Greenplum Architecture and Data Modeling Training Course

This instructor-led, live training in Brazil (online or onsite) is designed for advanced-level data professionals who aim 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, execute complex schema changes, and handle partition evolution.
• Master techniques to enhance 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 Brazil (online or onsite) is aimed at 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 Brazil (online or onsite) targets intermediate-level data scientists and engineers who wish to utilize Google Colab and Apache Spark for big data processing and analytics.

By the conclusion 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 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. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently manage large datasets by leveraging distributed computing principles.

This intensive three-day workshop is dedicated to constructing and refining high-efficiency data processing workflows within Kubernetes environments, utilizing PySpark, Pandas, and Polars.

Attendees will gain a practical grasp of Spark application execution mechanics on Kubernetes, focusing on how specific configuration choices impact performance, scalability, resource usage, and operational costs. Key optimization topics include executor sizing, memory management, dynamic allocation, partitioning methods, shuffle operations, mitigating the small-file issue, and optimizing Parquet processing.

The curriculum also tackles typical obstacles encountered with Pandas, such as memory constraints and out-of-memory exceptions, while introducing Polars as a high-speed alternative for specific data tasks. Through practical exercises, learners will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and implement optimization techniques in realistic ETL and machine learning contexts.

Throughout the course, the primary focus remains on practical decision-making: mastering the identification of bottlenecks, selecting the right tools, configuring Spark for maximum efficiency, and balancing performance against infrastructure resource consumption and cost.

This instructor-led, live training in Brazil (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 Brazil—delivered online or onsite—is designed for system administrators ranging from beginner to intermediate levels who aim to deploy, maintain, and optimize Spark clusters.

By the end of this program, participants will have the ability to:

• Install and configure Apache Spark in a variety of environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot typical Spark issues.

In this instructor-led, live training in Brazil, attendees will discover how to effectively combine Python and Spark for big data analysis while participating in hands-on exercises.

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

• Utilize Spark with Python to conduct Big Data analysis.
• Solve problems through exercises modeled after real-world cases.
• Employ diverse tools and methods for big data analysis using PySpark.

This instructor-led, live training in Brazil (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.