Course : Spark Java, developing applications for Big Data
Practical course - 3d
- 21h00 - Ref. SPK
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 | Master the fundamental concepts of Spark |
| Developing applications with Spark Streaming |
| Setting up a Spark cluster |
| Exploiting data with Spark SQL |
| A first approach to machine learning |
Intended audience
Project managers, data scientists, developers, architects.
Prerequisites
Good knowledge of Java. Knowledge of big data.
Practical details
Hands-on work
Practical application of the concepts covered in the course using the Java language.
Course schedule
1 Introducing Apache Spark
- History of the framework.
- The different versions of Spark (Scala, Python and Java).
- Comparison with the Apache Hadoop environment.
- The different Spark modules.
Hands-on work
Install and configure Spark. Run a first example with word counting.
2 Programming with Resilient Distributed Datasets (RDD)
- RDD presentation.
- Create, manipulate and reuse RDDs.
- Accumulators and broadcast variables.
- Use partitions.
Hands-on work
Handling different datasets with RDDs and using the API provided by Spark.
3 Handling structured data with Spark SQL
- SQL, DataFrames and datasets.
- The different types of data sources.
- Interoperability with RDDs.
- Spark SQL performance.
- JDBC/ODBC server and Spark SQL CLI.
Hands-on work
Dataset manipulation via SQL queries. Connection with an external database via Java DataBase Connectivity (JDBC) Open Database Connectivity (OBDC).
4 Spark on a cluster
- The different types of architecture: standalone, Apache Mesos or Hadoop YARN.
- Set up a cluster in standalone mode.
- Pack an application with its dependencies.
- Deploy applications with Spark-submit.
- Size a cluster.
Hands-on work
Setting up a Spark cluster.
5 Real-time analysis with Spark Streaming
- Operating principle.
- Introducing Discretized Streams (DStreams).
- The different types of source.
- API handling.
- Comparison with Apache Storm.
Hands-on work
Log consumption with Spark Streaming.
6 Graph manipulation with GraphX
- Introducing GraphX.
- The different operations.
- Create graphs.
- Vertex and Edge RDD.
- Presentation of different algorithms.
Hands-on work
Handling the GraphX API through various examples.
7 Machine learning with Spark
- Introduction to machine learning.
- The different classes of algorithms.
- Introducing SparkML and MLlib.
- Implementing different algorithms in MLlib.
Hands-on work
Using SparkML and MLlib.
Customer reviews
4,5 / 5
Customer reviews are based on end-of-course evaluations. The score is calculated from all evaluations within the past year. Only reviews with a textual comment are displayed.
ARBOUCHI SOUFIANE E.
15/06/26
5 / 5
The trainer (Fabrice) provided excellent explanations, and the case studies on production conditions were of a very high standard (deployment, debugging, design). The practical sessions were technically challenging and truly interesting.
GAËL M.
15/06/26
5 / 5
Fabrice is a good listener, very friendly; he knows what he’s doing and you can tell he enjoys sharing his knowledge. I felt as though I was back at uni, with a good lecturer.Technically, I think there was TOO much content; the PDF is slow to open – it would have been better to provide an A4-sized PDF. The content itself is still interesting, though perhaps a bit too repetitive, with not enough graphs and diagrams. The practicals are interesting, though there are a few typos.
SANTOS NELSON D.
15/06/26
5 / 5
We just had to pick up the pace at the end
Publication date : 09/30/2025
PARTICIPANTS
Project managers, data scientists, developers, architects.
PREREQUISITES
Good knowledge of Java. Knowledge of big data.
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.
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.
Project managers, data scientists, developers, architects.
PREREQUISITES
Good knowledge of Java. Knowledge of big data.
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.
• 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.
This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.
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This programme is an original creation, developed by the teaching teams at ORSYS Formation. Any reproduction, representation, adaptation or use, in whole or in part, without the prior written authorisation of ORSYS, is strictly prohibited. ORSYS reserves the right to take any action necessary to protect its intellectual property rights.