Training Course on Apache Spark for Advanced Data Processing

Training Course on Apache Spark for Advanced Data Processing

Introduction

Training Course on Apache Spark for Advanced Data Processing offers an in-depth exploration of Apache Spark, the leading unified analytics engine for large-scale data processing. Participants will gain mastery over distributed data processing, leveraging Spark's powerful capabilities for batch and real-time analytics. The curriculum focuses on practical application, enabling professionals to build robust, scalable, and high-performance big data solutions that drive impactful business insights. Through hands-on exercises and real-world case studies, attendees will unlock the full potential of Spark's core components, including Spark SQL for structured data manipulation and Spark MLlib for machine learning at scale.

Designed for data professionals seeking to advance their skills, this course dives deep into Spark's architecture and optimization techniques. From understanding Resilient Distributed Datasets (RDDs) and DataFrames to implementing advanced ETL pipelines and real-time data streaming, learners will acquire the expertise to tackle complex data engineering challenges. Emphasizing performance tuning and fault tolerance, the training equips participants with the knowledge to deploy and manage efficient Spark applications across diverse environments, from on-premise clusters to cloud-based platforms like Databricks and AWS EMR.

Course Duration

10 days

Course Objectives

1. Master distributed computing principles with Apache Spark.
2. Develop high-performance Spark applications for big data analytics.
3. Utilize Spark SQL for advanced data manipulation and query optimization.
4. Implement real-time data processing using Spark Structured Streaming.
5. Build and evaluate machine learning models with Spark MLlib.
6. Understand Spark architecture and cluster management.
7. Perform performance tuning and troubleshooting of Spark jobs.
8. Integrate Spark with various data sources and data lakes.
9. Apply data warehousing concepts using Spark.
10. Explore GraphX for graph processing and analysis.
11. Design scalable ETL pipelines using Spark.
12. Leverage Spark for predictive analytics and business intelligence.
13. Implement data governance and security best practices in Spark.

Organizational Benefits

• Faster analysis of massive datasets, leading to quicker insights and informed decision-making.
• Ability to handle growing data volumes efficiently, reducing infrastructure costs through optimized resource utilization.
• Rapid development and deployment of data-driven applications, enabling organizations to respond swiftly to market changes.
• Unlocking predictive power through machine learning and real-time analytics, driving innovation and competitive advantage.
• Streamlined data pipelines and improved troubleshooting capabilities, leading to less downtime and more reliable operations.
• Equipping data teams with cutting-edge skills in a highly demanded technology, fostering internal expertise and reducing reliance on external consultants.

Target Audience

• Data Engineers
• Data Scientists
• Big Data Developers
• BI Developers
• Machine Learning Engineers
• Solutions Architects
• ETL Developers
• Anyone working with large-scale datasets and seeking to leverage Apache Spark.

Course Outline

Module 1: Apache Spark Fundamentals & Architecture

• Introduction to Big Data and Distributed Computing
• Apache Spark Ecosystem: Spark Core, Spark SQL, Spark Streaming, MLlib, GraphX
• Spark Architecture: Driver, Executors, Cluster Manager (YARN, Mesos, Standalone)
• Resilient Distributed Datasets (RDDs): Transformations and Actions
• Setting up a Spark Development Environment (Local, Databricks, Cloud)
• Case Study: Analyzing web server logs for common errors and traffic patterns using RDDs.

Module 2: Spark Core: Advanced Concepts

• Spark Internals: DAGScheduler, TaskScheduler, Memory Management
• DataFrames and Datasets: Unified API for Structured Data
• Optimizing DataFrames: Catalyst Optimizer, Tungsten
• Wide vs. Narrow Transformations and Shuffles
• Partitioning and Caching Strategies for Performance
• Case Study: Optimizing a large-scale ETL job for a retail company to improve data loading times by 50%.

Module 3: Spark SQL: Structured Data Analysis

• Working with Spark SQL and DataFrames
• Loading and Saving Data: Parquet, ORC, JSON, CSV
• Advanced SQL Queries: Joins, Aggregations, Window Functions
• User-Defined Functions (UDFs) and User-Defined Aggregate Functions (UDAFs)
• Integrating Spark SQL with External Data Sources (Hive, JDBC, Cloud Storage)
• Case Study: Building a data warehouse layer for a financial institution, enabling complex SQL queries for risk analysis.

Module 4: Spark Structured Streaming: Real-time Data Processing

• Introduction to Stream Processing and Micro-batching
• Structured Streaming Architecture and Concepts
• Sources and Sinks: Kafka, File Stream, Socket Stream
• Stateful Operations: Aggregations, Watermarking, Joins
• Handling Late Data and Fault Tolerance in Streaming
• Case Study: Developing a real-time fraud detection system for an e-commerce platform by analyzing transaction streams.

Module 5: Spark MLlib: Machine Learning at Scale

• Machine Learning Fundamentals and Workflow
• ML Pipelines: Transformers, Estimators, Models
• Feature Engineering and Selection with Spark MLlib
• Supervised Learning: Regression, Classification (Linear Regression, Logistic Regression, Decision Trees)
• Unsupervised Learning: Clustering (K-Means)
• Case Study: Building a customer churn prediction model for a telecommunications company using historical customer data.

