Data Warehousing: Concepts, Design, and Implementation Training Course

This instructor-led, live training in Australia (online or onsite) is tailored for 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.

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 Australia (online or onsite) is tailored for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively utilise Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimising 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 Australia (online or onsite) is designed for intermediate-level data scientists and engineers who wish to leverage Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be able to:

• Set up a big data environment using Google Colab and Spark.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led, live training (online or onsite) is aimed at 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 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 process large datasets efficiently by leveraging distributed computing principles.

This intensive three-day program is dedicated to the construction and optimisation of high-efficiency data processing workloads leveraging PySpark, Pandas and Polars within Kubernetes environments.

Attendees will cultivate a hands-on understanding of Spark application execution on Kubernetes, specifically examining how application-level configuration choices impact performance, scalability, resource utilisation, and operational costs. The curriculum addresses critical optimisation domains, such as executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the small-file problem, and efficient Parquet processing.

Furthermore, the course tackles prevalent challenges associated with Pandas, including 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 in realistic ETL and machine learning contexts.

Throughout the session, the primary focus remains on practical decision-making: identifying bottlenecks, selecting the most suitable tools, configuring Spark for maximum efficiency, and striking a balance between processing performance and infrastructure resource consumption and costs.

This instructor-led, live training in Australia (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 analyse 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 Australia, available online or onsite, is designed for system administrators with beginner to intermediate skills who aim to deploy, sustain, and optimize Spark clusters.

By the conclusion of this program, participants will have the capability to:

• Install and configure Apache Spark in various settings.
• Manage cluster resources and monitor Spark applications.
• Improve the performance of Spark clusters.
• Establish security controls and ensure high availability.
• Diagnose and troubleshoot standard Spark issues.

In this live, instructor-led training in Australia, participants will explore how to combine Python and Spark for big data analysis through practical, hands-on exercises.

Upon completing this training, participants will be equipped to:

• Utilize Spark alongside Python for comprehensive Big Data analysis.
• Engage with exercises that reflect real-world scenarios.
• Apply various tools and techniques for big data analysis via PySpark.

This instructor-led, live training in Australia (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.