Hadoop and Apache Spark have become the cornerstone technologies in big data ecosystems, enabling distributed data storage and fast parallel processing for real-time and batch analytics. This workshop equips professionals with hands-on skills to leverage Hadoop and Spark for big data applications across industries.
The program introduces participants to the fundamentals of big data architecture, covering Hadoop Distributed File System (HDFS), MapReduce, and YARN for resource management. It further explores Spark’s in-memory computation framework, enabling high-speed processing for data engineering, machine learning, and business intelligence applications.
Through practical exercises and real-world use cases, learners will work with Hadoop and Spark clusters, practice data ingestion, transformation, and analysis, and build scalable data pipelines. The workshop emphasizes learning by doing, ensuring that participants can implement workflows in real environments.
Participants will also gain exposure to integrating Hadoop and Spark with emerging tools such as Hive, Pig, and Spark SQL, as well as cloud-based big data platforms. This knowledge prepares learners to work with structured, semi-structured, and unstructured datasets in modern enterprise ecosystems.
By the end of this intensive program, attendees will possess job-ready skills to manage big data workloads, optimize cluster performance, and design scalable data processing solutions. The course bridges theory with hands-on practice, ensuring professionals are fully prepared for the demands of big data engineering and analytics.
Who Should Attend
- Data engineers and ETL developers
- IT professionals managing enterprise data systems
- Data scientists and analysts working with big data
- Software developers transitioning into big data engineering
- Business intelligence and reporting professionals
- Database administrators seeking Hadoop/Spark expertise
- Cloud engineers integrating big data platforms
- Machine learning engineers handling large datasets
- Consultants and advisors in digital transformation
- Researchers in data-intensive fields
Duration
10 days
Course Objectives
By the end of this workshop, participants will be able to:
- Understand the fundamentals of Hadoop and Spark architectures.
- Implement HDFS for distributed data storage and management.
- Apply MapReduce and Spark transformations for big data processing.
- Develop scalable data pipelines with Spark Core and Spark SQL.
- Work with Hive, Pig, and Spark SQL for querying large datasets.
- Optimize Hadoop and Spark cluster performance for efficiency.
- Manage structured, semi-structured, and unstructured big data.
- Integrate Hadoop and Spark with cloud-based ecosystems.
- Apply big data workflows to machine learning and analytics.
- Design and deploy real-world big data solutions for enterprises.
Comprehensive Course Outline
Module 1: Introduction to Big Data and Ecosystems
- Defining big data and its challenges
- Hadoop and Spark roles in big data architecture
- Overview of distributed computing principles
- Use cases across industries
Module 2: Hadoop Ecosystem Fundamentals
- Hadoop Distributed File System (HDFS)
- MapReduce programming model
- YARN resource management
- Hadoop ecosystem tools overview
Module 3: Spark Essentials
- Introduction to Apache Spark architecture
- Spark Core and RDD concepts
- Transformations and actions
- Spark ecosystem (Spark SQL, MLlib, Streaming)
Module 4: Working with Spark SQL and Hive
- Introduction to Spark SQL
- Querying data using Hive
- Schema-on-read vs schema-on-write
- Hands-on querying exercises
Module 5: Data Ingestion and Processing
- Ingesting structured, semi-structured, and unstructured data
- Data pipelines with Spark
- Handling JSON, XML, and log data
- Batch vs streaming processing
Module 6: Advanced Spark Applications
- Using Spark MLlib for machine learning
- Spark Streaming for real-time data
- Graph processing with GraphX
- Performance optimization techniques
Module 7: Hadoop and Spark Integration
- Combining HDFS with Spark
- Workflow management with Oozie
- Leveraging Pig and Hive with Spark
- Hands-on integration labs
Module 8: Cloud Big Data Platforms
- Deploying Hadoop and Spark on AWS and Azure
- Managed cloud services for big data
- Containers and Kubernetes for Spark clusters
- Cost optimization in cloud environments
Module 9: Governance, Security, and Compliance
- Securing Hadoop and Spark clusters
- Data governance frameworks
- Compliance in big data environments (GDPR, HIPAA)
- Ethical considerations in big data analytics
Module 10: Building a Big Data Solution
- End-to-end big data pipeline design
- Data ingestion, processing, and visualization
- Machine learning integration
- Presentation and evaluation of solutions
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.
Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment
Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better
