Master Spark, Hadoop, and cloud platforms to process and analyze massive datasets at scale. Build robust data pipelines for enterprise applications.
Dive into the world of big data engineering and learn to build scalable systems that handle massive datasets. Master the tools and technologies used by top tech companies to process petabytes of data efficiently.
Introduction to big data concepts, distributed systems, and data engineering principles
HDFS, MapReduce, Hive, HBase, and cluster management
Spark Core, SQL, Streaming, MLlib, and performance optimization
Cloud data services, Kafka, real-time processing, and microservices
NoSQL databases, data lake architecture, and modern data stack
Build and deploy a complete big data solution with real-time analytics
Principal Data Engineer at Netflix
MS in Computer Science from Stanford, 12+ years in big data, architect of Netflix's recommendation data pipeline serving 200M+ users.
Build a streaming data pipeline using Kafka and Spark for real-time user behavior analysis.
Kafka + SparkDesign and implement a cloud-based data lake using AWS services for petabyte-scale storage.
AWS + HadoopCreate an end-to-end machine learning pipeline that processes millions of records daily.
Spark MLlib"This course was exactly what I needed to advance from traditional databases to big data. Now I'm leading data architecture at Spotify!"
"The hands-on projects were incredible. Building real-time pipelines with Michael's guidance prepared me perfectly for my role at Uber."