The main technical goal as a Software Developer is to Formalize Everything into Data Model. This makes it possible to personalize, make it repeatable and ready for automation. Probably almost every logic could be represented in an abstract computer model.
Object Oriented Modeling
You could map real world models into classes. Object oriented programming was understood even by Steve Jobs. It elevated the programming at much higher level – away from the pure technical representation of data and logic. It has some great features:
-
Abstraction is the process of selecting only relevant data about an object and hiding the unnecessary details. In OOP, classes use the concept of abstraction to define the properties and behaviors of objects.
-
Encapsulation is the mechanism of bundling the data (variables) and the methods (functions) that operate on the data into a single unit called an object. It helps in data hiding, which protects the integrity of the data.
-
Inheritance allows a class (subclass or derived class) to acquire the properties and behaviors of another class (superclass or base class). This enables the reusability of code and the creation of a hierarchy of classes.
-
You use Polymorphism to allow objects of different classes to be treated as objects of a common superclass, thereby enabling them to be used interchangeably. This feature facilitates the flexibility and extensibility of the code.
-
Class is a blueprint for creating objects, providing initial values for state (member variables or attributes) and implementations of behavior (member functions or methods). Objects are instances of classes, and they represent real-world entities.
-
Method Overloading and Overriding refers to the ability to define multiple methods with the same name but with different parameters within the same class. Method overriding occurs when a subclass provides a specific implementation of a method that is already provided by its superclass.
-
Association, Aggregation, and Composition: These are the different forms of relationships that can exist between objects. Association represents a relationship where objects are independent of each other, aggregation represents a “has-a” relationship, and composition represents a “part-of” relationship.
Database Systems (DBMS) features
DBMSs are designed to manage data using entity models. You could use Relational Database Systems, because they offer a lot of functionalities about the data – out of the box that may be needed.
-
DDL & DML Data Definition Language allows users to define the database schema and structure, including creating, altering, and dropping database objects such as tables, views, indexes, and constraints. Data Manipulation are commands for inserting, updating, and deleting data within the database. These commands enable users to interact with the data stored in the tables.
- Normalization and Denormalization – The information is cut in such peaces that no or very little information actually duplicates. Very high level of normalization may lead to query complexity, so it should be used with care.
-
Data Querying and Retrieval: SQL enables users to query and retrieve data from the database using the SELECT statement. JOIN and UNIONs allows for complex queries, filtering, sorting, and aggregating data based on specified criteria.
-
Transactions, Data Integrity and Constraints: SQL databases support various integrity constraints, including primary keys, foreign keys, unique constraints, and check constraints, which help maintain data accuracy and consistency. This is archived with transaction control features, such as commit, rollback, and savepoints. Transactions allow a set of database operations to be treated as a single unit, ensuring that all operations either succeed or fail together.
-
Data Security: SQL databases offer security features to control access to data, including user authentication, authorization, and data encryption. Users can be assigned specific roles and permissions to restrict access to sensitive data.
-
Concurrency Control: SQL systems implement concurrency control mechanisms to manage access to data by multiple users simultaneously, preventing conflicts and maintaining data consistency in multi-user environments.
-
Indexing and Performance Optimization: SQL databases use indexes to improve the performance of data retrieval operations. Indexes enable faster data access by creating a data structure that allows for efficient lookup and retrieval of specific data records.
Non-Structured Systems
There is a tendency nowadays to use NoSQL systems more.
-
Scalability: NoSQL databases are designed to handle large amounts of data and can easily scale horizontally, distributing data across multiple servers and enabling the system to handle increased load by adding more machines to the database cluster.
-
Flexibility in Data Models: NoSQL databases allow for flexible and dynamic data models, such as key-value pairs, columnar, document-based, and graph-based data models. This flexibility enables developers to store and manage various types of data without requiring a predefined schema.
-
High Performance: NoSQL databases are optimized for specific data models and access patterns, allowing for high performance and low latency. They are often well-suited for use cases that involve large-scale data processing, real-time applications, and high-speed data retrieval.
-
Distributed Architecture: NoSQL databases are typically designed with a distributed architecture. This enables you to spread the data across multiple servers or nodes. This distribution helps in achieving fault tolerance, high availability, and load balancing.
-
Replication and Sharding: NoSQL databases often support data replication and sharding. You could ensure data availability, fault tolerance, by distributing data across multiple nodes for improved performance.
-
Schema-less Design: NoSQL databases typically do not enforce a rigid schema like traditional RDBMS. This allows for greater flexibility when dealing with evolving data structures and the ability to store heterogeneous data.
-
Designed for Big Data: NoSQL systems are designed to handle the challenges posed by the storage and processing of large volumes of data, making them suitable for applications that deal with big data and real-time analytics.
-
Many NoSQL systems prioritize availability and partition tolerance over strict consistency. They provide eventual consistency in which data is expected to be consistent over time, given that the system is not experiencing failures or network partitions.
Example use
My latest project has some information workflows that I’ve modeled into a database. They include checks, transformations, flow process, generator.
- Process Activation may be implemented with simple list of regular expressions that the input must match
- Data Transformation could be implemented with simple input (find) and output (replace) parameters.
- Building an app could be implemented with a data model:
- current working directory
- list of code generators
- builder (executable file) and some additional parameters
- file uploader (executable file) – to transfer – from known output location of the builder to some app store.
Probably (Almost) Every logic could be formalized to a Data Model and made into an automatic application. This is the great philosophical goal of technology.
Artifitial Intelligence systems help a lot on more heuristic topics. Their super power is to use the past knowledge.
Some subjective aspects of human experience may be challenging to formalize. Some few areas are probably in the niche of creativity, music, emotions and nuances of human interaction, natural events.