PySpark Training in Uzbekistan

This live instructor-led training in Uzbekistan explores the Stratio platform, with a specific focus on the Rocket and Intelligence modules using PySpark. Participants will develop proficiency in data ingestion, transformation, and advanced analytics, acquiring practical skills in loops, UDFs, and enterprise-grade data workflows.

In this live, instructor-led training held in Uzbekistan, participants will explore the integration of Python and Spark for big data analysis, supported by interactive, hands-on exercises.

By the conclusion of this program, participants will be able to:

• Effectively use Spark and Python together to analyze big data.
• Complete exercises that simulate real-world professional cases.
• Leverage a variety of tools and techniques for big data analysis using PySpark.

This instructor-led, live training (online or onsite) is designed for data engineers, analysts, and professionals seeking to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.

Renowned for its clear syntax and code readability, Python is a high-level programming language. Spark serves as a powerful engine for processing big data, enabling efficient querying, analysis, and transformation. PySpark bridges the two, allowing users to interface Spark with Python.

Target Audience: This course is designed for intermediate-level banking professionals who are already familiar with Python and Spark and wish to enhance their expertise in big data processing and machine learning.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Participants will learn how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently manage large datasets using distributed computing principles.

This intensive three-day course centers on the construction and optimization of high-efficiency data processing workflows utilizing PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Attendees will acquire a hands-on grasp of Spark application execution on Kubernetes, exploring how configuration choices at the application level directly impact performance, scalability, resource utilization, and operational costs. Key optimization domains covered include executor sizing, memory distribution, dynamic allocation mechanisms, partitioning methodologies, shuffle dynamics, resolving small-file bottlenecks, and the efficient handling of Parquet files.

The curriculum also tackles typical obstacles encountered when leveraging Pandas, such as memory constraints and out-of-memory errors, while presenting Polars as a high-performance alternative for specific processing tasks. Through practical exercises, participants will learn to diagnose performance and memory issues, evaluate various configuration strategies, and implement optimization techniques in realistic ETL and machine learning contexts.

The course places a strong emphasis on practical decision-making: equipping learners with the ability to pinpoint bottlenecks, select the right tools, configure Spark effectively, and strike a balance between performance and infrastructure resource consumption and cost.