Cloudera Developer for Spark and Hadoop, Germany

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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 Germany

The Federal Republic of Germany is the second most populous country in Europe and is located in Central Europe. The official language of the country is German. Germany is one of the richest countries in the world. The main exports of the country include motor vehicles and iron and steel products.

Here are some fun facts about Germany:
The fairy tale writer, the Brothers Grimm, came from Germany and wrote many famous stories such as Cinderella, Snow White, and Sleeping Beauty.
Germany is home to the largest theme park in Europe, the Europa-Park.
The famous composer Ludwig van Beethoven was born in Germany.
The Autobahn, the German highway system, is known for having no general speed limit.

Berlin was divided by the Berlin Wall from 1961 to 1989. Known for its street art, Berlin has many colorful murals and graffiti throughout the city. Also, Berlin is home to many famous museums, such as the Pergamon Museum and the Museum Island. Many clubs and bars stay open until the early hours of the morning in this big city.

Another popular city is Munich, which is famous for its Oktoberfest beer festival that attracts millions of visitors every year. Munich is also home to many historic buildings, including Nymphenburg Palace and the Marienplatz town square.

The country's capital and largest city is Berlin, however Frankfurt is considered to be the business and financial center of Germany. It is home to the Frankfurt Stock Exchange, the European Central Bank, and many other financial institutions. Because of its central location within Europe and its status as a major financial hub, Frankfurt is often referred to as the "Mainhattan," a play on the city's name and its association with the Manhattan financial district in New York City.

Frankfurt is also a major transportation hub, with the largest airport in Germany and one of the largest in Europe, Frankfurt Airport. Additionally, it is a popular destination for tourists, with its historic city center, beautiful parks, and vibrant cultural scene.

Some of the top German technology companies like Siemens AG, Bosch, SAP SE, Deutsche Telekom, Daimler AG and Volkswagen has business centers in Frankfurt. The country has a strong tradition of engineering and innovation, and is home to many other world-class technology companies and research institutions.

Tailored to meet the specific needs of Germany, Bilginç IT Academy combines cutting-edge training methodologies with our comprehensive range of Certification Exam preparation courses and accredited corporate training programs. Experience a transformative approach to IT training that will redefine your expectations.

Cloudera Developer for Spark and Hadoop Training in Germany

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Cloudera Developer for Spark and Hadoop Training in Germany

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