Vous avez déjà un compte client ORSYS
Se connecter
Vous êtes nouveau chez ORSYS
Créer un compte
Vous ne souhaitez pas créer de compte
Poursuivre sans me connecter

Course : Hadoop Cloudera developer, certification preparation (CCA175)

Practical course - 4d - 28h00 - Ref. HDZ

Hadoop Cloudera developer, certification preparation (CCA175)




This course will teach you the key concepts and expertise needed to integrate and store data in a Hadoop cluster using the latest techniques and tools. It prepares you for the certification "CCA Spark and Hadoop developer".


INTER
IN-HOUSE
CUSTOM

Practical course in person or remote class
Available in English on request

Ref. HDZ
  4d - 28h00
Contact us

Download in PDF format

Share this course by email




This course will teach you the key concepts and expertise needed to integrate and store data in a Hadoop cluster using the latest techniques and tools. It prepares you for the certification "CCA Spark and Hadoop developer".


Teaching objectives
At the end of the training, the participant will be able to:
Discover the Hadoop ecosystem
Understand the HDFS distributed file system and master MapReduce processing and code writing
Best practices for developing and implementing common algorithms
Optimize configurations and improve performance
Using Hive, Pig, Flume, Mahout and Sqoop for Hadoop ecosystem projects
Preparing for Cloudera certification

Intended audience
Project managers, developers, data scientists, and anyone wishing to understand development techniques using MapReduce in the Hadoop environment.

Prerequisites
Basic knowledge of an object-oriented programming language.

Certification
Following the course, you can take the Cloudera Certified Associate Spark and Hadoop Developer (CCA175) exam. This exam takes place outside the training course. The aim is to become a certified Cloudera expert in your company. To register, visit www.examslocal.com.

Practical details
Teaching methods
This big data training course includes 50% practical work over the 4 days.

Course schedule

1
Hadoop, HDFS and distributed processing on a Hadoop cluster

  • General introduction to Hadoop and its ecosystem.
  • Data processing.
  • HDFS: the Hadoop file system.
  • The components of a Hadoop cluster.
  • HDFS architecture. Using HDFS.
  • YARN architecture and working with YARN.

2
Spark basics

  • Introduction to Spark.
  • Start up and use the Spark console.
  • Introduction to Spark Datasets and DataFrames.
  • Operations on DataFrames.

3
DataFrame and schema manipulation, data analysis with queries

  • Create DataFrames from various data sources.
  • Saving DataFrames. DataFrame schemas.
  • Gluttonous and lazy execution of Spark.
  • Query DataFrames with expressions on named columns.
  • Grouping and aggregation queries.
  • Joints.

4
RDDs and table and view querying with Spark SQL

  • Spark's fundamental structure.
  • Transform data with Resilient Distributed Dataset (RDD).
  • Data aggregation with pair RDDs.
  • Query tables in Spark using SQL.
  • Query files and views.
  • The Spark Catalog API.

5
Working with Spark

  • Working with Spark datasets in Scala. The differences between datasets and DataFrames.
  • Create, load and save datasets. Operations on datasets.
  • Write, configure and run Spark applications.
  • Writing a Spark application. Compiling and launching an application. Deploying an application.
  • Spark applications web user interface. Configure application properties.
  • Distributed processing with Spark. A reminder of how Spark works with YARN.
  • Data partitioning in RDDs, queries, jobs, steps and tasks.

6
Persistence of distributed data

  • DataFrame and dataset persistence.
  • Persistence levels.
  • Persistent HHW

7
Iterative algorithms with Spark and introduction to Spark streaming

  • Other common uses of Spark.
  • Iterative algorithms in Spark. Machine learning with Spark.
  • Introduction to Spark streaming. Creating streaming DataFrames.
  • Transform DataFrames. Execute streaming requests.

8
Structured streaming with Kafka and operations on streaming DataFrames

  • Introduction. Receiving and sending Kafka messages.
  • Aggregate and join streaming DataFrames.
PARTICIPANTS
Project managers, developers, data scientists, and anyone wishing to understand development techniques using MapReduce in the Hadoop environment.

PREREQUISITES
Basic knowledge of an object-oriented programming language.

TRAINER QUALIFICATIONS
The experts leading the training are specialists in the covered subjects. They have been approved by our instructional teams for both their professional knowledge and their teaching ability, for each course they teach. They have at least five to ten years of experience in their field and hold (or have held) decision-making positions in companies.

ASSESSMENT TERMS
The trainer evaluates each participant’s academic progress throughout the training using multiple choice, scenarios, hands-on work and more.
Participants also complete a placement test before and after the course to measure the skills they’ve developed.

TEACHING AIDS AND TECHNICAL RESOURCES
• The main teaching aids and instructional methods used in the training are audiovisual aids, documentation and course material, hands-on application exercises and corrected exercises for practical training courses, case studies and coverage of real cases for training seminars.
• At the end of each course or seminar, ORSYS provides participants with a course evaluation questionnaire that is analysed by our instructional teams.
• A check-in sheet for each half-day of attendance is provided at the end of the training, along with a course completion certificate if the trainee attended the entire session.

TERMS AND DEADLINES
Registration must be completed 24 hours before the start of the training.

ACCESSIBILITY FOR PEOPLE WITH DISABILITIES
Do you need special accessibility accommodations? Contact Mrs. Fosse, Disability Manager, at psh-accueil@orsys.fr to review your request and its feasibility.