Programming with Big Data in R Training Course

This instructor-led, live training in the Netherlands (online or onsite) is aimed at intermediate-level advanced R users who wish to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

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

This guided, live training in the Netherlands (online or on-site) is tailored for intermediate-level data scientists and engineers who intend to use Google Colab and Apache Spark for big data processing and analytics.

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

• Establish a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led, live training in the Netherlands (online or onsite) targets beginner-level data analysts who wish to use R programming to manipulate data, perform basic data analysis, and create compelling visualizations for insights.

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

• Understand the basics of R Programming.
• Apply fundamental 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 course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Participants will gain insights 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 workshop is dedicated to the construction and optimisation of high-performance data-processing workloads within Kubernetes environments, utilising PySpark, Pandas, and Polars.

Learners will gain a hands-on grasp of how Spark applications function on Kubernetes, exploring how specific configuration choices impact performance, scalability, resource usage, and overall costs. Key optimisation topics covered include executor sizing, memory management, dynamic allocation, partitioning methods, shuffle operations, the challenge of small files, and efficient Parquet handling.

The curriculum also tackles frequent hurdles encountered with Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a high-speed alternative for specific data tasks. Through practical exercises, attendees will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and implement optimisation strategies in realistic ETL and machine learning contexts.

A core focus of the course is on practical decision-making: equipping you with the skills to identify bottlenecks, choose the right tools, configure Spark effectively, and strike a balance between performance and infrastructure resource consumption and cost.

This instructor-led live training (online or onsite) targets engineers who wish to set up and deploy Apache Spark systems for processing very large datasets.

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 the Netherlands (available online or on-site) is designed for system administrators ranging from beginner to intermediate level who aim to deploy, maintain, and fine-tune Spark clusters.

Following the training, participants will have the capability to:

• Deploy and configure Apache Spark in various environments.
• Manage cluster resources while monitoring Spark applications.
• Improve the performance efficiency of Spark clusters.
• Apply security measures and ensure high availability.
• Diagnose and fix frequent Spark-related issues.

In this instructor-led, live training in the Netherlands, participants will learn how to combine Python and Spark to analyse big data while completing hands-on exercises.

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

• Use Spark with Python to analyse big data.
• Work on exercises that reflect real-world cases.
• Employ different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in the Netherlands (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, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.