Module 6: Advanced Spark MLlib Techniques

• Collaborative Filtering for Recommendation Systems
• Model Evaluation and Hyperparameter Tuning
• Model Persistence and Deployment
• Integrating MLlib with other Data Science tools
• Scalability challenges in ML and Spark's solutions
• Case Study: Implementing a personalized recommendation engine for a streaming service to suggest movies and shows to users.

Module 7: Performance Tuning & Optimization

• Understanding Spark UI and Metrics
• Debugging Spark Applications: Common Errors and Solutions
• Shuffle Optimization Techniques
• Memory Management and Garbage Collection in Spark
• Resource Allocation and Cluster Configuration Best Practices
• Case Study: Diagnosing and resolving performance bottlenecks in a slow-running Spark batch job for a large dataset.

Module 8: Integrating Spark with Data Lakes & Cloud

• Working with Apache HDFS and Cloud Storage (S3, ADLS, GCS)
• Delta Lake: ACID Transactions on Data Lakes
• Integrating Spark with Kafka for message queuing
• Using Spark with NoSQL Databases (Cassandra, MongoDB)
• Deploying Spark on Cloud Platforms (AWS EMR, Azure Databricks, Google Cloud Dataproc)
• Case Study: Migrating an on-premise data processing pipeline to a cloud-based Spark environment using Delta Lake for data reliability.

Module 9: Advanced Data Engineering with Spark

• Building Robust ETL Pipelines: Data Ingestion, Transformation, Loading
• Schema Evolution and Data Governance
• Data Quality and Validation Techniques
• Orchestrating Spark Jobs with Airflow or Oozie
• Designing and Implementing Data Warehousing Solutions
• Case Study: Developing a robust ETL process to consolidate data from various sources into a unified data lake for a healthcare provider.

Module 10: Spark GraphX for Graph Processing

• Introduction to Graph Theory and Graph Processing
• GraphX API: Vertices, Edges, Property Graphs
• Graph Algorithms: PageRank, Connected Components
• Use Cases for Graph Analysis
• Building Graph-Based Applications with Spark
• Case Study: Analyzing social network connections to identify influential users or communities.

Module 11: Security and Monitoring in Spark

• Authentication and Authorization in Spark Clusters
• Data Encryption at Rest and In Transit
• Auditing and Logging for Compliance
• Monitoring Spark Applications with Prometheus, Grafana
• Best Practices for Securing Big Data Environments
• Case Study: Implementing security measures for a Spark cluster processing sensitive customer data to meet regulatory requirements.

Module 12: Advanced Deployment and Operations

• Spark on Kubernetes: Containerized Deployments
• Spark on YARN: Advanced Configurations
• Autoscaling Spark Clusters
• Backup and Disaster Recovery Strategies
• CI/CD for Spark Applications
• Case Study: Automating the deployment and scaling of a Spark application using Kubernetes for a continuous data pipeline.

Module 13: Best Practices & Future Trends

• Code Optimization Best Practices for Spark
• Performance Benchmarking and Capacity Planning
• Emerging Trends: Spark 3.x Features, Photon, Data Lakehouse
• Real-world Spark Use Cases and Architectures
• Career Paths and Certification Opportunities
• Case Study: Reviewing the architecture of a successful large-scale Spark deployment by a leading tech company.

Module 14: Capstone Project

• Participants will work on a comprehensive project applying learned concepts.
• Project options include building an end-to-end data pipeline, developing a machine learning application, or optimizing an existing Spark job.
• Mentored guidance and peer review.
• Presentation of final solutions.
• Case Study: Building a complete data pipeline from data ingestion to advanced analytics and machine learning model deployment for a chosen industry.

Module 15: Workshop & Q&A

• Hands-on workshop to reinforce key concepts.
• Troubleshooting clinic: bring your Spark challenges.
• Open discussion and Q&A with instructors.
• Advanced tips and tricks for Spark development.
• Resource sharing for continued learning.
• Case Study: Collaborative problem-solving session on a complex Spark performance issue encountered in a real-world scenario.

Training Methodology

This course adopts an interactive and hands-on learning approach to maximize knowledge retention and practical skill development. The methodology includes:

• Instructor-Led Sessions: Expert-led lectures with clear explanations of complex concepts.
• Live Coding Demonstrations: Practical examples showcasing real-world Spark implementations.
• Hands-on Labs & Exercises: Extensive practical assignments to solidify understanding and build proficiency.
• Case Studies: Analysis and discussion of industry-specific use cases to illustrate practical applications.
• Group Discussions & Collaborative Problem-Solving: Fostering peer learning and diverse perspectives.
• Q&A Sessions: Dedicated time for addressing participant queries and clarifying doubts.
• Capstone Project: A comprehensive project to apply all learned skills in a real-world scenario.
• Resource Materials: Access to comprehensive slides, code repositories, and supplementary readings.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

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