Course Overview

Big Data using Hadoop and Spark is a comprehensive professional training program designed to equip data engineers, data scientists, business analysts, IT professionals, software developers, researchers, and decision-makers with advanced skills in managing, processing, analyzing, and extracting insights from massive datasets using modern big data technologies. As organizations increasingly adopt Big Data Analytics, Hadoop, Apache Spark, Distributed Computing, Data Engineering, Data Lakes, Real-Time Data Processing, Machine Learning, Cloud Analytics, and Business Intelligence, there is a growing demand for professionals who can efficiently handle high-volume, high-velocity, and high-variety data. This course provides participants with practical expertise in leveraging Hadoop and Spark ecosystems to build scalable, high-performance data processing solutions.

The training explores the complete big data lifecycle, including data ingestion, storage, distributed processing, data transformation, analytics, machine learning, visualization, and reporting. Participants will learn how to manage structured, semi-structured, and unstructured data using Hadoop Distributed File System (HDFS), MapReduce, Hive, HBase, Spark SQL, Spark Streaming, and other big data tools. The course combines theoretical foundations with practical applications using real-world datasets from finance, healthcare, telecommunications, retail, manufacturing, and government sectors.

Participants will gain hands-on experience in big data architecture design, cluster management, data warehousing, distributed analytics, machine learning with Spark, real-time data processing, performance optimization, and dashboard development. The course emphasizes scalability, fault tolerance, data governance, security, and operational efficiency. Through practical exercises and case studies, participants will develop confidence in designing and implementing enterprise-grade big data solutions that support advanced analytics and business intelligence.

The training further addresses emerging trends in big data technologies, including cloud-based data platforms, AI-powered analytics, data lakes, lakehouse architectures, IoT analytics, streaming data systems, advanced machine learning pipelines, and modern data engineering frameworks. Participants will develop competencies required to build robust big data ecosystems that support innovation, digital transformation, predictive intelligence, and data-driven decision-making.

Course Objectives

1. Understand the principles and architecture of big data ecosystems.

2. Install, configure, and manage Hadoop and Spark environments.

3. Store and process large datasets using distributed computing technologies.

4. Perform data ingestion, transformation, and management using Hadoop tools.

5. Utilize Apache Spark for fast and scalable data processing.

6. Apply machine learning and predictive analytics using Spark.

7. Process real-time streaming data efficiently.

8. Optimize big data workflows and cluster performance.

9. Develop dashboards and reporting systems for big data insights.

10. Implement secure, scalable, and enterprise-ready big data solutions.

Organizational Benefits

1. Improved capability to process and analyze large-scale datasets.

2. Enhanced operational efficiency through distributed computing.

3. Faster access to business intelligence and analytical insights.

4. Improved scalability for growing data volumes.

5. Better decision-making through advanced analytics.

6. Enhanced predictive modeling and forecasting capabilities.

7. Improved integration of structured and unstructured data.

8. Reduced data processing time and infrastructure costs.

9. Strengthened innovation through AI and machine learning applications.

10. Accelerated digital transformation and competitive advantage.

Target Participants

· Data engineers and data architects

· Data scientists and machine learning practitioners

· Business intelligence and analytics professionals

· Database administrators

· Software developers and programmers

· IT infrastructure and cloud professionals

· Researchers and academic professionals

· Data warehouse and ETL developers

· Big data consultants and solution architects

· Government and enterprise data managers

· Technology innovation specialists

· Anyone interested in Hadoop, Spark, and big data analytics

Course Outline

Module 1: Introduction to Big Data and Distributed Computing

1. Fundamentals of big data concepts

2. Characteristics of big data (Volume, Velocity, Variety, Veracity, Value)

3. Distributed computing principles

4. Big data ecosystem overview

5. Hadoop and Spark architecture fundamentals

6. Emerging trends in big data analytics

Case Study:
Designing a big data strategy to support enterprise-wide analytics and digital transformation.

Module 2: Hadoop Ecosystem Architecture

1. Hadoop framework components

2. Hadoop Distributed File System (HDFS)

3. Hadoop cluster architecture

4. NameNode and DataNode management

5. Resource management with YARN

6. Hadoop deployment models

Case Study:
Implementing a Hadoop cluster for large-scale data storage and processing.

Module 3: Data Storage and Management with HDFS

1. HDFS architecture and operations

2. Data storage and replication mechanisms

3. File management and access control

4. Data partitioning strategies

5. Fault tolerance and recovery

6. HDFS performance optimization

Case Study:
Managing terabytes of enterprise data using Hadoop Distributed File System.

