Greenplum Architecture and Data Modeling Training Course

This instructor-led live training in the UK (online or on-site) is designed for advanced data professionals aiming to optimise data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can navigate the complexities of modern big data applications.

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

• Gain a deep 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 optimise 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 the UK (delivered online or onsite) is intended for beginner-level data professionals aiming to gain the knowledge and skills needed to effectively leverage Apache Iceberg for managing large-scale datasets, maintaining data integrity, and streamlining 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 the UK (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 (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 intensive three-day practical course is dedicated to constructing and refining high-performance data processing workloads by leveraging PySpark, Pandas and Polars within Kubernetes-based environments.

Learners will cultivate a practical grasp of Spark application execution on Kubernetes, focusing on how application-level configuration decisions directly impact performance, scalability, resource utilisation and overall costs. Key optimisation topics covered include executor sizing, memory management, dynamic allocation, partitioning strategies, shuffle mechanics, mitigating the small-file issue and efficient Parquet handling.

The curriculum also tackles common 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 hands-on 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 course, the primary focus remains on practical decision-making: identifying bottlenecks, selecting the most suitable tools, configuring Spark for efficiency and striking a balance between performance, infrastructure resource usage and cost.

This instructor-led, live training the UK (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.

This live, instructor-led training in the UK (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 the UK, 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 instructor-led live training, conducted in the UK (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 the UK 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.