Programming with Big Data in R Training Course

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level advanced R users who wish to leverage R to build faster workflows, enhance code quality, and tackle more complex analytical tasks.

By the end of this training, participants will be able to: create reusable functions, improve data workflows, debug and optimise code, and produce reproducible reports.

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for intermediate-level data scientists and engineers who wish to leverage 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 analyse large datasets efficiently with Apache Spark.
• Visualise big data within a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led, live training in New Zealand (delivered online or on-site) is designed for beginner-level data analysts who wish to use R to manipulate data, perform foundational data analysis, and create compelling visualisations to derive insights.

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

• Understand the fundamentals of R programming.
• Apply core data science processes.
• Create visual representations of data.

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 building scalable data processing and Machine Learning workflows using PySpark. Participants will learn how Apache Spark functions within modern Big Data ecosystems and how to efficiently process large datasets using distributed computing principles.

This immersive three-day programme is dedicated to the construction and refinement of high-efficiency data processing workflows, leveraging PySpark, Pandas, and Polars within Kubernetes-based infrastructures.

Attendees will cultivate a hands-on grasp of how Spark applications function within Kubernetes, specifically examining how application-level configuration choices impact overall performance, scalability, resource utilisation, and operational expenditure. The curriculum addresses critical optimisation domains, including executor sizing, memory management, dynamic allocation, partitioning methodologies, shuffle mechanics, mitigation of small-file issues, and streamlined Parquet handling.

Furthermore, the course tackles typical challenges encountered with Pandas, such as memory constraints and out-of-memory exceptions, while presenting Polars as a high-performance alternative for specific data processing tasks. Through practical exercises, learners will diagnose performance and memory bottlenecks, evaluate various configuration strategies, and implement optimisation techniques within realistic ETL and machine learning contexts.

The core focus of the programme remains on practical decision-making: mastering the identification of bottlenecks, selecting the right tools, configuring Spark for optimal efficiency, and striking a balance between performance and infrastructure resource consumption and cost.

This instructor-led, live training in New Zealand (online or on-site) is intended for engineers who wish to set up and deploy the Apache Spark system to process extremely large volumes of data.

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

• Install and configure Apache Spark.
• Efficiently process and analyse very large data sets.
• 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 New Zealand—delivered online or onsite—targets beginner to intermediate system administrators who wish to deploy, maintain, and optimise Spark clusters.

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

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimise the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

In this instructor-led, live training in New Zealand, participants will learn to integrate Python and Spark to analyse big data, supported by practical, hands-on exercises.

Upon completion of this training, participants will be able to:

• Apply Spark alongside Python to analyse big data.
• Complete exercises that reflect real-world scenarios.
• Utilise various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in New Zealand (available online or on-site) is tailored for developers seeking to use and integrate Spark, Hadoop, and Python to process, analyse, and transform large and complex data sets.

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

• Set up the necessary environment to begin processing big data using Spark, Hadoop, and Python.
• Understand the key features, core components, and architecture of both Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for efficient big data processing.
• Explore tools within the Spark ecosystem, including Spark MLlib, Spark Streaming, Kafka, Sqoop, and Flume.
• Build collaborative filtering recommendation systems similar to those used by Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.