Oracle Big Data Fundamentals Ed 2 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


This is a Rare Course and it can be take up to 3 weeks to arrange the training.

The 1-on-1 Advantage

Flexible Dates

  • • Choose Start Date
  • • Reschedule After Booking
  • • Weekend / Evening Option

4-Hour Sessions

You will learn:

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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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)
  • 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
  • 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
  • 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
  • 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
  • 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
  • Apache Solr (Cloudera Search)
  • Cloudera Search: Key Capabilities
  • Cloudera Search: Features
  • Cloudera Search Tasks
  • Indexing in Cloudera Search
  • Types of Indexing
  • The solrctl Command
  • Unifying Data: A Typical Requirement
  • Comparing Big Data Processing Engines
  • Introducing Data Unification Options
  • Introducing Data Unification Options
  • Apache Sqoop
  • Oracle Loader for Hadoop
  • Oracle Copy to Hadoop
  • 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
  • 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
  • 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
  • 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
  • Oracle Advanced Analytics (OAA)
  • OAA: Oracle Data Mining
  • OAA: Oracle Data Mining
  • 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
Live Online Training (Duration : 40 Hours) Fee On Request
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.

4 Hours
8 Hours
Week Days

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Group Training
Date On Request
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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Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?

Student Name Country Feedback Rating
Abu United States A1. Yes, she was very clear & a strong mastery of both English & Azure platforms hence following was easy. She was also patient and was answering every question asked, she also shared material & tips on how to pass the exam, Well done, and keep it up.
Nick Barker United States A1. Calm and understanding, she not only kept the course running well but also took time to help those with lots of questions. Very well done.
Robin Johnson United States A2. Individual attention during training, excellent customer service and coordination. Neha has been ever so helpful and is definitely an asset to Koenig.
Shivani Prasad United States A1. The trainer was great and his teaching methods were incredible. He used different teaching methods to ensure I was learning well. A fun trainer with lots of patience and understanding.
Adam United States A1. The trainer was very knowledgeable and well prepared about the presentation/training subject, with a positive attitude towards students and making every effort in answering questions during the training session. Top-notch service!
Suresh United States A1. Exceptional... recommend to everyone!
Shyam Vaddakapett United States A1. My trainer, Mohit Kakkar was excellent as he focused on hands-on assignments to supplement the concepts of AZ 900. The training coordinator Jasmeet as well as the ops manager Sheshadri are also very professional and customer-centric. I recommend Koenig for training courses, and I will take some more courses with them soon.
Gilles Chirlias United States A1. Chavi was a great teacher, I have learned a lot from her. I would like to have her for the next training, which would be the AZ 301 and AZ 500.
Abdulrahman Saad Alshahrani United States A1. Excellent trainer. I will take another course with him someday. He deserves a good appreciation from his Manager.
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1-on-1 Public - Select your start date. Other students can be merged.
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