Build the skills to analyze, apply, and implement scalable data systems using NoSQL databases, Apache Oozie, Apache Storm, and Apache Mahout. You’ll begin by exploring the origins and benefits of NoSQL, including schema flexibility, diverse data types, data versioning, and the role of NoSQL in managing large-scale, unstructured data. You’ll also compare ACID and BASE consistency models and apply consistency principles to application development.

Next, you’ll design and schedule big data workflows with Apache Oozie using Hive actions, control nodes, coordinators, and workflow applications. You’ll then use Apache Storm for real-time stream processing, working with topologies, stream groupings, tasks, workers, Zookeeper, deployment, parallelism, and reliability mechanisms. Finally, you’ll apply Apache Mahout to scalable machine learning workflows. You’ll design recommendation systems, use classification and clustering techniques, evaluate model performance, and work with Canopy, Naïve Bayes, KMeans, and Logistic Regression. Designed for aspiring data engineers, developers, and analysts, this course uniquely connects database design, workflow orchestration, real-time processing, and machine learning in one structured journey. Enroll to gain practical skills for building scalable, fault-tolerant, and intelligent big data solutions.