Apache Spark Training in Cardiff

This instructor-led, live training in Cardiff (online or onsite) is designed for intermediate-level data scientists and engineers seeking to utilize Google Colab and Apache Spark for big data processing and analytics.

By the end of this session, attendees will be able to:

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

This live, instructor-led training in Cardiff focuses on the Stratio platform, specifically the Rocket and Intelligence modules integrated with PySpark. Participants will gain proficiency in data ingestion, transformation, and advanced analytics, while acquiring practical skills in loops, UDFs, and enterprise data workflows.

This instructor-led live training, conducted in Cardiff (either online or on-site), targets developers aiming 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 Cardiff (delivered online or onsite) targets system administrators at beginner to intermediate levels who are looking to deploy, maintain, and optimise Spark clusters.

Upon completion of this course, participants will be equipped to:

• Install and configure Apache Spark across diverse environments.
• Oversee cluster resources and monitor Spark applications.
• Enhance the performance of Spark clusters.
• Apply security protocols and guarantee high availability.
• Diagnose and resolve frequent Spark issues.

In this instructor-led, live training in Cardiff, participants will learn how to utilise Python and Spark in tandem to analyse big data, supported by hands-on exercises.

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

• Learn how to use Spark with Python to analyse Big Data.
• Work on exercises that mirror real-world scenarios.
• Use different tools and techniques for big data analysis using PySpark.

This live, instructor-led training (available online or on-site) is tailored for data engineers, analysts, and professionals aiming to utilise Databricks and PySpark to build scalable data pipelines and migrate established SQL workflows.

This course provides a practical introduction to building scalable data processing and Machine Learning workflows using PySpark. Learners will explore how Apache Spark operates within modern Big Data ecosystems and how to efficiently process large datasets using distributed computing principles.

This three-day practical workshop is dedicated to constructing and refining high-efficiency data processing workloads by leveraging PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Learners will cultivate a working knowledge of Spark application execution on Kubernetes, gaining insight into how application-level configuration choices impact performance, scalability, resource usage, and expenditure. The curriculum encompasses critical optimisation domains, including executor sizing, memory distribution, dynamic allocation, partitioning methodologies, shuffle dynamics, small-file issues, and streamlined Parquet processing.

Additionally, the programme addresses typical hurdles associated with Pandas, such as memory constraints and out-of-memory failures, while presenting Polars as a high-performance alternative for specific data processing tasks. Through practical exercises, participants will learn to identify 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 pinpoint bottlenecks, choose suitable tools, configure Spark effectively, and strike a balance between performance, infrastructure resource consumption, and cost.

This instructor-led, live training Cardiff (online or onsite) is designed for engineers looking to set up and deploy Apache Spark systems for processing very large data volumes.

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

• Install and configure Apache Spark.
• Swiftly process and analyse very large datasets.
• Distinguish between Apache Spark and Hadoop MapReduce, identifying the most suitable tool for specific tasks.
• Integrate Apache Spark with other machine learning tools.