Database Design & Management: MySQL, Oracle & PostgreSQL
Database design is the process of producing a detailed data model of a database. This data model contains all the needed logical and physical design choices and physical storage parameters needed to generate a design in a data definition language, but if you don’t master database designing and management, you will miss the opportunity to create databases designs or manage data properly.
What if you could change that?
My complete Database Design course will show you the exact techniques and strategies you need to learn database design process, create advanced SQL queries, master SQL and master Oracle & PostgreSQL. For less than a movie ticket, you will get over 4 hours of video lectures and the freedom to ask me any questions regarding the course as you go through it. 🙂
What Is In This Course?
Your Database Design Skills Will Never Be The Same.
Except if you’re an expert at database design, know The Relational Data Model, master Logical Database Design, do Querying and Manipulating Data, learn Data Management, know Functional Dependencies and Normalization, you are going to lose many job/career opportunities or even design databases.
As what Vitalik Buterin, a Russian-Canadian programmer and a co-founder of Ethereum, says “In order to have a decentralised database, you need to have security. In order to have security, you need to – you need to have incentives.”
This is offered with a 30 days money back guarantee. You can try it with no financial risk.
In This Database Design Training, You’ll Learn:
- The Entity Relationship (= ER) Model
- The Relational Data Model
- Logical Database Design
- Relational Algebra (an algebraic query
- language for the relational model)
- MYSQL, PostgreSQL
- Querying and Manipulating Data
- SQL Data Definition Language
- Single Block Queries
- Transaction Management and Concurrency Control
- Database Access from a Programming Language: JDBC 5
- Data Management
- Data Storage and Indexing
- File Organization and Indexes
- Tree-structured Indexing: B+-trees
- Hash-based Indexing
- Indexes in PostgreSQL
- Query Evaluation, optimization & Plans in PostgreSQL
- Functional Dependencies and Normalization
- System Development Life Cycle
- Preparing Written Documentation, Visual Materials
- Database Design
- Database development process
- Relational database design using ERD
- Normalizing database designs
- Object-relational design using EERD
- Advanced SQL
- The DBMS lifecycle
Is This For You?
- Do you want to learn database design process?
- Are you wondering how to create advanced SQL queries?
- Do you understand how to master SQL, Oracle & PostgreSQL?
Then this course will definitely help you.
This course is essential to all software developers, database designers web developers, data scientist, data analysts and anyone looking to master database design.
I will show you precisely what to do to solve these situations with simple and easy techniques that anyone can apply.
Why To Master Database Design?
Let Me Show You Why To Master Database Design:
1. You will learn database design process.
2. You will create advanced SQL queries.
3. You will master SQL.
4. You will master Oracle & PostgreSQL.
Thank you so much for taking the time to check out my course. You can be sure you’re going to absolutely love it, and I can’t wait to share my knowledge and experience with you inside it!
Why wait any longer?
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Introduction To Database
A database is just what the name implies, a base collection of data. The data is organized in some manner so that the information contained within the database can be easily retrieved. Some of the simple databases that you might be familiar with are things like phone books or rolodexes. As data processing has become more sophisticated, so have methods for collecting, storing and retrieving information. Databases have become the cornerstone for an overwhelming amount of the computing environment in existence.
The so-called semantic modeling method nowadays is commonly used in database structure design. Semantic modeling is modeling data structures, based on the meaning of these data. Different variants of the entity-relationship diagrams are used as a tool for the semantic modeling. ER-model based diagrams have three main components: an entity, a relation and attributes. An entity is a class of similar objects, information about which should be taken into account in the model. Each entity must have a name, expressed by a noun in the singular. Examples of entities can be such classes of objects as "Supplier", "Employee", "Invoice". Each entity in the model is depicted in the form of a rectangle with the name.
The relational data model was introduced by C. F. Codd in 1970. Currently, it is the most widely used data model.
The relational model has provided the basis for:
- Research on the theory of data/relationship/constraint
- Numerous database design methodologies
- The standard database access language called structured query language (SQL)
- Almost all modern commercial database management systems
- The relational data model describes the world as “a collection of inter-related relations (or tables).”
Data modelling is the first step in the process of database design. This step is sometimes considered to be a high-level and abstract design phase, also referred to as conceptual design. The aim of this phase is to describe:
Relational Algebra is procedural query language, which takes Relation as input and generate relation as output. Relational algebra mainly provides theoretical foundation for relational databases and SQL.
Structured Query Language (SQL) is a database language designed for managing data held in a relational database management system. SQL was initially developed by IBM in the early 1970s (Date 1986). The initial version, called SEQUEL (Structured English Query Language), was designed to manipulate and retrieve data stored in IBM’s quasi-relational database management system, System R. Then in the late 1970s, Relational Software Inc., which is now Oracle Corporation, introduced the first commercially available implementation of SQL, Oracle V2 for VAX computers.
