Data Warehousing: Concepts, Design, and Implementation Training Course

This instructor-led live training in Greece (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.

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

• Gain an in-depth 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 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 Greece (online or onsite) is designed for beginner-level data professionals seeking to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.

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

• Gain a comprehensive understanding of Apache Iceberg's architecture, features, and advantages.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg across various 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 Greece (online or onsite) is designed for intermediate-level data scientists and engineers seeking to apply Google Colab and Apache Spark to big data processing and analytics.

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

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

This instructor-led live training (online or onsite) is designed for data engineers, analysts, and professionals who wish to utilize Databricks and PySpark to build scalable data pipelines and migrate current SQL workflows.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows utilizing PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This intensive three-day workshop is dedicated to designing and fine-tuning high-efficiency data-processing workloads utilizing PySpark, Pandas, and Polars within Kubernetes-based environments.

Learners will gain a hands-on understanding of how Spark applications operate on Kubernetes and how application-level configuration choices impact performance, scalability, resource utilization, and overall cost. The curriculum explores critical optimization domains, including executor sizing, memory management, dynamic allocation, partitioning methodologies, shuffle mechanics, mitigating small-file issues, and optimizing Parquet processing.

The training also tackles frequent obstacles encountered when using 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 strategies, and implement optimization techniques in realistic ETL and machine learning contexts.

The primary focus of the course is on practical decision-making: equipping participants with the ability to identify performance bottlenecks, select the right tools, configure Spark efficiently, and strike a balance between performance and infrastructure resource costs.

This instructor-led live training in Greece (online or on-site) is aimed at engineers who wish to set up and deploy an 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 in Greece (available online or onsite) is designed for system administrators with beginner to intermediate proficiency who aim to deploy, maintain, and optimize Spark clusters.

Upon completion, participants will be equipped to:

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

In this live, instructor-led training in Greece, participants will master the integration of Python and Spark to analyze big data through practical, hands-on exercises.

Upon completing this training, participants will be equipped to:

• Apply Spark alongside Python for Big Data analysis.
• Engage in exercises that replicate real-world scenarios.
• Leverage diverse tools and techniques for big data analysis using PySpark.

This instructor-led live training in Greece (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.