The growth of the internet has brought along with it the phenomena of Big Data and its massive quantities of rapidly evolving, unstructured information. The need to process and store this information in a timely and cost effective way has led to the adoption of the computer cluster as the infrastructure of choice. The adoption of computer clusters as a primary tool in the IT world has given greater impetus to the development of distributed systems that take full advantage of this infrastructure. Apache Spark is an example of such a distributed system for data processing. This course is about distributed persistence technologies, focusing on NoSQL databases, and their query languages.
The Hadoop Distributed File System (HDFS) is the first persistence technology presented in the course. Well known for its role in Hadoop MapReduce, HDFS is also used directly by many Big Data and NoSQL applications including Apache Spark, Pig, Hive, and HBase. Each of these three technologies: Pig for data mining, Hive for data warehousing, and HBase as a NoSQL column-family store, are described.
The course then presents representative NoSQL databases for each of the four previously mentioned data models: MongoDB for the document, Cassandra for column-family, Neo4j for graph, and Redis for key-value.
For each particular NoSQL implementation, its architecture is described and positioned via the CAP theorem. Common use cases are presented and the API demonstrated. Specific approaches for achieving scalability are identified.
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