The SQL data manipulation language (DML) is used to query and modify database data. In this chapter, we will describe how to use the SELECT, INSERT, UPDATE, and DELETE SQL DML command statements, defined below.
One thing that you will most certainly run into at one point or another when working with Microsoft SQL Server, or any other Relational Database Management System (RDBMS), is blocked processes caused by locks on database objects. But what are database locks and why can they sometimes cause one process to block another?
Data aggregation is any process in which information is gathered and expressed in a summary form, for purposes such as statistical analysis. A common aggregation purpose is to get more information about particular groups based on specific variables such as age, profession, or income.
There are two primary processes (1) transaction management and (2) concurrency control in database management system (DBMS):
Transaction management (TM) handles all transactions properly in DBMS. Database transactions are the events or activities such as series of data read/write operations on data object(s) stored in database system (Larson, Blanas, Diaconu, Freedman, Patel, & Zwilling, 2011).
Java Database Connectivity (JDBC) is an application programming interface (API) for the programming language Java, which defines how a client may access a database. It is Java based data access technology and used for Java database connectivity. It is part of the Java Standard Edition platform, from Oracle Corporation. It provides methods to query and update data in a database, and is oriented towards relational databases. A JDBC-to-ODBC bridge enables connections to any ODBC-accessible data source in the Java virtual machine (JVM) host environment.
The data is present in arbitrary order, but the logical ordering is specified by the index. The data rows may be spread throughout the table regardless of the value of the indexed column or expression. The non-clustered index tree contains the index keys in sorted order, with the leaf level of the index containing the pointer to the record (page and the row number in the data page in page-organized engines; row offset in file-organized engines).
A database index is a data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index data structure.
As we have seen already, database consists of tables, views, index, procedures, functions etc. The tables and views are logical form of viewing the data. But the actual data are stored in the physical memory. Database is a very huge storage mechanism and it will have lots of data and hence it will be in physical storage devices. In the physical memory devices, these data cannot be stored as it is. They are converted to binary format. Each memory devices will have many data blocks, each of which will be capable of storing certain amount of data. The data and these blocks will be mapped to store the data in the memory.
A B+ tree is an N-ary tree with a variable but often large number of children per node. A B+ tree consists of a root, internal nodes and leaves. The root may be either a leaf or a node with two or more children.
Indexes are a common way to enhance database performance. An index allows the database server to find and retrieve specific rows much faster than it could do without an index. But indexes also add overhead to the database system as a whole, so they should be used sensibly.
Query Optimizer Uses heuristic algorithms to evaluate relational algebra expressions. This involves: estimating the cost of a relational algebra expression transforming one relational algebra expression to an equivalent one choosing access paths for evaluating the subexpressions Query optimizers do not optimize just try to find reasonably good evaluation strategies
This command displays the execution plan that the PostgreSQL planner generates for the supplied statement. The execution plan shows how the table(s) referenced by the statement will be scanned — by plain sequential scan, index scan, etc. — and if multiple tables are referenced, what join algorithms will be used to bring together the required rows from each input table.
The notion of functional dependencies is used to define second, and third normal form, and the Boyce-Codd normal form (BCNF). ... Functional Dependencies are fundamental to the process of Normalization Functional Dependency describes the relationship between attributes (columns) in a table.
The systems development life cycle (SDLC), also referred to as the application development life-cycle, is a term used in systems engineering, information systems and software engineering to describe a process for planning, creating, testing and deploying an information system. The systems development lifecycle concept applies to a range of hardware and software configurations, as a system can be composed of hardware only, software only or a combination of both.
A core aspect of software engineering is the subdivision of the development process into a series of phases, or steps, each of which focuses on one aspect of the development. The collection of these steps is sometimes referred to as the software development life cycle (SDLC). The software product moves through this life cycle (sometimes repeatedly as it is refined or redeveloped) until it is finally retired from use. Ideally, each phase in the life cycle can be checked for correctness before moving on to the next phase.
The entity relationship (ER) data model has existed for over 35 years. It is well suited to data modelling for use with databases because it is fairly abstract and is easy to discuss and explain. ER models are readily translated to relations. ER models, also called an ER schema, represented by ER diagrams.
Normalization should be part of the database design process. However, it is difficult to separate the normalization process from the ER modelling process so the two techniques should be used concurrently.
Use an entity relation diagram (ERD) to provide the big picture, or macro view, of an organization’s data requirements and operations. This is created through an iterative process that involves identifying relevant entities, their attributes and their relationships.
One important theory developed for the entity relational (ER) model involves the notion of functional dependency (FD). The aim of studying this is to improve your understanding of relationships among data and to gain enough formalism to assist with practical database design.