Big Data Fundamentals
The five V's, the data life cycle, MapReduce and BSP, Lambda/Kappa architectures, performance metrics, the CAP theorem, and responsible-AI governance — all worked on CityCourier's founding sixteen orders, including the veracity bug that echoes through every later chapter.
Distributed Storage Systems
HDFS blocks and replication, the NameNode/DataNode split, the four NoSQL data models, consistent hashing, quorum replication, B-Tree vs LSM-Tree indexing, and scatter-gather query execution.
Parallel & Distributed Data Processing
YARN's cluster resource management, a MapReduce job traced task by task, combiners, data skew and salting, Spark's DAG execution model, and task-retry versus lineage-based fault tolerance.
In-Memory Data Processing
Why memory beats disk by roughly 80×, RDD caching and persistence, LRU eviction and spill-to-disk, and how Spark's Catalyst optimizer pushes filters down to storage that a raw RDD never could.
Streaming Analytics & Real-Time Processing
Tumbling, sliding, and session windows; event time versus processing time; watermarks and allowed lateness; Kafka's topic/partition/consumer-group model; micro-batch versus true streaming; and exactly-once semantics.
Scalable Machine Learning
Data versus model parallelism, distributed gradient descent (and the size-weighting bug that quietly wrecks it), distributed K-means, embarrassingly-parallel Naive Bayes, and fair train/test splitting at scale.
Scalable Visualization & Analytics
Why exact answers stop scaling; Bloom filters, HyperLogLog, and Count-Min Sketch as three answers to membership, cardinality, and frequency; OLAP cubes with free roll-up and drill-down; and sampling for legible dashboards.