Which data storage to choose - from flat files, through SQL, NoSQL to massive distributed systems

Though no technical background is required, understanding the examples requires some level of database theory (e.g. SQL, etc...)

This course helps customer to chose the write data storage depend on their needs. It covers almost all possible modern approaches.

1. File Document Storage (Cloud Storage)
   1. Features (OCR, Scalaibility, Search, etc...)
   2. Open Source examples (e.g. Next Cloud)
   3. Some commercial examples
2. Flat file storage
   1. XML databases
   2. CSV databases
3. Relational databases
   1. Normalization
   2. Dependencies and Constrants
   3. Scalability - replications, clusters
   4. Open Source and commercial software (MySQL, PostrgreSQL, DM7, Oracle, etc.)
4. NoSQL Storage
   1. Document Oriented Databases (MongoDB, CouchDB etc...)
   2. Column Orientation (Canadra, Scylla etc...)
   3. Search Orientation (Elasticsearch...
5. NewSQL
   1. CAP Theorem
   2. Opensource software (SequoiaDB, etc...)
6. Search Engines
   1. Features (text processing, relevancy, etc...)
   2. Open Source examples
   3. Scalability, High Availability, Load Balacing, etc....
7. Traditional Datawherehouses
   1. Business Inteligence, OLTP and Datawherehouse
   2. Opensource and commercial solutions
8. MapReduce and Distributed Parallel Processing
   1. Hadoop-like (Hive, HFS, Impala)
9. Distributed filesystem
   1. Overview of opensource (Ceph etc...)
10. In-memory Databases
    1. Opensource solution (e.g. ApacheIgnite)
11. Others
    1. Hypertable (Google Bigtable)
    2. BigQuery
    3. AWS solutsion (S3, etc...)
12. Beyond present - future trends