 1: Introductio
  • 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
Module 2: 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
Module 3: 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 F
  • Importing the Appliance File
  • Staring the Big Data Lite VM and Starting and Stopping Services
  • Introducing the Oracle Movieplex Case Study
Module 4: 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
Module 5: 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
Module 6: 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
Module 7: 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
Module 8: 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)
Module 9: 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
Module 10: 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
Module 11: 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
Module 12: 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
Module 13: 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
Module 14: 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
Module 15: Integrating Your Big Data
  • Unifying Data: A Typical Requirement
  • Comparing Big Data Processing Engines
  • Introducing Data Unification Options
  • Introducing Data Unification Options
Module 16: Batch Loading Options
  • Apache Sqoop
  • Oracle Loader for Hadoop
  • Oracle Copy to Hadoop
Module 17: 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
Module 18: Using Oracle Data Integrator and Oracle GoldenGate for Big Dat
  • 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
Module 19: 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
Module 20: 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
Module 21: Using Oracle Advanced Analytics
  • Oracle Advanced Analytics (OAA)
  • OAA: Oracle Data Mining
  • OAA: Oracle Data Mining
Module 22: 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
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Course Prerequisites
  • Exposure to Big Data.

Upon Completion of this Course, you will accomplish following:-

  • Define Big Data.
  • Describe Oracle's Integrated Big Data Solution & its components.
  • Define Cloudera's distribution of Hadoop and its core components & the Hadoop ecosystem.
  • Use the Hadoop Distributed File System (HDFS).
  • Acquire big data using the Command Line Interface, Flume & Oracle NoSQL Database.
  • Process big data using MapReduce, YARN, Hive, Oracle XQuery for Hadoop, Solr & Spark.
  • Use and manage Oracle Big Data Appliance.
  • Identify the key features & benefits of Oracle Big Data Cloud Service.
  • Identify the key features & benefits of Oracle Big Data Cloud Service - Compute Edition.


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