Data Warehousing: Concepts, Design, and Implementation Training Course

This instructor-led, live training in France (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 France (online or onsite) is tailored for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively use Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing 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 France (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

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

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led live training (available online or onsite) is designed for data engineers, analysts, and professionals seeking to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and how to effectively manage large datasets by applying distributed computing principles.

This intensive three-day workshop is dedicated to constructing and refining high-performance data-processing workloads using PySpark, Pandas, and Polars within Kubernetes environments.

Learners will gain a hands-on grasp of Spark execution mechanics on Kubernetes, discovering how specific configuration choices impact performance, scalability, resource efficiency, and operational costs. The curriculum delves into critical optimisation domains, such as executor sizing, memory management, dynamic allocation, partitioning logic, shuffle operations, mitigating small-file issues, and enhancing Parquet processing efficiency.

The training also tackles frequent obstacles associated with Pandas, such as memory constraints and out-of-memory crashes, while presenting Polars as a robust, high-performance solution for specific data tasks. Through practical exercises, attendees will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and implement optimisation techniques in realistic ETL and machine learning contexts.

Central to this course is practical decision-making: mastering the identification of bottlenecks, selecting the optimal tool, configuring Spark effectively, and balancing high performance against infrastructure resource usage and cost.

This instructor-led, live training in France (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 live, instructor-led training in France (conducted online or on-site) is designed for system administrators from beginner to intermediate levels who aim to deploy, maintain, and optimize Spark clusters.

By the end of the program, 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.

This instructor-led, live training in France guides participants in harnessing the power of Python and Spark to analyze big data, supported by engaging hands-on exercises.

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

• Utilize Spark and Python together for effective big data analysis.
• Complete exercises that simulate real-world professional challenges.
• Employ various tools and techniques for big data analytics via PySpark.

This instructor-led, live training in France (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.