A data model is an abstract view over the data that hides the way it is stored physically. The same idea from (Codd 1970) This is why we should not modify data directly, but pass though some abstraction that maintain the properties of that specific data model. Data Models Tree view 馃煩 We can view all JSON and XML data, as presented in Markup, as trees. This structure is usually quite evident, as it is inherent in their design....
Little bits of history It was invented in 1970 in Almaden (San Jose) by IBM (Don Chamberlin, Raymond Boyce worked on this) for the first relational database, called system R. Then for copyright issues it hasn鈥檛 been called SEQUEL, so they branded it as SQL. SQL is a declarative language With declaratives language there is a separation between what I call the intentionality and the actual process. In declarative languages we just say what we want the result to be, and don鈥檛 care what the actual implementation is like....
Data Cubes is a data format especially useful for heavy reads. It has been popularized in business environments where the main use for data was to make reports (many reads). This also links with the OLAP (Online Analytical Processing) vs OLTP (Online Transaction Processing) concepts, where one is optimized for reads and the other for writes. The main driver behind data cubes was business intelligence. While traditional relational database systems are focused on the day-to-day business of a company and record keeping (with customers placing or- ders, inventories kept up to date, etc), business intelligence is focused on the production of high-level reports for supporting C-level executives in making informed decisions....
p> Document stores provide a native database management system for semi-structured data. Document stores also scale to Gigabytes or Terabytes of data, and typically millions or billions of records (a record being a JSON object or an XML document). Introduction to Document Stores A document store, unlike a data lake, manages the data directly and the users do not see the physical layout. Unlike data lakes, using document stores prevent us from breaking data independence and reading the data file directly: it offers an automatic manager service for semi-structured data that we need to throw and read quickly....
This is a new framework that is faster than MapReduce (See Massive Parallel Processing). It is written in Scala and has a more functional approach to programming. Spark extends the previous MapReduce framework to a generic distributed dataflow, properly modeled as a DAG. There are other benefits of using Spark instead of the Map reduce Framework: Spark processes data in memory, avoiding the disk I/O overhead of MapReduce, making it significantly faster....