Programming with Big Data in R Training Course

This instructor-led, live training in South Korea (online or onsite) is aimed at intermediate-level advanced R users who wish to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

By the end of this training, participants will be able to: create reusable functions, improve data workflows, debug and optimize code, and produce reproducible reports.

This instructor-led live training in South Korea (online or onsite) targets intermediate-level data scientists and engineers who want to apply Google Colab and Apache Spark for big data processing and analytics.

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

• Establish a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led, live training in South Korea (online or onsite) targets beginner-level data analysts who intend to use R programming to manipulate data, perform basic data analysis, and create compelling visualizations to gain insights.

By the end of this training, participants will be able to:

• Understand the basics of R Programming.
• Apply fundamental data science processes.
• Create visual representations of data.

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

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

Over the course of three days, this hands-on program equips participants with the skills to architect and refine high-performance data pipelines utilizing PySpark, Pandas, and Polars within Kubernetes environments.

Learners will gain a functional grasp of Spark execution mechanics on Kubernetes, exploring how application-level tuning directly impacts scalability, resource efficiency, and operational costs. The curriculum addresses critical optimization domains, such as executor sizing, memory management, dynamic allocation, partitioning strategies, shuffle mechanics, and resolving small-file issues for efficient Parquet processing.

Additionally, the course tackles common constraints associated with Pandas, such as memory bottlenecks and out-of-memory errors, while introducing Polars as a high-performance alternative for specific data-intensive tasks. Through practical exercises, participants will learn to diagnose performance and memory challenges, evaluate diverse configuration strategies, and implement optimization techniques for realistic ETL and machine learning workflows.

Central to the course is practical decision-making: mastering the ability to pinpoint bottlenecks, select the optimal tool, configure Spark effectively, and strike a balance between performance and infrastructure costs.

This instructor-led, live training in South Korea (online or onsite) is designed for engineers who wish to set up and deploy Apache Spark systems for processing very large amounts of data.

By the end of this training, participants will be able to:

• Install and configure Apache Spark.
• Quickly process and analyze very large data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

This instructor-led live training in South Korea (online or onsite) targets beginner to intermediate system administrators looking to deploy, maintain, and optimize Spark clusters.

By the end of the program, participants will be able to:

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

Join this instructor-led, live training session in South Korea to discover how to seamlessly integrate Python and Spark for efficient big data analysis, reinforced by practical, hands-on exercises.

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

• Utilize Spark in conjunction with Python for comprehensive Big Data analysis.
• Complete exercises designed to simulate real-world application cases.
• Employ a variety of tools and methodologies for big data analysis via PySpark.

This instructor-led, live training in South Korea (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

By the end of this training, participants will be able to:

• Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
• Understand the features, core components, and architecture of Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for big data processing.
• Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.