Today in this article, we will go over Data Modelling – Fundamentals and Building Blocks and their usage guidelines.

What is Data Modelling

Data modeling is an important technique for effective database design, understanding the data requirements of a particular use case, and offering a structured method for understanding, representing, and implementing data structures for a given requirement.

Data Modelling normally occurs at the beginning of the development process to understand and document business requirements before database implementation.

This means it heavily helps you identify and documentation of business logic, rules, and relationships to ensure that the data model aligns with business objectives.

Data modeling focuses on organizing data at a Conceptual or Abstract or Physical level.

It aims to understand the information requirements of a business or system and define the relationships between different data entities.

Data modeling utilizes techniques like Entity-Relationship Diagrams (ERD) to represent entities, attributes, and relationships between entities, but this evolution and brainstorming does help you define your requirements in a true sense.

Along with facilitating discussions, it ensures changes to the design are visited during the early stages of development. In the world of development, which fully relies on design in the first place this does help in a greater way.

Data Modelling encourages communication between various stakeholders including the product owner, scrum, developers, or architects by providing a visual representation of data concepts that is accessible to both technical and non-technical audiences.

It very good technique for extending logical and physical design, normalization, relationship mapping, and performance optimization at the very beginning of use case or requirements explorations.

SQL and NO SQL – Data modeling is still a valid technique

Data Modelling operates at a more elevated level of abstraction, stressing entities, attributes, and relationships without getting into specific database implementation details, and hence this technique’s value holds for SQL or No SQL database design.

The outcomes of Data modeling ensure conceptualization, understanding, and representation of data requirements to various stake stakeholders and hence build confidence in the product which is going to evolve later stage as deliverables.

Type of Data Modeling Concepts

Conceptual Data Modeling

• Focuses on the high-level representation of the domain entities, business concepts, and relationships within a system.

• Helps in getting all the the fundamental entities, attributes, and relationships between.

• Provide a clear and abstract view of the business requirements, serving as a foundation for further development.

Logical Data Modeling

• This modeling helps translate the conceptual data model into a more detailed and structured format that can be implemented in a database.

• Defines tables, columns, fields, data types, relationships, and constraints for the given database management system.

• It acts as a bridge between the conceptual model and the physical implementation, ensuring a comprehensive and consistent representation of data.

Physical Data Modeling:

• Focuses on the actual implementation of the database on a specific database management system SQL or No SQL.

• Includes details such as storage structures, indexing, partitioning, and other optimization techniques to enhance performance.

• Involves decisions about how data is stored, accessed, and retrieved at the physical level, considering the specific features and requirements of the chosen DBMS.

Building blocks of Data modeling

Entities in Data Modelling

The best way to judge an entity is as some type of “real-world subject.”

At the end entity represents ” a collection of attributes or fields.”

Entities represent distinct objects, concepts, or things within a system that are described and stored as data.

These entities encapsulate relevant information through attributes, defining the properties or characteristics associated with them.

For example, in a customer relationship system, a “Customer” entity might include attributes such as “CustomerID,” “Name,” and “Email.”

Identifying and modeling entities is foundational for a clear understanding of the information requirements and relationships within a given domain or system.

Attributes in Data Modelling

Attributes provide granularity to the data model, offering a way to distinguish and categorize information within entities.

By defining these attributes, data modeling achieves a more comprehensive representation of the underlying data, enabling the organization and retrieval of specific details associated with each entity in a structured and meaningful manner.

Characteristics or properties of entities that describe the data associated with them.

Attributes provide details about the entities and are often represented as adjectives, like “Name” or “DateOfBirth.”

Relationships in Data modeling

In data modeling, relationships establish connections between entities, defining how they interact. T

These connections convey associations and dependencies, crucial for understanding the dynamics within a system.

For instance, in a database for a library, a “Customer” entity may have a relationship with an “Order” entity, indicating the association between individuals requesting an order.

• Relationships specify the cardinality and nature of connections, such as one-to-one or many-to-many, providing a structured framework for data representation.

• By modeling relationships, data modeling ensures a comprehensive understanding of how entities relate to one another, fostering accurate and efficient retrieval of information from the database.

Keys in Data modeling

• Keys play a pivotal role in data modeling by uniquely identifying and organizing data within a database.

• In RDBMS or SQL worlds example – the “primary key” serves as a distinct identifier for each record, ensuring data integrity.
  • Foreign keys establish relationships between tables, linking data across entities.
  • Index keys enhance query performance, enabling swift data retrieval.
  • Composite keys combine multiple attributes to create a unique identifier.

