Cloudera Developer for Spark and Hadoop Training in Norway

  • Learn via: Classroom / Virtual Classroom / Online
  • Duration: 4 Days
  • Price: Please contact for booking options
We can host this training at your preferred location. Contact us!

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.

There are no prerequisites for this course.

  • 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

  • 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

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



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

Upcoming Trainings

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

Classroom / Virtual Classroom
09 juni 2024
Oslo, Bergen, Trondheim
4 Days
Classroom / Virtual Classroom
15 juni 2024
Oslo, Bergen, Trondheim
4 Days
Classroom / Virtual Classroom
09 august 2024
Oslo, Bergen, Trondheim
4 Days
Classroom / Virtual Classroom
24 august 2024
Oslo, Bergen, Trondheim
4 Days
Classroom / Virtual Classroom
12 oktober 2024
Oslo, Bergen, Trondheim
4 Days
Classroom / Virtual Classroom
16 oktober 2024
Oslo, Bergen, Trondheim
4 Days
Classroom / Virtual Classroom
24 oktober 2024
Oslo, Bergen, Trondheim
4 Days
Classroom / Virtual Classroom
22 oktober 2024
Oslo, Bergen, Trondheim
4 Days
Cloudera Developer for Spark and Hadoop Training Course in Norway

The Nordic country Norway, is in Northern Europe. Known for its stunning natural beauty, including fjords, mountains, and forests, Norway is also famous for its high standard of living and strong social welfare system. Norway's capital and largest city is Oslo. Tromsø, Bergen, Trondheim and Stavanger are the other tourist attracting cities of Norway.

Norway is a constitutional monarchy with King Harald V as the head of state. The country has a population of 5,425,270 as of January 2022. Norway is a relatively small country and has a relatively low population density, with much of its land area covered by forests, mountains, and fjords. Despite its small size, Norway is known for its rich cultural heritage, strong economy, and stunning natural beauty, which attracts millions of visitors every year. This Nordic country is also known for its winter sports, such as skiing and snowboarding, and is a popular destination for outdoor enthusiasts.

Norway has a long history of invention and is home to numerous more top-tier tech firms and research facilities, such as; Kongsberg Gruppen, Telenor, Atea, Evry and Gjensidige Forsikring.

Due to the country's high latitude, there are large seasonal variations in daylight. From late May to late July, the sun never completely descends beneath the horizon. Which attracts many tourists around the world to see the "Land of the Midnight Sun". Tourists mainly visit Sognefjord, Norway's Largest Fjord, Pulpit Rock, one of the most photographed sites in Norway and of course the capital; Oslo.

Oslo is considered the business center of Norway. It is the country's largest city and the capital of Norway. The city is home to many of Norway's largest and most important companies, as well as several international organizations and research institutions. Additionally, the city is a popular tourist destination, known for its scenic location on the Oslo Fjord, its many museums and cultural attractions, and its vibrant nightlife and dining scene. Some of the most popular museums in Oslo are The Norwegian Museum of Cultural History, The Nobel Peace Center, The National Museum of Art, Architecture, and Design, The Munch Museum and The Vigeland Museum.
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