Technologies / Data & Analytics

Spark Streaming with Python and Kafka

The training is devoted to the implementation of real-time data stream processing systems using Apache Spark Streaming, Python and Apache Kafka. The program guides participants through the process of building scalable solutions for analyzing streaming data, combining theory with practical implementation workshops. The class focuses on real-world scenarios of using streaming technologies in Big Data projects.

Issues

Spark Structured Streaming Architecture
Spark integration with Kafka
Stream transformations
Aggregations and windowing
Watermarking
Checkpointing
Performance optimization
Monitoring streams
Sink-i and output modes
Combining streams
Support for delayed data
Stream processing patterns

This training is part of the path:

Data Engineer

Benefits

The participant will gain practical skills in implementing data stream processing systems using Spark Streaming and Kafka
Will learn to design and implement scalable solutions for real-time data analysis
Will develop skills in optimizing and monitoring streaming systems
Will learn techniques for integrating various components of the Big Data ecosystem
Will gain the knowledge to consciously select appropriate processing patterns for specific use cases

Who is this training for?

Data engineers working with Big Data systems

Python programmers interested in stream processing

Data analysts in need of real-time analysis tools

Architects of Big Data solutions

Developers of applications that process large volumes of data

Big Data systems integration specialists

Data Scientists working with streaming data

Prerequisites

Knowledge of Python programming
Basic knowledge of Apache Spark
Understand the concept of stream processing
Experience in working with data

Training program

Integration with Apache Kafka
Stream processing model
Configuring the environment
Implementation of stream transforms
Operations on data streams
Aggregations and windowing

Support for delayed data

Watermarking and late data
Checkpointing and fault tolerance
Performance optimization
Monitoring of processing
Integration into the ecosystem
Sink-i and output modes
Integration with external systems

Good manufacturing practices

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

What are the prerequisites for this training?

For Spark Streaming with Python and Kafka we recommend: Knowledge of Python programming; Basic knowledge of Apache Spark; Understand the concept of stream processing.

What is the format and duration of this training?

The training lasts 1 day and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data engineers working with Big Data systems; Python programmers interested in stream processing; Data analysts in need of real-time analysis tools.