Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Kenya (online or onsite) is targeted at 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.

By the end 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 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 Kenya (online or onsite) is targeted 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 live, instructor-led training delivered in Kenya (online or on-site) is designed for intermediate-level data scientists and engineers who aim to utilize 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 (offered online or onsite) is tailored for data engineers, analysts, and professionals aiming to build scalable data pipelines using Databricks and PySpark while migrating existing SQL workflows.

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

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

Attendees will cultivate a deep, practical grasp of how Spark applications operate on Kubernetes, exploring how strategic configuration choices directly impact performance, scalability, resource efficiency, and overall operational costs. The curriculum delves into critical optimization domains, such as executor sizing, memory distribution, dynamic allocation, partitioning methodologies, shuffle dynamics, mitigating small-file inefficiencies, and enhancing Parquet handling.

Additionally, the course tackles prevalent challenges associated with Pandas, particularly memory constraints and out-of-memory exceptions, while introducing Polars as a robust, high-performance alternative for specific data tasks. Through immersive, hands-on labs, participants will learn to troubleshoot performance and memory bottlenecks, evaluate various configuration approaches, and implement optimization techniques in realistic ETL and machine learning contexts.

The core focus remains on practical decision-making: mastering the ability to pinpoint bottlenecks, select the right tool for the job, configure Spark effectively, and strike a balance between performance and infrastructure resource usage and cost.

This instructor-led, live training in Kenya (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 Kenya—conducted either online or onsite—targets system administrators at beginner to intermediate levels seeking to deploy, maintain, and optimize Spark clusters.

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

• Install and configure Apache Spark in various settings.
• Manage cluster resources and track Spark application performance.
• Optimize the efficiency of Spark clusters.
• Apply security controls and ensure high availability.
• Debug and troubleshoot standard Spark challenges.

In this live, instructor-led training held in Kenya, participants will learn to leverage Python and Spark together for big data analysis, supported by practical, hands-on exercises.

Upon completion of this training, participants will be equipped to:

• Utilize Spark in conjunction with Python for comprehensive Big Data analysis.
• Engage with exercises that replicate real-world business scenarios.
• Apply diverse tools and methodologies for big data analysis using PySpark.

This instructor-led, live training in Kenya (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.