Cloudera Developer for Spark and Hadoop, Finland

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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

Cloudera Developer for Spark and Hadoop Training Course in Finland

Finland is a country located in northern Europe. Helsinki is the capital and largest city of the country. The majority of the people are Finns but there is also a small Lapp population in Lapland, where the country is famous for the Northern Lights. Finland's national languages are Finnish and Swedish.

Known for its vast forests, lakes, and natural beauty, Finland is one of the world's largest producers of forest products, such as paper, pulp, and lumber. One of the world's largest sea fortresses Suomenlinna, Rovaniemi with the "White Nights", dogsled safaris and of course the Northern Lights are what makes Finland so popular for tourists. Finland is one of the best places in the world to see the Northern Lights and attracts millions of tourists during its seasons.

Finland is home to a thriving technology industry and is widely recognized as one of the world's leading technology hubs. Companies such as Nokia and Rovio (creator of the popular game Angry Birds) are based in Finland. Some of the key factors that have contributed to Finland's success in technology include; strong investment in research and development, a highly educated workforce and fundings.

Finland has a strong educational system, and is widely regarded as one of the world's most literate countries. In fact, Finland's literacy rate is one of the highest in the world, and its students consistently perform well in international tests of math and reading ability.

Also, as a pioneer in environmental sustainability, Finland is known for its efforts to reduce its carbon footprint and promote clean energy. This Nordic country is also famous for its unique and distinctive cultural heritage, including its traditional folk music and its elaborate traditional costumes.

Helsinki, Finland's capital city, is the country's business center. Helsinki is Finland's largest city, and it is home to many of the country's major corporations and organizations, including many of the country's leading technology firms. The city is also a commercial, trade, and financial center, as well as one of the busiest ports in the Nordic region.

Take advantage of our diverse IT course offerings, spanning programming, software development, business skills, data science, cybersecurity, cloud computing and virtualization. Our knowledgeable instructors will provide you with practical training and industry insights, delivered directly to your chosen venue in Finland.

Cloudera Developer for Spark and Hadoop Training in Finland

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Cloudera Developer for Spark and Hadoop Training in Finland

Prerequisites

Who Should Attend

What You Will Learn

Outline

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