Greenplum Architecture and Data Modeling Training Course

This instructor-led, live training in Argentina (online or onsite) is designed for advanced-level data professionals seeking to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling 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 Argentina (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 Argentina (online or onsite) is designed for intermediate-level data scientists and engineers who intend 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 (online or onsite) is aimed at data engineers, data analysts, and data professionals who wish to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.

This training offers a hands-on introduction to creating scalable data processing and Machine Learning workflows using PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently process large datasets by applying distributed computing principles.

Designed as a three-day immersive experience, this course empowers learners to construct and fine-tune high-efficiency data-processing pipelines utilizing PySpark, Pandas, and Polars within Kubernetes-based architectures.

Learners will cultivate a practical command of how Spark applications function on Kubernetes, gaining insight into how specific configuration choices at the application level directly impact performance, scalability, resource utilization, and operational costs. Key optimization domains covered include executor sizing, memory management, dynamic allocation, partitioning logic, shuffle dynamics, mitigating small-file issues, and streamlined Parquet processing.

The curriculum also tackles prevalent challenges associated with Pandas, such as memory constraints and out-of-memory failures, while presenting Polars as a high-performance alternative for specific workloads. Through interactive, hands-on 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.

Throughout the course, the primary focus remains on practical decision-making: mastering the ability to identify bottlenecks, select the most suitable tool, configure Spark effectively, and strike a balance between performance and infrastructure resource consumption and cost.

This instructor-led, live training in Argentina (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 in Argentina (online or onsite) is designed for beginner- to intermediate-level system administrators seeking to deploy, maintain, and optimize Spark clusters.

By the conclusion of this training, participants will have the ability 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 in Argentina, participants will learn to combine Python and Spark for big data analysis through hands-on exercises.

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

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

This instructor-led, live training in Argentina (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.