• In No SQL Unique Keys identify documents or entries within a collection, ensuring data integrity.
  • In document-oriented databases, like MongoDB, keys often include a unique identifier, such as the document’s “_id.”
  • In key-value stores, like Redis, keys serve as direct access points to values.
  • NoSQL databases may also use composite keys or secondary indexes for diverse query patterns.
  • Keys are crucial for optimizing data access, retrieval, and navigation in NoSQL databases, contributing to performance in managing various data structures and relationships.

Benefits of Data Modeling

Below are the top-level benefits of data modeling.

• Creating a structure for collaboration between your IT and business teams.

• Revealing opportunities for improving business processes by defining data needs and uses.

• Saving time and money on IT and process investments through appropriate planning.

• Reducing errors (and error-prone redundant data entry) while improving data integrity.

• Increasing the speed and performance of data retrieval and analytics by planning for capacity and growth.

• Setting and tracking target key performance indicators tailored to your business objectives.

Data modeling Tools

Here are a few useful tools for Data modeling examples:

• erwin Data Modeler is a data modeling tool based on the Integration DEFinition for information modeling (IDEF1X) data modeling language that now supports other notation methodologies, including a dimensional approach.

• Enterprise Architect is a visual modeling and design tool that supports the modeling of enterprise information systems and architectures. It’s based on object-oriented languages and standards.

• ER/Studio is database design software that’s compatible with several of today’s most popular database management systems. It supports both relational and dimensional data modeling.

• Open Source tools include open-source solutions such as Open ModelSphere.

That’s all! Happy coding!

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Today in this guidelines section, we will cover a few useful guidelines to get Faster Insert and Update in Entity Framework Core.

Although the performance of SQL or entity framework query is a vast subject and has many aspects which are important.

However, this guideline details some common guidelines for making your read query faster and helping you achieve efficient updating.

Indexing the Fields in SQL

• A powerful method for facilitating effective database searches is indexing.

• Here, you searched through the sorted index first in order to increase performance rather than running a full database scan (scanning through everything).

• It’s the most effective way to manage the database scan.

• Allows you to effectively manage the scanning of each document. Please be aware that every single document in a collection will undergo a thorough scan if there are no indexes.

• It gives a query the ability to focus results set using the index.

• Indexing improved the efficiency of queries.

Indexes as AntiPattern in SQL query

• Don’t create Indexes that are not used or used rarely.

• Build indexes on only fields that are used often in the query.

• Use Composite indexes for filtering multiple columns.

• Avoid duplicate indexes.

Schema Modelling for Database

Your DB Schema modeling normalization plays important role in the performance of EFCore queries.

You need to perform Normalization and denormalization of domain entities and create them appropriately even before you could load the data the first time.

The benefits of schema modeling are as below,

• Optimize the query performance.

• Help you focus on reading and writing only selected domain entities.

• Eliminate heavy use of joins or evaluation of queries.

Break your class into small class entities.

The below example gives a small glimpse of what is expected by schema modeling.

Below Employee class is broken into small entities like Employee, Contact, SocialSecurity, etc.

Read Optimization – Don’t Load the entire Entity in the Application Memory

An Entity consists of multiple properties which are internally mapped to multiple columns in a respective Database table.

Below Example loads entire Employee entities.

Let’s say an example: If Employee has 100 columns then EF will load all 100 columns even if they are not used.

 foreach (var employee in _context.Employee)
            {
                /// Do something here 
                Console.WriteLine("EmployeeID : " + employee.EmployeeId);
            }
            _context.SaveChanges();

It’s not recommended to load the DB Context entity entirely in memory due to the below issues,

• It could cause huge memory problems if you are loading the entire Entity set (if not aware of the size of the set).  This brings all the Employee row data to the client side causing performance issues.

• Loading unnecessary data could add additional load for processing the queries.

We can optimize the above code by using the Select below,

Content-Based Optimization – Limit your query resultset

• Another optimization technique is to limit the resultset by means of some matching filter.

• Use paging or Filter matching criteria can be derived using business requirements, if possible.

• Content-based loading will let you Load only data that is much sufficient to serve the purpose. Exa UI-based application.

Update Optimization: Use of BulkUpdate

EFCore doesn’t support bulk updates as of now.

But below is the example that developer tends to write.

Here we are going to add preceding zeros to all employee’s ID fields using EFCore.

     foreach(var employee in _context.Employee)
            {
                employee.EmployeeId = employee.EmployeeId.PadRight(4, '0');
                Console.WriteLine("EmployeeID : " + employee.EmployeeId);
                _context.SaveChanges();
            }
            

In the above code,

• The first database roundtrip is performed by code _context.Employee brings all employee data on client-side code execution.

• The second database round trip is performed to save the changes. However 2nd roundtrip can be further optimized into a few more round trips depending on the database provider type and the amount of data.

• _context.SaveChanges() is being called for every record which could affect the performance.

