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Course Outline
- File Document Storage (Cloud Storage)
- Features (OCR, Scalaibility, Search, etc...)
- Open Source examples (e.g. Next Cloud)
- Some commercial examples
- Flat file storage
- XML databases
- CSV databases
- Relational databases
- Normalization
- Dependencies and Constrants
- Scalability - replications, clusters
- Open Source and commercial software (MySQL, PostrgreSQL, DM7, Oracle, etc.)
- NoSQL Storage
- Document Oriented Databases (MongoDB, CouchDB etc...)
- Column Orientation (Canadra, Scylla etc...)
- Search Orientation (Elasticsearch...
- NewSQL
- CAP Theorem
- Opensource software (SequoiaDB, etc...)
- Search Engines
- Features (text processing, relevancy, etc...)
- Open Source examples
- Scalability, High Availability, Load Balacing, etc....
- Traditional Datawherehouses
- Business Inteligence, OLTP and Datawherehouse
- Opensource and commercial solutions
- MapReduce and Distributed Parallel Processing
- Hadoop-like (Hive, HFS, Impala)
- Distributed filesystem
- Overview of opensource (Ceph etc...)
- In-memory Databases
- Opensource solution (e.g. ApacheIgnite)
- Others
- Hypertable (Google Bigtable)
- BigQuery
- AWS solutsion (S3, etc...)
- Beyond present - future trends
Requirements
Though no technical background is required, understanding the examples requires some level of database theory (e.g. SQL, etc...)
7 Hours
Testimonials (2)
I liked that he had actual know how of when to use each technology, that's valuable.
Radu Mazilu - eMAG IT Research
Course - Which data storage to choose - from flat files, through SQL, NoSQL to massive distributed systems
The Trainer Subject Knowledge