Greenplum Architecture and Data Modeling Training Course

This instructor-led live training in Malaysia (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 Malaysia (online or onsite) is designed for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively use 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 Malaysia (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 (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 training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process vast datasets efficiently by applying distributed computing principles.

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

Attendees will gain a deep practical understanding of how Spark applications operate on Kubernetes and how specific configuration choices at the application level directly impact performance, scalability, resource usage, and overall costs. The curriculum addresses critical optimisation areas, including executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, resolving small-file issues, and implementing efficient Parquet processing.

The course also tackles common pain points 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 practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and implement optimisation techniques for realistic ETL and machine learning use cases.

The core focus of the course is on practical decision-making: mastering the ability to identify bottlenecks, select the right tools, configure Spark effectively, and strike the right balance between performance and infrastructure cost efficiency.

This instructor-led, live training in Malaysia (online or onsite) is aimed at 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 datasets.
• Understand the differences between Apache Spark and Hadoop MapReduce and when to use each.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in Malaysia, delivered online or on-site, is designed for beginner to intermediate level system administrators looking to deploy, maintain, and optimize Spark clusters.

By the end of this session, participants will be capable of:

• Installing and configuring Apache Spark in various environments.
• Managing cluster resources and monitoring Spark applications.
• Optimizing the performance of Spark clusters.
• Implementing security measures and ensuring high availability.
• Debugging and troubleshooting common Spark issues.

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

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

• Utilize Spark alongside Python to analyze Big Data.
• Practice with exercises designed to mirror real-world applications.
• Employ diverse tools and techniques for big data analysis via PySpark.

This instructor-led, live training in Malaysia (online or on-site) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyse, and transform large and complex datasets.

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.

This live, instructor-led training in Malaysia dives deep into the Stratio platform, with a specific focus on leveraging Rocket and Intelligence modules via PySpark. Participants will gain mastery over data ingestion, transformation, and advanced analytics, while developing practical skills in loop management, UDF creation, and enterprise-grade data workflow orchestration.