Greenplum Architecture and Data Modeling Training Course

This instructor-led, live training in Botswana (online or onsite) is tailored 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 an in-depth 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 Botswana (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 Botswana (online or onsite) is designed for data scientists and engineers at an intermediate level who wish to utilise Google Colab and Apache Spark for big data processing and analytics.

Upon completing 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 within a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led live training (online or onsite) targets data engineers, analysts, and professionals seeking to utilize Databricks and PySpark for building scalable data pipelines and migrating existing SQL workflows.

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into 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 constructing and refining high-performance data processing workloads within Kubernetes environments, leveraging PySpark, Pandas and Polars.

Attendees will gain a working knowledge of how Spark applications operate on Kubernetes, along with the impact that application-level configuration choices have on overall performance, scalability, resource utilisation and operational costs. The curriculum explores critical optimisation domains such as executor sizing, memory distribution, dynamic allocation, partitioning methods, shuffle operations, mitigating small-file issues and implementing efficient Parquet handling.

Additionally, the session tackles frequent difficulties associated with Pandas, such as memory constraints and out-of-memory errors, while presenting Polars as a high-performance alternative for specific processing tasks. Through practical exercises, participants will learn to diagnose performance and memory challenges, evaluate various configuration strategies and implement optimisation techniques for realistic ETL and machine learning contexts.

Throughout the workshop, the focus remains on practical decision-making: identifying bottlenecks, choosing the right tools, configuring Spark effectively and balancing performance against infrastructure resource usage and expenditure.

This instructor-led, live training in Botswana (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 Botswana (available online or onsite) is designed for system administrators at beginner to intermediate levels who seek to deploy, maintain, and optimize Spark clusters.

Once this training is complete, participants will be equipped to:

• Install and configure Apache Spark in multiple environments.
• Manage cluster resources while monitoring Spark applications.
• Improve the performance of Spark clusters.
• Enforce security protocols and maintain high availability.
• Debug and troubleshoot standard Spark problems.

In this live, instructor-led training held in Botswana, participants will discover how to combine Python and Spark for big data analysis while engaging in hands-on exercises.

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

• Utilize Spark alongside Python for Big Data analysis.
• Complete exercises that simulate real-world situations.
• Employ a variety of tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Botswana (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.