Course Overview
Welcome to the world of Big Data processing with Hadoop and Spark! In today's data-driven era, organizations across industries are grappling with the challenges posed by the ever-increasing volume, variety, and velocity of data. Traditional data processing systems are often inadequate to handle the scale and complexity of modern datasets. This is where Hadoop and Spark come into play. Hadoop and Spark are two powerful frameworks designed to address the challenges of processing and analyzing large-scale datasets in a distributed and fault-tolerant manner. By leveraging these frameworks, organizations can unlock valuable insights from their data, enabling informed decision-making and driving innovation.
Duration
10 Days
Target Audience
- Data analysts and scientists
- IT professionals
- Software engineers
- Data engineers
- Business intelligence professionals
- Project managers
Personal Impact
- Mastery of Hadoop and Spark for big data analysis
- Enhanced ability to manage and analyze massive datasets
- Improved skills in data processing, storage, and retrieval
- Increased proficiency in using big data tools for real-world applications
- Capability to implement big data solutions in diverse industries
Organizational Impact
- Enhanced data processing efficiency and scalability
- Improved decision-making through comprehensive data analysis
- Reduced costs through optimized big data workflows
- Increased ability to handle and analyze large volumes of data
- Competitive advantage through advanced big data analytics capabilities
Course Level:
Course Objectives
- Understand the fundamentals of big data and its importance
- Learn to use Hadoop for distributed storage and processing of large datasets
- Gain proficiency in using Spark for real-time data processing
- Develop skills in managing and analyzing big data with Hadoop and Spark
- Implement big data solutions in various industry scenarios
- Optimize big data workflows for improved performance and efficiency
- Communicate data insights effectively to stakeholders using big data tools
Course Outline
Module 1: Introduction to Big Data and Distributed Computing
- Overview of Big Data concepts
- Introduction to distributed computing
- Understanding the need for frameworks like Hadoop and Spark
- Case Study/Practical Component: Analyze a real-world scenario where Big Data frameworks like Hadoop and Spark are used to address a specific problem, such as fraud detection in financial transactions.
Module 2: Fundamentals of Hadoop
- Introduction to Apache Hadoop ecosystem
- Hadoop Distributed File System (HDFS)
- MapReduce paradigm for parallel processing
- Hadoop ecosystem components: YARN, HBase, Hive, etc.
- Case Study/Practical Component: Implement a simple MapReduce job to process a large dataset, such as log files, and analyze the results.
Module 3: Setting Up and Working with Hadoop
- Setting up a Hadoop cluster (local or distributed)
- Hands-on exercises with Hadoop streaming and MapReduce jobs
- Data ingestion and storage strategies in Hadoop
- Case Study/Practical Component: Configure a local Hadoop cluster and run a real-world data ingestion scenario, such as importing and processing customer data from CSV files.
Module 4: Introduction to Apache Spark
- Overview of Apache Spark framework
- Key features and advantages of Spark over Hadoop
- Spark architecture: RDDs, DAGs, and execution model
- Case Study/Practical Component: Explore a case where Spark is used to improve performance over Hadoop, such as real-time analytics on user interactions for a website.
Module 5: Spark Programming Fundamentals
- Working with Spark using Scala or Python
- Understanding Spark Context and SparkSession
- Basic transformations and actions in Spark RDDs
- Case Study/Practical Component: Develop a Spark application to perform data aggregation and filtering on a sample dataset, such as analyzing sales data.
Module 6: Data Processing with Spark
- Exploring Spark's DataFrame API
- Data manipulation and transformation using Spark DataFrames
- Introduction to Spark SQL for querying structured data
- Case Study/Practical Component: Use Spark DataFrames and SQL to perform complex queries and transformations on a large dataset, such as customer purchase history.
Module 7: Advanced Spark Techniques
- Introduction to Spark Streaming for real-time data processing
- Machine learning with Spark MLlib
- Graph processing with Spark GraphX
- Case Study/Practical Component: Implement a real-time streaming application using Spark Streaming to process and analyze live social media feeds.
Module 8: Integrating Hadoop and Spark
- Leveraging HDFS for data storage in Spark
- Running Spark on YARN for resource management
- Interacting with Hadoop ecosystem tools from Spark (e.g., Hive, HBase)
- Case Study/Practical Component: Integrate Spark with Hadoop's HDFS and Hive to perform a complex data analysis task, such as querying and processing data from a Hive table.
Module 9: Performance Optimization in Hadoop and Spark
- Techniques for optimizing performance in Hadoop and Spark
- Scaling Hadoop and Spark clusters for large-scale data processing
- Monitoring and tuning Spark applications for efficiency
- Case Study/Practical Component: Optimize a Spark job for performance and scalability, including tuning parameters and analyzing resource usage to handle a large dataset.
Module 10: Advanced Topics and Future Trends in Big Data
- Emerging trends and technologies in Big Data and distributed computing
- Case studies and real-world applications
- Future directions and opportunities in Big Data analytics
- Case Study/Practical Component: Investigate a recent advancement in Big Data technology, such as new tools or techniques, and assess its potential impact on future data analytics practices.
Related Courses
Course Administration Details:
Methodology
These instructor-led training sessions are delivered using a blended learning approach and include presentations, guided practical exercises, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience as professionals and trainers in these fields. All facilitation and course materials are offered in English. Participants should be reasonably proficient in the language.
Accreditation
Upon successful completion of this training, participants will be issued an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
Training Venue
The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
Accommodation and Airport Transfer
Accommodation and Airport Transfer are arranged upon request. For reservations contact the Training Officer.
- Email: [email protected]
- Phone: +254715 077 817
Tailor-Made
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on:
- Email: [email protected]
- Phone: +254715 077 817
Payment
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to [email protected]
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