Module 4: MapReduce and Batch Processing

1. MapReduce programming concepts

2. Mapper and Reducer functions

3. Job execution workflows

4. Distributed data processing techniques

5. Performance tuning and optimization

6. MapReduce use cases and applications

Case Study:
Processing large transaction datasets using MapReduce for business reporting.

Module 5: Data Warehousing with Hive and HBase

1. Apache Hive architecture and components

2. Hive Query Language (HQL)

3. Data warehousing concepts in Hadoop

4. HBase architecture and NoSQL databases

5. Structured and semi-structured data management

6. Query optimization techniques

Case Study:
Building a big data warehouse for customer analytics and reporting.

Module 6: Introduction to Apache Spark

1. Spark architecture and ecosystem

2. Resilient Distributed Datasets (RDDs)

3. Spark execution model

4. Spark transformations and actions

5. Spark cluster deployment

6. Performance benefits of Spark

Case Study:
Migrating batch processing workloads from MapReduce to Spark for faster execution.

Module 7: Spark SQL and DataFrame Analytics

1. Spark SQL fundamentals

2. DataFrames and Datasets

3. Data transformation and aggregation

4. Query optimization techniques

5. Structured data analytics

6. Integration with Hadoop ecosystem

Case Study:
Analyzing large customer datasets using Spark SQL and DataFrame operations.

Module 8: Real-Time Data Processing with Spark Streaming

1. Streaming analytics concepts

2. Spark Streaming architecture

3. Processing real-time data streams

4. Window operations and transformations

5. Event-driven analytics

6. Streaming performance optimization

Case Study:
Building a real-time monitoring system for operational and customer data streams.

Module 9: Machine Learning with Spark MLlib

1. Introduction to Spark MLlib

2. Supervised learning algorithms

3. Unsupervised learning techniques

4. Feature engineering and model preparation

5. Model evaluation and optimization

6. Scalable machine learning workflows

Case Study:
Developing predictive customer churn models using Spark machine learning tools.

Module 10: Big Data Security, Governance, and Performance Optimization

1. Big data security frameworks

2. Authentication and authorization mechanisms

3. Data governance and compliance

4. Cluster monitoring and management

5. Performance tuning strategies

6. Resource optimization techniques

Case Study:
Implementing secure and high-performance big data infrastructure for enterprise analytics.

Module 11: Cloud-Based Big Data Analytics

1. Big data platforms in the cloud

2. Data lakes and lakehouse architectures

3. Cloud-native Hadoop and Spark environments

4. Integration with cloud storage systems

5. Scalable analytics pipelines

6. Cost optimization in cloud environments

Case Study:
Deploying a cloud-based Hadoop and Spark platform for enterprise data processing.

Module 12: Advanced Big Data Solutions and Future Trends

1. AI and big data convergence

2. IoT and sensor data analytics

3. Advanced data engineering pipelines

4. Modern data architectures and platforms

5. Future trends in Hadoop and Spark ecosystems

6. Building enterprise big data strategies

Case Study:
Designing an integrated big data analytics ecosystem that combines Hadoop storage, Spark processing, real-time streaming analytics, machine learning pipelines, cloud-based data lakes, business intelligence dashboards, governance frameworks, and predictive analytics tools to improve operational efficiency, customer insights, strategic decision-making, innovation, and digital transformation outcomes.

Essential Information

Our courses are customizable to suit the specific needs of participants.
Participants are required to have proficiency in the English language.
Our training sessions feature comprehensive guidance through presentations, practical exercises, web-based tutorials, and collaborative group activities. Our facilitators boast extensive expertise, each with over a decade of experience.
Upon fulfilling the training requirements, participants will receive a prestigious Global King Project Management certificate.
Training sessions are conducted at various Global King Project Management Centers, including locations in Nairobi, Mombasa, Kigali, Dubai, Lagos, and others.
Organizations sending more than two participants from the same entity are eligible for a generous 20% discount.
The duration of our courses is adaptable, and the curriculum can be adjusted to accommodate any number of days.
To ensure seamless preparation, payment is expected before the commencement of training, facilitated through the Global King Project Management account.
For inquiries, reach out to us via email at training@globalkingprojectmanagement.org or by phone at +254 114 830 889.
Additional amenities such as tablets and laptops are available upon request for an extra fee. The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a certificate of successful completion. Participants are responsible for arranging and covering their travel expenses, including airport transfers, visa applications, dinners, health insurance, and any other personal expenses.

Big Data using Hadoop and Spark Training Course