Greenplum Architecture and Data Modeling Training Course

This instructor-led live training in South Korea (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.

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

• Gain a deep understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance across various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led, live training in South Korea (online or onsite) is aimed at beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.

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

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in different environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

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 (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.