Big Data Engineering for Analytics: Architecting Solutions

Course Description

This comprehensive 5-day course equips data professionals with the skills to design, implement, and manage big data solutions for analytics. Participants will learn to architect data lakes, build fault-tolerant distributed systems, and develop scalable data processing pipelines. The course covers essential big data technologies, best practices, and real-world applications.

Learning Objectives

• Design and implement scalable big data architectures for analytics
• Build and manage data lakes using various storage technologies
• Develop efficient data processing pipelines using distributed computing frameworks
• Apply best practices for data governance, security, and quality in big data environments
• Integrate big data solutions with analytics and machine learning workflows

Course Modules

Day 1: Introduction to Big Data Engineering

• Big data concepts and ecosystem overview
• Data engineering fundamentals and best practices
• Introduction to distributed systems and parallel processing
• Big data architecture patterns and use cases

Day 2: Data Storage and Management

• Data lake design and implementation
• Distributed file systems (HDFS, S3)
• NoSQL databases for big data (MongoDB, Cassandra)
• Data modeling and schema design for big data

Day 3: Data Processing and Analytics

• Batch processing with MapReduce and Hadoop
• Stream processing with Apache Kafka and Spark Streaming
• SQL on big data with Hive and Presto
• Machine learning at scale with Spark MLlib

Day 4: Data Pipelines and Workflow Management

• ETL and data integration for big data
• Workflow orchestration with Apache Airflow
• Data quality and validation techniques
• Monitoring and optimization of data pipelines

Day 5: Advanced Topics and Best Practices

• Data governance and security in big data environments
• Performance tuning and optimization techniques
• Real-time analytics and dashboarding
• Case studies and practical applications

Practical Wins for Participants

• Design and implement a scalable data lake architecture
• Build an end-to-end data processing pipeline using distributed frameworks
• Develop a real-time analytics solution for streaming data
• Create a data governance strategy for a big data environment