Technologies / Data & Analytics

Python and Spark for Big Data (PySpark)

Advanced training combining the capabilities of Python and Apache Spark in the context of Big Data processing. The workshop program covers both the theoretical foundations of distributed data processing and the practical aspects of implementing Big Data solutions. Participants will learn the tools and techniques necessary to efficiently process large data sets in a distributed environment, with a focus on performance optimization and scalability.

Issues

Apache Spark architecture
Distributed processing
Transformations and actions
Spark SQL and DataFrames
Machine Learning at Spark
Spark Streaming
Performance optimization
Application monitoring
Cluster management
Integration of data sources
Graph processing
Best practices in Big Data

This training is part of the path:

Data Engineer

Benefits

The participant will be able to design and implement efficient Big Data solutions using the Apache Spark and Python ecosystem
He or she will gain the ability to optimize data processing in a distributed environment and effectively manage cluster resources
To apply advanced analytical techniques on large data sets, including machine learning and streaming analytics
Will learn the practical aspects of deploying and maintaining Spark applications in a production environment
Will know how to monitor and optimize the performance of Big Data applications
Will develop skills in integrating different data sources into Big Data solutions

Who is this training for?

Data engineers working with Big Data

Data Scientists processing large data sets

Architects of Big Data solutions

Analytical application developers

DevOps Engineers involved in data processing

Business Intelligence Specialists

Prerequisites

Practical knowledge of Python
Basic knowledge of Big Data
Experience in data processing
Knowledge of SQL

Training program

RDD and DataFrames
Transformations and actions

PySpark data processing
Operations on DataFrames
Windowing and partitioning

Integration with external sources
Machine Learning with MLlib

Spark SQL

Implementation and monitoring
Cluster configuration
Performance monitoring
Debugging the application
Optimization of resources

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

Who is the Python and Spark for Big Data (PySpark) training for?

The target audience includes data engineers working with Big Data, Data Scientists processing large data sets, and architects of Big Data solutions. Level: advanced.

How long is the Python and Spark for Big Data (PySpark) training?

The training lasts 3 days. It is delivered online or on-site.

Will I receive a certificate?

Yes — every participant receives a completion certificate confirming acquired competencies. EITT is ISO 9001 certified.

Can this training be conducted for a closed group?

Yes — we offer dedicated closed trainings for companies. We customize the program to your team's needs. Contact us for an individual quote.