Introduction to databases

The importance of data storage and the chosen technology, classic vs. database access, different types of databases.

Introduction to the role of databases in the IS design

Introduction to information system design. Necessary steps for its implementation - requirements management, design, implementation and deployment. Necessary knowledge for the implementation of such a process. Introduction to 3-tier information system architecture. The role of the data layer for information systems.

Product decomposition

Analysis of processed system entities. Product decomposition and its role in information system design. Sample of product decomposition for a larger system - e-shop.

Introduction to relational databases

Introduction to relational databases. Examples of relational databases - Oracle, MSSQL, MySQL, PostgreSQL. How data is represented in a relational database - tables and relationships between tables. Different types of relationships between tables. Examples of storage and relationships - the employee and his contract, data in the e-shop.

Creating a data model

In this lesson, we will introduce the concept of data model and show methods of creating it. Description of the basics of the Entity-Relation model. The concept of Entity, attributes and their domains. The concept of a session and its different types. The process of creating a data model. Examples of the creation of individual parts on examples.

Data model

The aim of this lesson will be to demonstrate the creation of a data model on a sample data model - e-shop.

Introduction to SQL language

Introduction to SQL language. Implementation of individual tasks in the database in SQL language. Demonstration of basic options - table listing, listing from multiple tables (simple use of links).

SQL querying

Description of other database options. Description of query optimization - explanation of the difference between a query over a primary key and a query conditional on a given column. Explanation of the principle of a database index and its acceleration. Example of time differences. Mention of the existence of database transactions and explanation of the reason by example. Mention of the need for more complex PL / SQL queries without explaining the syntax itself. Mention of database security and description of an interesting database attack via SQL injection.

Object database

Description of the object-oriented approach to world modeling. Description of objects, their relationships and inheritance. Mention of the frequent use of this approach in software design - UML and object languages Java, C #, Python, Ruby and others. Description of how we solve the problem of the difference between the ER model and the object model in programming - ORM mapping. Description of a more suitable approach to object databases.

Database server architecture

Description of database deployment issues. Existence of database servers and the reasons that lead to such solutions - speed, security, persistence. Multiple database servers and load balancer functions. Primary and secondary databases, ETL process, special requirements, OLAP. Demonstration of database deployment solutions in the banking sector.

Introduction to business intelligence

Basic description of what is Business Intelligence Data, Context, Information, Knowledge, Wisdom. The importance of Business Intelligence in the decision-making process of companies and operational, tactical and strategic planning; BI's contribution to business strategy. Incorporation of BI architecture into the overall information portfolio of the company. Traditional methods of Business Intelligence vs. Agile Business Warehousing. Data Warehousing, Information Delivery, Reporting, KPI, Dashboards, Self-Service Business Intelligence (so-called Self-Service BI).

Structured data and the big data problem

What is Big data and its evolution, technologies enabling the creation and development of Big data. Importance and use of Big Data in operational and decision-making processes of companies. Data types, structured versus unstructured data, principles of data storage and data quality. Changes in the way large volumes of data are processed compared to traditional Business Intelligence methods, NoSQL and other types of databases. Basic principles of Big data solution architectures, MapReduce framework for distributed processing. Tools for ad-hoc data querying, so-called querying. The most commonly used methods of analysis and visualization of large data volumes. Data processing and analysis in real time, so-called Complex event processing (CEP). Basic technologies of the Hadoop ecosystem and the most used analytical tools. Problems in obtaining knowledge from Big data inaccurate / incomplete data, own data processing and integration, so-called "outliers", etc. Typical scenarios for using Big Data in large corporations.