Every data science project requires a data structure for its implementation and application to achieve business objectives. In this block of posts entitled: Data access and storage systems, I will explain the different data access, storage and backup architectures. For completing everything related to data architectures, I will allocate another two blocks of posts. A second block will address everything about Data Base Management Systems and a last third block will deal with Big Data.
Data access systems
- Data organization: Basic introduction on how data went from being stored in paper files to digital files stored on a computer.
- Sequential access files: The sequential access files was the first solution for data digitization. Here you can learn its main features and limitations.
- Random access files: Their main features and structure. Making an example exercise of a random access file with Visual Basic.
- Indexed access: Compeling explanation of how indexed access works. Adding some examples of indexed access files, using dense index and sparse index.
- Tree indexing: Introduction to indexing in data tree structures. Brief explanation of the data tree structures.
- Binary search trees: How binary search trees work, and how nodes are organized into these structures. Example of a binary search tree.
- Binary search tree operations: How to work with the different operations that can be performed with binary search trees. Those basic operations are: Search, insert or delete a node.
- Hash algorithm: How does it work, what are its main characteristics and how is it used in the organization of data?
Data storage architecture
- RAID: What is a RAID? Explanation of how the different levels of RAID systems work and their possible combinations.
- Storage architectures: The different types of storage architectures according to the required access: Hard drive access (internal disk, DAS, SAN) and file access (NAS).
- Backup Systems: The different types of backup systems, how they work and how to plan the backup policies.