Modern Data Management Approaches in Real World Cases | | Software Development

Modern Data Management Approaches in Real World Cases
This training provides an overview of modern methods for data storage, including key-value stores, document-oriented and database management systems, distributed data storage and processing systems.
16 hours
Online
English
EAS-011
Modern Data Management Approaches in Real World Cases
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Duration
16 hours
Location
Online
Language
English
Code
EAS-011
Schedule and prices
€ 400
Training for 7-8 or more people? Customize trainings for your specific needs
Modern Data Management Approaches in Real World Cases
Sign Up
Duration
16 hours
Location
Online
Language
English
Code
EAS-011
Schedule and prices
€ 400
Training for 7-8 or more people? Customize trainings for your specific needs

Description

In application design, one of the most important decisions is to build a scalable architecture and select the method for data storage. For decades, relational databases remained the first and only option, thus projects differed only in their degree of normalization, location of business logic, etc. In the last 10-15 years, a lot of alternative systems have appeared – from object-oriented and document-oriented DBMS to distributed file / data flow processing systems.

This course reviews a range of modern solutions which work together in an issue of collecting statistics from the gaming card. We will learn Read / Write Paths, Physical Stores, Data Formats, Amount of Data, Pros & Cons of such storages like Relation Model, Document Oriented, Message Queue, Key Value, MPP, In Memory, etc.
After completing the course, a certificate
is issued on the Luxoft Training form

Objectives

Upon completion of the course, students will be able to:
  • build a real-world architecture with regard to the issue of collecting statistics for more than 20M gaming cards;
  • understand Read / Write Paths, Physical Stores, Data Formats, Amount of Data, Pros & Cons of such storages like Relation Model, Document Oriented, Message Queue, Key Value, MPP, In Memory, etc.;
  • understand what data and request characteristics have to be considered at the stage of requirements analysis - and selection of proper data management systems;
  • know the possibilities and limitations of modern relational and non-relational data management systems;
  • analyze requirements while selecting database management systems.

Target Audience

Architects, application developers, analysts, and database administrators.

Roadmap

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  • Real-world architecture with regard to the issue of collecting statistics for more than 20M gaming cards. Estimates.
  • The evolution of approaches to data storage: databases, data storages, database machines, mass-parallel architectures, hyperconvergence
  • Relational model: which problems can be solved at the expense of what replication, sharding, distributed transactions
  • Document-oriented model. [MongoDB]
  • Message queues and streaming platforms. Data stream processing. [Spark Streaming]
  • “Key-value” minimal model: key structure options, value structure options, program interfaces. Efficiency of non-relational databases: necessary and sufficient conditions [Cassandra, HBase]
  • Distributed file systems: cluster architecture [HDFS]. SQL over distributed file systems: possible architectures, limitations, transactions. [Hive, Spark, Spark SQL, Parquet, ORC]
  • Distributed in-memory data storage systems.
  • Distributed OLAP systems. [Druid]
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