Data Warehousing: Concepts, Design, and Implementation Training Course

This instructor-led, live training in Denmark (available online or onsite) is designed for advanced data professionals aiming to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that address the complexities of contemporary big data applications.

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

• Develop a comprehensive understanding of Iceberg’s architecture, encompassing metadata management and file layout strategies.
• Optimize Iceberg configurations for peak performance across various environments and integrate it seamlessly with multiple data processing engines.
• Oversee large-scale Iceberg tables, execute complex schema modifications, and manage partition evolution effectively.
• Apply advanced techniques to enhance query performance and improve data scan efficiency for extensive datasets.
• Establish mechanisms to maintain data consistency, manage transactional guarantees, and address failures within distributed systems.

This live, instructor-led training in Denmark (online or onsite) is tailored for beginner-level data professionals aiming to gain the proficiency required to utilize Apache Iceberg for managing large-scale datasets, maintaining data integrity, and enhancing data processing workflows.

By the conclusion of this session, participants will be capable of:

• Gaining a deep insight into the architecture, features, and benefits of Apache Iceberg.
• Understanding table formats, partitioning mechanisms, schema evolution, and time travel capabilities.
• Installing and configuring Apache Iceberg in various settings.
• Creating, managing, and manipulating Iceberg tables.
• Comprehending the process of migrating data from other table formats to Iceberg.

This live, instructor-led session, conducted in Denmark (either online or on-site), is specifically tailored for intermediate-level data scientists and engineers seeking to harness Google Colab and Apache Spark for efficient big data processing and analytics.

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

• Establish a big data environment leveraging Google Colab and Spark.
• Effectively process and analyze extensive datasets using Apache Spark.
• Visualize big data insights within a collaborative workspace.
• Connect Apache Spark with cloud-based toolsets.

This live, instructor-led training (conducted online or on-site) is designed for data engineers, data analysts, and data professionals seeking to utilize Databricks and PySpark to construct scalable data pipelines and transition existing SQL workflows.

This training delivers a practical introduction to building scalable data processing and Machine Learning workflows using PySpark. Participants learn how Apache Spark operates within modern Big Data ecosystems and how to efficiently process large datasets using distributed computing principles.

This intensive three-day workshop is designed to equip professionals with the skills to construct and refine high-performance data-processing workloads leveraging PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Attendees will gain a functional understanding of how Spark applications operate on Kubernetes, focusing on how configuration choices at the application level directly impact performance, scalability, resource utilisation, and operational costs. The curriculum addresses critical optimisation domains, such as executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the impact of small-file issues, and efficient Parquet handling.

Additionally, the course 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 practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and implement optimisation strategies for realistic ETL and machine learning use cases.

The core focus of the course remains on practical decision-making: mastering the ability to pinpoint bottlenecks, select the most appropriate tooling, configure Spark effectively, and achieve a balance between performance and infrastructure resource consumption and cost.

This instructor-led, live training in Denmark (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 instructor-led, live training on Denmark—delivered online or onsite—is designed for beginner to intermediate-level system administrators looking to deploy, maintain, and optimize Spark clusters.

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

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

In this live, instructor-led training held in Denmark, participants will explore how to combine Python and Spark for big data analysis, supported by hands-on practical exercises.

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

• Apply Spark and Python to analyze big data effectively.
• Engage in practical exercises that simulate real-world conditions.
• Employ various tools and techniques for big data analysis using PySpark.

This instructor-led live training (online or onsite) is tailored for developers seeking to utilize and integrate Spark, Hadoop, and Python to process, analyze, and transform large, complex datasets.

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

• Set up the required 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 those found on Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.