Introduction to NoSQL Databases

Understand what NoSQL databases offer, avoid apples-to-apples comparisons with RDBMS, and recognize that each application must assess NoSQL suitability.

Trace the evolution of NoSQL databases from early RDBMS to the 1998 coining of NoSQL and the 2000s shift to graph, document, and key-value systems, highlighting open-source impacts and Hadoop.

Contrast vertical scaling with horizontal scaling and their limits. See how Hadoop and RAC clusters enable distributed processing across thousands of nodes in NoSQL architectures using Kubernetes and EC2.

Explore the five v's of big data—velocity, volume, variety, veracity, and value—and how data quality and IoT data streams drive business insights and fraud detection.

Explore how NoSQL databases embrace non-relational models, flexible schemas, and denormalization to store data without rigid tables, scale horizontally, and balance availability and consistency.

Discover the main NoSQL types—document stores, key-value stores, graph databases, and wide column stores—and why organizations use multiple databases for performance and stability.

Explore the vast and evolving NoSQL landscape, where more than 200 databases span proprietary and open source ecosystems, with examples like Rocksdb, Cassandra, and Elasticsearch.

Examine document databases like MongoDB, storing data as JSON documents converted to bson, retrieved by id, with a primary-secondary cluster and language drivers.

Explore key-value stores with Redis, including master and read replica setups, and how they store diverse data types like bitmaps, hyperloglog, time series, and geospatial data via multiple language clients.

Explore graph data, the most powerful yet complex NoSQL type, with Neo4j's graph database, its nodes and vertices, and interactions via GraphQL, Cypher, Node.js, Python, and Java.

Explore wide column stores and column family databases, focusing on Cassandra's peer-to-peer, masterless architecture with active-active nodes, multi-cloud deployment, and GDPR-compliant data residency.

Explore time-series data within NoSQL databases, highlighting Druid's specialization in streaming and batch workloads, deep storage to HDFS or Amazon S3, and Druid SQL for the Internet of things analytics.

Explore the cap theorem and how NoSQL databases balance consistency, partition tolerance, and availability. See tunable consistency in Cassandra and MongoDB's consistency and partition tolerance.

Compare rdbms and nosql architectures, highlighting general purpose olap workloads versus purpose-built Cassandra and MongoDB, and evaluate their advantages and disadvantages for specific use cases.