Programming with Big Data in R Training Course

This instructor-led, live training in Portugal (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 instructor-led, live training in Portugal (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 in Portugal (online or onsite) is aimed at 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) targets data engineers, data analysts, and data professionals aiming to build scalable data pipelines using Databricks and PySpark, as well as those seeking to migrate existing SQL workflows.

This course offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently handle large datasets by applying distributed computing principles.

This intensive three-day workshop is dedicated to constructing and refining high-efficiency data-processing workloads that leverage PySpark, Pandas, and Polars within Kubernetes-based environments.

Learners will gain a practical insight into the execution mechanics of Spark applications on Kubernetes, exploring how application-level configuration choices directly impact performance, scalability, resource consumption, and overall cost. Key optimisation topics include executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the small-file problem, and the efficient handling of Parquet data.

The curriculum also tackles frequent 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 interactive 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.

The core focus of the course remains on practical decision-making: mastering the ability to identify performance bottlenecks, choose the right tools, configure Spark effectively, and strike a balance between processing speed and infrastructure resource expenditure.

This instructor-led, live training in Portugal (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.

This live, instructor-led training session in Portugal (delivered online or on-site) is designed for beginner to intermediate system administrators looking to deploy, sustain, and optimize Spark clusters.

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

• Install and configure Apache Spark in a variety of environments.
• Manage cluster resources and keep track of Spark application performance.
• Improve the efficiency of Spark clusters.
• Apply security standards and maintain high availability.
• Diagnose and fix frequent Spark-related issues.

In this instructor-led, live training in Portugal, participants will gain proficiency in using Python and Spark together for big data analysis through hands-on exercises.

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

• Apply Spark and Python for in-depth Big Data analysis.
• Complete exercises that reflect real-world use cases.
• Utilise diverse tools and techniques for big data analysis with PySpark.

This instructor-led, live training in Portugal (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.