The above code can be further optimized by calling SaveChanges at the end of foreach loop.

The second option can be further optimized as below by calling a raw stored procedure

_context.Employee.FromSqlRaw("UPDATE [EmployeeDB] SET [EmployeeId] =  RIGHT( '0000' + EmployeeId, 4)");

Hope you find these guidelines useful. We will see some more guidelines in the next updates. Hence kindly stay tuned!

Do you have any comments or ideas or any better suggestions to share?

Please sound off your comments below.

Happy Coding !!

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Today in this article, we will discuss MongoDB Security guidelines and Best practices.

Security measures can be applied while data is at rest i.e. storage level security and while data is at the move.

Obviously, Security is not an afterthought process. You must build your system considering the security and risk exposure. You also need to design a risk mitigation process just in case there is a need.

As with all software, MongoDB administrators must consider security and risk exposure for a MongoDB
deployment.

We will cover the below aspects in today’s article,

User Rights Management – Enable Authentication and Authorization

Control access to sensitive data authentication and authorization at the database level.

Define permissions for a user or application and define what data the application or user can access.

Provision users defined roles defining different entities accessing and managing the data.

• Restrict write access at the application level or admin level.

• Restrict access as ReadOnly for non-admin members.

Use any custom authorization or leverage LDAP level integration to verify (the user using a centralized Server.)

Protect Sensitive Data at Rest – Encryption and Decryption

Protecting data at rest in persistent storage is important. Field-level encryption is recommended for protecting any PII, or PHI, or PFI information.

Leverage MongoDB-supported encryption and decryption mechanism.

• Field-level encryption is supported for MongoDB 4.2 and onwards support.

• With client-side field-level encryption, developers can encrypt fields on the client side.

• Encrypt fields are supported without any server-side configuration or directives.

• Client-side field-level encryption in the .NET/C# driver is currently only supported on x64-compatible CPUs.

For more details visit,

• MongoDB Encryption- Field Level Encryption and Decryption C#

Protect Data in Transit

The below guidelines are more generic but ideal for most scenarios to implement secured data transfer.

• Implement encryption to protect the integrity of data when data transit for two-way communications.

• Enforce user-defined encryption requirements based on the enterprise’s organizational and compliance requirements by following standards, guidelines, and best practices.

• Add Encryption of data in transit using TLS v1.2, and above cipher suites for all publicly accessible sites or APIs or Applications.

• TLS (Transport Layer Security) and SSL (Secure Sockets Layer) are the primary means to over the wire or in-transit data.

• Enforce HTTPS-secured transport protocol for accessing websites, API and database connection, etc.

Data Monitoring or Database activity monitoring (DAM)

• Monitor Data Base Access using Health check monitoring tools if any.

• Whitelist and Backlist of IP and environment to control enterprise access.

• Detect any anomalies or attempts to move data outside of defined boundaries.

• Monitor Databases against malicious activity by firewalls and intrusion.

• Audit and monitor all database activity separately, such as privileged user activities and SELECT transactions, without a loss of performance.

• Produce notifications or alerts when a policy violation is identified.

• Prevent tampering or manipulation of database logs.

• Use of Database activity monitoring (DAM) tool (if suitable ) can be used to report any identify and report fraudulent, illegal, or other undesirable behavior.

Implement secure key and certificate management

• Implement secure key and certificate management process.

• Store keys and certificates securely.

• Apply strict access control on key access.

Restrict Network Access

• Secure network access to your MongoDB server. Configure firewall and RBAC to allow connection only from Authorized IPs or networks.
• Follow the Principle of Least Privilege.
• Restrict administrative access to a limited number of trusted personnel.

Upgrade to the Latest MongoDB version

• Plan and be ready to regularly update MongoDB to the latest stable version.
• New releases often include security patches and bug fixes, so staying up to date helps protect against known vulnerabilities.

Implement – Data backup policy

• Implement a regular backup strategy to ensure a copy of your data is available in case of data loss, data corruption, or any security incidents happens.

• Follow backup best practices, such as storing backups in secure locations and testing the restoration process.

Implement – Disaster Recovery plan

Implement a robust disaster recovery strategy. It is important along with regular backup of MongoDB data to a secure place.

Your downtime and recovery time should address your enterprise application requirements which may be dependent on multiple factors.

Perform Security Audits

Conduct security audits and vulnerability assessments to identify and address any security weaknesses in your MongoDB deployment.

This can include performing penetration testing, code reviews, and infrastructure assessments.

Summary

Security is not an afterthought process in fact it is an ongoing process, and it’s important to stay alert and assertive in managing and improving the security of your MongoDB Database.

Do you have any comments or ideas or any better suggestions to share?

Please sound off your comments below.

Happy Coding !!

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