Cloudera Developer for Spark and Hadoop Training in New Zealand

  • Learn via: Online Instructor-Led / Classroom Based / Onsite
  • Duration: 4 Days
  • Price: Please contact for booking options
  • Upcoming Date:
  • UK Based Global Training Provider

This hands-on training course delivers the key concepts and expertise participants need to ingest and process data on a Hadoop cluster using the most up-to-date tools and techniques. Employing Hadoop ecosystem projects such as Spark (including Spark Streaming and Spark SQL), Flume, Kafka, and Sqoop, this training course is the best preparation for the real-world challenges faced by Hadoop developers. With Spark, developers can write sophisticated parallel applications to execute faster decisions, better decisions, and interactive actions, applied to a wide variety of use cases, architectures, and industries.

We can organize this training at your preferred date and location. Contact Us!

Prerequisites

There are no prerequisites for this course.

Who Should Attend

  • This course is designed for developers and engineers who have programming experience, but prior knowledge of Hadoop is not required
  • Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required
  • Basic familiarity with the Linux command line is assumed
  • Basic knowledge of SQL is helpful

What You Will Learn

  • Through expert-led discussion and interactive, hands-on exercises, participants will learn how to:
  • Distribute, store, and process data in a Hadoop cluster
  • Write, configure, and deploy Apache Spark applications on a Hadoop cluster
  • Use the Spark shell for interactive data analysis
  • Process and query structured data using Spark SQL
  • Use Spark Streaming to process a live data stream
  • Use Flume and Kafka to ingest data for Spark Streaming

Training Outline

Introduction

Introduction to Apache Hadoop and the Hadoop Ecosystem

  • Apache Hadoop Overview
  • Data Storage and Ingest
  • Data Processing
  • Data Analysis and Exploration
  • Other Ecosystem Tools
  • Introduction to the Hands-On Exercises
  • Apache Hadoop File Storage
  • Problems with Traditional

Large-Scale Systems

  • HDFS Architecture
  • Using HDFS
  • Apache Hadoop File Formats

Data Processing on an Apache Hadoop Cluster

  • YARN Architecture
  • Working With YARN

Importing Relational Data with Apache Sqoop

  • Apache Sqoop Overview
  • Importing Data
  • Importing File Options
  • Exporting Data

Apache Spark Basics

  • What is Apache Spark?
  • Using the Spark Shell
  • RDDs (Resilient Distributed Datasets)
  • Functional Programming in Spark

Working with RDDs

  • Creating RDDs
  • Other General RDD Operations

Aggregating Data with Pair RDDs

  • Key-Value Pair RDDs
  • Map-Reduce
  • Other Pair RDD Operations

Writing and Running Apache Spark Applications

  • Spark Applications vs. Spark Shell
  • Creating the SparkContext
  • Building a Spark Application

(Scala and Java)

  • Running a Spark Application
  • The Spark Application Web UI

Configuring Apache Spark Applications

  • Configuring Spark Properties
  • Logging

Parallel Processing in Apache Spark

  • Review: Apache Spark on a Cluster
  • RDD Partitions
  • Partitioning of File-Based RDDs
  • HDFS and Data Locality
  • Executing Parallel Operations
  • Stages and Tasks

RDD Persistence

  • RDD Lineage
  • RDD Persistence Overview
  • Distributed Persistence

Common Patterns in Apache Spark

Data Processing

  • Common Apache Spark Use Cases
  • Iterative Algorithms in Apache Spark
  • Machine Learning
  • Example: k-means

DataFrames and Spark SQL

  • Apache Spark SQL and the SQL Context
  • Creating DataFrames
  • Transforming and Querying DataFrames
  • Saving DataFrames
  • DataFrames and RDDs
  • Comparing Apache Spark SQL, Impala, and Hive-on-Spark
  • Apache Spark SQL in Spark 2.x

Message Processing with Apache Kafka

  • What is Apache Kafka?
  • Apache Kafka Overview
  • Scaling Apache Kafka
  • Apache Kafka Cluster Architecture
  • Apache Kafka Command Line Tools

Capturing Data with Apache Flume

  • What is Apache Flume?
  • Basic Flume Architecture
  • Flume Sources
  • Flume Sinks
  • Flume Channels
  • Flume Configuration

Integrating Apache Flume and Apache Kafka

  • Overview
  • Use Cases
  • Configuration

Apache Spark Streaming:

Introduction to DStreams

  • Apache Spark Streaming Overview
  • Example: Streaming Request Count
  • DStreams
  • Developing Streaming Applications

Apache Spark Streaming:

Processing Multiple Batches

  • Multi-Batch Operations
  • Time Slicing
  • State Operations
  • Sliding Window Operations

Apache Spark Streaming: Data Sources

  • Streaming Data Source Overview
  • Apache Flume and Apache Kafka

Data Sources

  • Example: Using a Kafka Direct Data Source

Conclusion

Why Choose Us

Experience Cloudera Developer for Spark and Hadoop in New Zealand through Bilginç IT Academy's live and interactive virtual classroom environment, accessible from your home, office, or any location. Connect with expert trainers in real time and bring the energy of classroom learning into the digital experience.

  • Live Instructor-Led Sessions: Join scheduled training sessions with your instructor and fellow delegates in real time.
  • Interactive Learning Experience: Take part in discussions, practical exercises, group activities, and Q&A sessions throughout the course.
  • Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
  • Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's long-standing experience in delivering professional training since 1995.
  • Flexible and Scalable Delivery: Access live virtual classrooms from New Zealand and worldwide, with flexible planning options for individual and corporate training needs.

Experience Cloudera Developer for Spark and Hadoop in a focused classroom environment in New Zealand. Bilginç IT Academy's carefully selected training venues provide a professional setting where delegates can interact directly with expert trainers and peers.

  • Experienced Trainers: Learn from specialists with extensive field experience and real-world knowledge.
  • Professional Training Venues: Attend courses in comfortable, well-equipped classrooms designed to support effective learning.
  • Focused Classroom Experience: Benefit from limited class sizes that encourage discussion, interaction, and personalized support.
  • Quality-Driven Learning: Develop practical skills through structured, up-to-date, and professionally designed training content.

Meet your team's training needs with Bilginç IT Academy's onsite Cloudera Developer for Spark and Hadoop in New Zealand solution, delivered at your office or preferred location. Align your team's development with your business goals through a training experience tailored to your organization.

  • Tailored Course Content: Adapt the training program to your organization's projects, team structure, and specific business requirements.
  • Time and Cost Efficiency: Reduce travel, accommodation, and operational costs while maximizing the value of your training investment.
  • Team-Focused Learning: Help your employees develop around the same knowledge base and strengthen collaboration across your organization.
  • Simplified Planning and Tracking: Manage the training process, participant development, and organizational requirements with greater control.


Contact us for more detail about our trainings and for all other enquiries!

Cloudera Developer for Spark and Hadoop Training Course in New Zealand Schedule

Join our public courses in our New Zealand facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

We can organize this training at your preferred date and location.
08 July 2026 (4 Days)
Auckland, Wellington, Christchurch
12 July 2026 (4 Days)
Auckland, Wellington, Christchurch
14 July 2026 (4 Days)
Auckland, Wellington, Christchurch
17 July 2026 (4 Days)
Auckland, Wellington, Christchurch
21 July 2026 (4 Days)
Auckland, Wellington, Christchurch
22 July 2026 (4 Days)
Auckland, Wellington, Christchurch
02 August 2026 (4 Days)
Auckland, Wellington, Christchurch
11 August 2026 (4 Days)
Auckland, Wellington, Christchurch

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