Introduction

In the era of Zettabytes, extracting meaningful insights from massive, diverse, and rapidly growing datasets requires specialized tools and expertise. Big Data Analytics with Apache Hadoop and Spark empowers organizations to tackle these challenges head-on, providing the foundational distributed processing and storage frameworks essential for handling big data volumes, velocities, and varieties, enabling advanced analytics, machine learning, and real-time processing capabilities. This training course is meticulously designed to equip data engineers, data scientists, software developers, BI architects, and IT professionals with cutting-edge knowledge and practical skills in understanding the Big Data ecosystem, mastering distributed file systems (HDFS), leveraging the power of MapReduce for batch processing, diving deep into Apache Spark for in-memory and real-time analytics, utilizing Spark SQL for structured data, and exploring machine learning with Spark MLlib to unlock profound business insights and drive innovation. Participants will gain a comprehensive understanding of how to design, implement, and optimize scalable big data solutions that transform raw data into a strategic asset.

Duration

10 days

Target Audience

• Data Engineers
• Data Scientists
• Big Data Developers
• BI Architects
• Solution Architects
• Software Developers (interested in Big Data)
• Database Administrators (DBAs)
• IT Professionals managing Big Data infrastructure
• Researchers working with large datasets

Objectives

• Understand the core concepts of Big Data, its challenges, and opportunities.
• Master the architecture and components of Apache Hadoop (HDFS, YARN, MapReduce).
• Develop proficiency in distributed data storage and processing using HDFS and MapReduce.
• Understand the architecture and advantages of Apache Spark for big data analytics.
• Learn to write data processing applications using Spark Core (RDDs, DataFrames, Datasets).
• Explore Spark SQL for structured data analysis and integration.
• Develop skills in real-time data processing using Spark Streaming.
• Understand the fundamentals of machine learning with Spark MLlib.
• Learn about common tools in the Hadoop and Spark ecosystems (Hive, HBase, Sqoop, Flume).
• Formulate strategies for deploying, monitoring, and optimizing Big Data applications.
• Apply Hadoop and Spark to solve complex, large-scale data problems.

Course Content

Module 1. Introduction to Big Data and the Hadoop Ecosystem

• Defining Big Data: The 3Vs (Volume, Velocity, Variety) and beyond
• Challenges of traditional data processing systems
• Introduction to Apache Hadoop: History, philosophy, and core components
• Overview of the Hadoop ecosystem: HDFS, YARN, MapReduce
• Case studies of Big Data applications

Module 2. Hadoop Distributed File System (HDFS)

• HDFS Architecture: NameNode, DataNodes, Secondary NameNode
• Data Block management, replication, and fault tolerance
• HDFS commands for file management
• Understanding data locality and its importance
• Hands-on: Interacting with HDFS via command line

Module 3. Hadoop YARN (Yet Another Resource Negotiator)

• YARN Architecture: ResourceManager, NodeManager, ApplicationMaster, Containers
• Resource allocation and job scheduling
• How YARN enables multiple processing engines on Hadoop
• YARN commands and monitoring
• Understanding resource management for efficiency

Module 4. MapReduce: The Traditional Batch Processing Engine

• MapReduce Programming Model: Map and Reduce phases
• How MapReduce works: Input splits, shuffling, sorting
• Writing basic MapReduce programs (conceptual and practical examples)
• Limitations of MapReduce for iterative and real-time processing
• Use cases for MapReduce

Module 5. Introduction to Apache Spark: The Next-Gen Big Data Engine

• What is Apache Spark?: Its advantages over MapReduce (speed, generality, ease of use)
• Spark Architecture: Driver, Executors, Cluster Manager
• Resilient Distributed Datasets (RDDs): Immutability, laziness, fault tolerance
• Spark's deployment modes: Standalone, YARN, Mesos, Kubernetes
• Spark's unified stack: Spark Core, Spark SQL, Spark Streaming, MLlib, GraphX

Module 6. Spark Core: RDDs and Transformations

• Creating RDDs: From collections, HDFS, local files
• RDD Transformations: map, filter, flatMap, distinct, union, intersection
• RDD Actions: collect, count, take, reduce, saveAsTextFile
• Understanding lazy evaluation and DAG (Directed Acyclic Graph)
• Key-Value Pair RDDs and common operations

Module 7. Spark SQL and DataFrames

• Introduction to Spark SQL: Structured data processing
• DataFrames: Creation, operations, and benefits
• Schema inference and explicit schema definition
• Performing SQL queries on DataFrames
• Interoperating with Hive and Parquet data

Module 8. Spark SQL and Datasets

• Introduction to Datasets: Type-safe, object-oriented API
• Differences between RDDs, DataFrames, and Datasets
• When to use DataFrames vs. Datasets
• Performing transformations and actions on Datasets
• Optimizing performance with Catalyst Optimizer and Tungsten Engine

Module 9. Spark Streaming for Real-Time Analytics

• Concepts of Stream Processing: Micro-batching vs. continuous processing
• DStreams (Discretized Streams): Creating, transforming, outputting
• Integrating with data sources: Kafka, Flume, HDFS
• Fault tolerance and exactly-once semantics in Spark Streaming
• Real-time use cases: Log analysis, sensor data processing

Module 10. Machine Learning with Spark MLlib

• Introduction to MLlib: Scalable machine learning library
• MLlib Pipelines: Data preparation, feature engineering, model training
• Common ML algorithms: Classification (Logistic Regression), Regression (Linear Regression)
• Clustering (K-Means), Collaborative Filtering (ALS)
• Model evaluation and deployment basics

Module 11. Hadoop Ecosystem Tools: Hive and Pig

• Apache Hive: Data warehouse infrastructure on Hadoop, HiveQL
• Creating tables, loading data, querying in Hive
• Apache Pig: High-level platform for data flow, Pig Latin
• Writing Pig scripts for ETL and data analysis
• Comparison and integration of Hive and Pig with Spark

Module 12. Hadoop Ecosystem Tools: HBase and NoSQL Databases

• Apache HBase: NoSQL column-oriented database on HDFS
• HBase architecture: HMaster, RegionServers, ZooKeeper
• CRUD operations in HBase
• Introduction to other NoSQL databases (Cassandra, MongoDB) and their Big Data roles
• Use cases for NoSQL with Hadoop/Spark

Module 13. Hadoop Ecosystem Tools: Sqoop and Flume

• Apache Sqoop: Importing/exporting data between Hadoop and relational databases
• Sqoop commands for data transfer
• Apache Flume: Collecting and aggregating large log data
• Flume agents, sources, channels, sinks
• Real-time data ingestion with Flume

Module 14. Cluster Management and Monitoring

• Hadoop Cluster Setup: Single-node vs. multi-node configurations (conceptual)
• YARN Resource Management: Monitoring cluster health and job execution
• Spark UI: Monitoring Spark applications, stages, tasks
• Logging and debugging Big Data applications
• Performance tuning tips for Hadoop and Spark

Module 15. Big Data Case Studies and Advanced Topics

• Real-World Big Data Architectures: Data Lake, Data Lakehouse concepts
• Industry-specific Big Data use cases: Retail, Finance, Healthcare
• Introduction to other Big Data technologies: Kafka, Flink, Presto
• Cloud Big Data services (e.g., AWS EMR, Azure HDInsight, Google Dataproc)
• The future of Big Data analytics and emerging trends

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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute 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 Skills for Africa Training Institute 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: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.