Big Data Bootcamp: 2 Days Workshop in Melbourne

Bring your team and save:

1. Groups of three or more receive a 10% discount

2. Organizations hosting an in-house session with 10+ participants enjoy a 15% discount.

About This Course

Duration: 2 Days (9:00 AM – 5:00 PM)
Delivery Mode: Classroom / In-Person Workshop
Language: English
Credits: 16 PDUs / Training Hours
Certification: Course Completion Certificate Provided
Refreshments: Lunch, tea/coffee, and snacks included

Course Overview

Big Data Bootcamp is a two-day workshop that provides a technical overview of Apache Hadoop for project managers, business managers, and data analysts.
Students will understand the overall big data space, technologies involved, and get a detailed overview of Apache Hadoop. The course exposes students to real world use cases to comprehend the capabilities of Apache Hadoop. Students will also learn about YARN, HDFS, Apache Pig, and Apache Hive, with hands-on experience for each topic.

Learning Objectives

• Learn about the big data ecosystem
• Understand the benefits and ROI from existing data
• Learn about Hadoop and how it is transforming the workspace
• Learn about MapReduce and Hadoop Distributed File System
• Learn about identifying new business opportunities using Hadoop
• Learn about improving data management processes
• Learn about clarifying results using Hadoop
• Learn about expanding data sources
• Learn about scaling current workflows to handle more users at lower cost
• Learn about Hadoop ecosystem technologies
• Learn how to write a simple MapReduce job
• Learn how to transform data using scripting languages
• Learn how to analyze large quantities of data using SQL-like languages
• Learn how to connect data warehouses to Hadoop
• Learn how to move data into Hadoop
• Learn how to move analysis results to BI tools like Tableaux
• Learn how to automate workflows using Oozie
• Learn about polyglot persistence
• Learn about future trends in Big Data
• Discover tips and tricks behind successful Hadoop deployments

Target Audience

This course is for anybody involved with databases, data analysis, or dealing with large volumes of data.
It is ideal for:

• Business Analysts
• Software Engineers
• Project Managers
• Data Analysts
• Business Customers
• Team Leaders
• System Analysts

No prior knowledge of big data or Hadoop is required. Some programming experience is a plus but not necessary.

Why Choose This Course?

This two-day workshop provides a comprehensive overview of the big data ecosystem and Apache Hadoop, combining theory with hands-on exercises. Participants gain practical exposure to real world use cases, understand how to manage and analyze large datasets, and learn how to automate workflows and improve data management processes using Hadoop technologies.

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Want to Train Your Entire Team Together?

This two-day workshop can be delivered in classroom or virtual format, making it suitable for teams looking to build big data and Hadoop capabilities together.

Contact us today to schedule a customized in-house, face-to-face session:

Info: • Big Data – beyond the obvious trends

• Exponentially increasing data

• Big data sources

• Data warehousing, business intelligence, analytics, predictive statistics, data science

Info: • First generation systems

• Second generation systems

• Enterprise search

• Visualizing and understanding data with processing

• NOSQL databases

• Apache Hadoop

Info: • What is Hadoop? Who are the major vendors?

• A dive into the Hadoop Ecosystem

• Benefits of using Hadoop

• How to use Hadoop within your infrastructure?

Info: • What is MapReduce?

• Why do you need MapReduce?

• Using MapReduce with Java and Ruby

Info: • What is YARN?

• What are the advantages of using YARN over classical MapReduce?

• Using YARN with Java and Ruby

Info: • What is HDFS?

• Why do you need a distributed file system?

• How is a distributed file system different from a traditional file system?

• What is unique about HDFS when compared to other file systems?

• HDFS and reliability

• Does it offer support for compressions, checksums, and data integrity?

Info: • Why do you need to transform data?

• What is Pig?

• Use cases for Pig

Info: • How do you handle structured data with Hadoop?

• What is Hive / HCatalog?

• Use cases for Hive / HCatalog

Info: • How do you move your existing data into Hadoop?

• What is Sqoop?

Info: • Benefits of automation

• What is Oozie?

• Automatically running workflows

• Setting up workflow triggers

Info: • Framing scenarios

• Understanding how to ask questions

• Tying possibilities to your own business drivers

• Common opportunities

• Real world examples

Info: • How to use MapReduce in Hadoop

• How to use YARN within Hadoop

• Overview of HDFS commands

• Hands-on activities with Pig

• Hands-on activities with Hive / HCatalog

• Hands-on activities with Sqoop

• Demonstration of Oozie

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