Apache Iceberg Fundamentals Training Course

This instructor-led, live training in Germany (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 can navigate the complexities of modern big data applications.

By the conclusion 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 diverse 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 optimize 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 Germany (online or onsite) targets intermediate-level data scientists and engineers aiming to utilize Google Colab and Apache Spark for big data processing and analytics.

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

• Establishing a big data environment with Google Colab and Spark.
• Processing and analyzing large datasets effectively using Apache Spark.
• Visualizing big data in a collaborative setting.
• Connecting Apache Spark with cloud-based utilities.

Participants completing this instructor-led live training in Germany will acquire a practical, real-world perspective on Big Data, encompassing its related technologies, methodologies, and tools.

Learners will have the chance to apply their knowledge through hands-on exercises. Group collaboration and instructor feedback are key elements of the learning experience.

The course begins by introducing fundamental Big Data concepts, then advances to the programming languages and methodologies essential for Data Analysis. It concludes by exploring the tools and infrastructure necessary for Big Data storage, Distributed Processing, and Scalability.

Directed by an instructor, this live training (available online or onsite) targets data engineers, analysts, and professionals seeking to utilize Databricks and PySpark to develop scalable data pipelines and transition existing SQL workflows to a modern stack.

This training delivers a practical introduction to developing scalable data processing and machine learning workflows using PySpark. Participants will understand how Apache Spark fits into modern big data ecosystems and how to leverage distributed computing principles for efficient large-scale data processing.

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

Learners will gain a robust operational understanding of how Spark applications are executed on Kubernetes. They will explore how strategic application-level configuration decisions directly impact performance, scalability, resource efficiency, and overall cost. The curriculum addresses critical optimisation domains, such as executor sizing, memory allocation, dynamic resource allocation, partitioning strategies, shuffle mechanics, mitigating small-file issues, and maximising Parquet processing efficiency.

The course further tackles common operational challenges encountered with Pandas, such as memory constraints and out-of-memory exceptions, 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 strategies, and implement optimisation techniques in realistic ETL and machine learning contexts.

Throughout the training, the focus remains on practical decision-making: mastering the ability to identify performance bottlenecks, select the most suitable tool, configure Spark effectively, and balance performance gains against infrastructure resource consumption and cost.

This live, instructor-led training in Germany (delivered online or onsite) is tailored for engineers aiming to configure and deploy Apache Spark systems for the processing of massive data volumes.

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

• Install and configure Apache Spark.
• Swiftly process and analyze large-scale datasets.
• Identify the key differences between Apache Spark and Hadoop MapReduce, and determine which tool to apply in specific contexts.
• Integrate Apache Spark with external machine learning tools.

This live, instructor-led session in Germany (delivered online or on-site) targets system administrators with beginner to intermediate proficiency who intend to deploy, maintain, and optimize Spark clusters.

Upon completion, participants will be capable of:

• Installing and configuring Apache Spark in various settings.
• Managing cluster resources and monitoring Spark applications.
• Improving the performance of Spark clusters.
• Implementing security controls and ensuring high availability.
• Debugging and resolving standard Spark issues.

In this instructor-led live training in Germany, participants will learn how to leverage Python and Spark together to analyze big data, supported by practical hands-on exercises.

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

• Use Spark alongside Python to analyze big data.
• Complete exercises that simulate real-world cases.
• Employ diverse tools and techniques for big data analysis using PySpark.

This live, instructor-led training session, delivered either online or onsite, is tailored for developers looking to harness the power of Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

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

• Establish the necessary environment to begin processing big data using Spark, Hadoop, and Python.
• Comprehend the features, core components, and architecture of Spark and Hadoop.
• Apply knowledge on integrating Spark, Hadoop, and Python for efficient big data processing.
• Explore the tools within the Spark ecosystem, such as Spark MlLib, Spark Streaming, Kafka, Sqoop, and Flume.
• Construct collaborative filtering recommendation systems similar to those found on Netflix, YouTube, Amazon, Spotify, and Google.
• Implement Apache Mahout to scale machine learning algorithms effectively.