Data Warehousing: Concepts, Design, and Implementation Training Course

This instructor-led live training in Vietnam (online or onsite) targets advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling the complexities of modern big data applications.

Upon completing 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, execute complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and improve 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 Vietnam (online or onsite) is designed for 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 comprehensive 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 across various 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 Vietnam (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

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

• Set up 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) targets 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 course offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently manage large datasets by applying distributed computing principles.

This intensive three-day course is designed to help participants build and fine-tune high-efficiency data processing workloads by leveraging PySpark, Pandas, and Polars within Kubernetes-based environments.

Learners will gain a practical grasp of how Spark applications are executed on Kubernetes, exploring how specific application-level configuration choices impact overall performance, scalability, resource utilization, and operational costs. Key optimization topics covered include executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the challenges of small files, and efficient handling of Parquet data.

The curriculum also tackles frequent obstacles encountered when using Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a high-performance alternative for specific data processing tasks. Through hands-on practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and implement optimization techniques for realistic ETL and machine learning use cases.

Throughout the course, the focus remains on practical decision-making: equipping learners with the skills to identify bottlenecks, select the right tools, configure Spark effectively, and strike a balance between high performance and efficient infrastructure resource usage and cost management.

This instructor-led, live training in Vietnam (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system 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 live, instructor-led session in Vietnam—available online or on-site—targets system administrators at the beginner to intermediate level who intend to deploy, sustain, and optimize Spark clusters.

By the time this training concludes, participants will be equipped to:

• Install and configure Apache Spark in a variety of environments.
• Manage cluster resources and monitor the status of Spark applications.
• Optimize the performance of Spark clusters.
• Implement security controls and ensure high availability.
• Debug and troubleshoot typical Spark issues.

In this live, instructor-led training held in Vietnam, participants will explore how to combine Python and Spark for big data analysis while engaging in hands-on exercises.

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

• Use Spark with Python to analyze Big Data.
• Complete exercises that simulate real-world cases.
• Apply different tools and techniques for big data analysis using PySpark.

This instructor-led live training in Vietnam (online or onsite) targets developers who wish to integrate Spark, Hadoop, and Python for processing, analyzing, and transforming 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.