Greenplum Architecture and Data Modeling Training Course

This instructor-led, live training in Taiwan (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 an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in 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 Taiwan (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 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 Taiwan (online or onsite) is tailored for intermediate-level data scientists and engineers who wish to utilize Google Colab and Apache Spark for big data processing and analytics.

By the conclusion 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 (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 using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by applying distributed computing principles.

This intensive three-day course is designed to help you build and fine-tune high-performance data processing workloads using PySpark, Pandas, and Polars within Kubernetes-based environments.

Participants will gain a practical understanding of how Spark applications operate on Kubernetes and how specific configuration decisions impact performance, scalability, resource utilization, and overall cost. The curriculum covers critical optimization areas such as executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle behavior, mitigating small-file issues, and optimizing Parquet processing.

The course also tackles common challenges associated with 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 exercises, participants will learn to diagnose performance and memory issues, evaluate different configuration strategies, and apply optimization techniques to realistic ETL and machine learning scenarios.

Throughout the course, the primary focus is on practical decision-making: identifying bottlenecks, selecting the right tools, configuring Spark effectively, and balancing performance against infrastructure resource usage and cost.

This instructor-led, live training in Taiwan (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.

Offered by an instructor in Taiwan (either online or onsite), this live training is designed for beginner to intermediate-level system administrators seeking to deploy, maintain, and optimize Spark clusters.

Once this training concludes, participants will be equipped to:

• Install and set up Apache Spark in various environments.
• Administer cluster resources and monitor Spark applications.
• Improve the performance of Spark clusters.
• Deploy security measures and ensure high availability.
• Debug and troubleshoot typical Spark problems.

During this instructor-led live training in Taiwan, participants will master the integration of Python and Spark for big data analysis through hands-on exercises.

Upon completion, participants will be able to:

• Apply Spark and Python for big data analysis.
• Solve exercises modeled after real-world scenarios.
• Utilize various tools and techniques for big data analysis with PySpark.

This instructor-led, live training in Taiwan (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.