Greenplum Architecture and Data Modeling Training Course

This instructor-led, live training in Italy (online or onsite) is designed for advanced-level data professionals who want to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling the complexities of modern big data applications.

Upon completion 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 across 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 Italy (online or onsite) is tailored for beginner-level data professionals who wish 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.

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 Italy (online or onsite) is intended for intermediate-level data scientists and engineers seeking to utilize Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be capable of:

• Establishing a big data environment using Google Colab and Spark.
• Efficiently processing and analyzing large-scale datasets with Apache Spark.
• Visualizing big data within a collaborative framework.
• Integrating 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 utilize Databricks and PySpark for building scalable data pipelines and migrating 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 learn to process extensive datasets efficiently by leveraging distributed computing principles.

This intensive three-day workshop is dedicated to engineering and refining high-performance data-processing pipelines that leverage PySpark, Pandas, and Polars within Kubernetes environments.

Learners will gain a hands-on grasp of how Spark applications operate on Kubernetes, focusing on how configuration choices at the application level directly impact performance, scalability, resource efficiency, and overall cost. The curriculum covers essential optimization topics such as executor sizing, memory management, dynamic allocation, partitioning strategies, shuffle mechanics, mitigating the small-file issue, and efficient handling of Parquet data.

The program also tackles frequent hurdles encountered with Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a high-speed alternative for specific data tasks. Through practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and apply optimization strategies to real-world ETL and machine learning use cases.

The core focus remains on practical decision-making: equipping professionals to pinpoint bottlenecks, choose the right tools, configure Spark effectively, and strike a balance between performance and infrastructure resource consumption and cost.

This instructor-led, live training in Italy (online or onsite) is designed for engineers who want to set up and deploy an Apache Spark system for processing vast 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 datasets.
• Understand the distinctions between Apache Spark and Hadoop MapReduce, and identify when to use each.
• Integrate Apache Spark with other machine learning tools.

This live, instructor-led training in Italy—delivered either online or onsite—targets system administrators at beginner to intermediate levels who are looking to deploy, maintain, and optimize Spark clusters.

By the end of the program, participants will be able 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 instructor-led, live training in Italy, participants will discover how to leverage Python and Spark together for big data analysis through practical, hands-on exercises.

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

• Analyze Big Data effectively using the integration of Spark and Python.
• Engage with exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Italy (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.