For any data-driven application, the ability to efficiently connect to databases and perform CRUD operations is critical. .NET provides extensive support for connecting to and working with both traditional relational databases as well as modern NoSQL databases.

This comprehensive guide covers key database connectivity technologies, architectures, and best practices for .NET developers. Whether building enterprise business applications or public-facing cloud services, understanding optimal approaches for data access can have profound implications for scalability, performance, and developer productivity.

Relational Database Connectivity

For decades, relational databases like SQL Server, MySQL, and Oracle have dominated enterprise data persistence thanks to ACID compliance and rock-solid data integrity guarantees. .NET offers several major approaches for working with relational databases:


ADO.NET is the traditional low-level data access technology for .NET allowing the execution of raw SQL statements and stored procedures along with handling lower-level details like connection pooling, transactions, and parameter binding.

With ADO.NET, developers work directly with classes like SqlConnection, SqlCommand, DataReader and take full responsibility for overall data access code. Key capabilities include:

  • Executing Commands– The SqlCommand object allows executing parameterised SQL/stored procedures and mapping results.
  • Reading Data– The SqlDataReader provides forward-only, read-only access to query results via streaming.
  • Updating Data– The SqlCommandBuilder helps generate SQL for insert, update, delete operations.
  • Transactions– System.Transactions namespace coordinates distributed ACID transactions.

While very flexible, ADO.NET requires significant repetitive boilerplate code which led to the development of more modern ORMs. Hiring .NET developers well-versed in ADO.NET ensures they can work at any level of abstraction.

Entity Framework Core

Entity Framework (EF) Core is now the de-facto object-relational mapper for .NET – allowing developers to work with databases via .NET objects thus minimizing repetitive data access code. Using EF Core:

  • DB Sets Map to Collections– Entire database tables are mapped to IQueryable object collections for querying.
  • Entities Map to Objects– Each database row maps directly to an entity POCO object in .NET.
  • Business Logic in .NET– Business logic for data manipulation resides in clean C# or VB.NET code rather than SQL.
  • Change Tracking– EF Core handles change tracking to synchronize object state back to database.
  • Migrations– DB schema changes are version controlled via code-based migrations that can be deployed across environments.
  • Lazy Loading– Related objects are loaded on demand to avoid over-fetching data.
  • Bulk Operations– Bulk insert, update, delete operations minimize database round trips.

EF Core works with SQL Server, Azure SQL, Oracle, MySQL, PostgreSQL and more. For most scenarios, EF Core is optimal for data access in greenfield .NET apps. When recruiting .NET teams, ensure EF expertise.


Dapper is a “micro-ORM” that provides a lightweight wrapper over ADO.NET. Dapper adds convenience methods for mapping query results to objects while retaining tight control for writing queries. Key features include:

  • Parameterized Queries– Queries defined using params for protection against SQL injection.
  • POCO Mapping– Map emitted query results directly to plain old .NET objects.
  • Extension Methods– Dapper methods cleanly extend IDbConnection interface.
  • Multiple Platforms– Works across SQL Server, MySQL, PostgreSQL and more.
  • High Performance– The simplified object mapping is highly optimized for speed.

For simpler data access needs, Dapper provides high speed data access without the overhead of full ORMs. Seek out .NET developers comfortable with both EF Core and Dapper.

Database Connectivity Approaches

When architecting .NET applications, there are several major approaches for structuring database connectivity:

  • Database First– This traditional approach involves designing the database schema first and then modeling the .NET domain entities based on the database structure. Database first can enable faster prototyping but creates tighter coupling long term.
  • Code First– With Code First, the domain model is designed in .NET code and then used by EF Core to generate the database schema. This allows rapid application development with frequent schema changes.
  • Model First– For Model First, an abstract model is created initially (either in UML or EF Core Power Tools designer) and then used to automatically generate both database and .NET code artifacts.
  • Database Per Service– Microservices architectures often isolate databases on a per service basis for loose coupling. This puts the burden of managing data consistency across services.
  • Shared Database– A monolithic database is shared across multiple services to simplify data management. Downside is tighter coupling and constraints on granular scalability.

Choosing appropriate connectivity patterns is key. Involving experienced .NET architects and developers early on helps set optimal data access strategy.

Entity Framework Core Code First

Given its flexibility, EF Core Code First has emerged as the predominant choice for greenfield .NET development so it’s worth exploring in more depth. Some key capabilities include:

  • POCO Entity Classes– Clean separation of business logic from data access code.
  • Fluent API Configuration– API for configuring domain mappings, indexes, conventions, etc.
  • Schema Generation– EF can automatically create SQL database schema based on model.
  • Querying– LINQ support for writing strongly typed queries against entities.
  • Change Tracking– Detect and persist entity changes to keep DB synchronized.
  • Lazy Loading– Related entities automatically loaded only when referenced.
  • Eager Loading– Prefetch related data via Include() to reduce round trips.
  • Concurrency– Optimistic concurrency via row versioning and handling conflicts.
  • Caching– Multiple levels of caching help performance including in-memory, second level, and query caching.
  • Migrations– Alter database schema via incremental migrations that can be applied across environments.
  • Connection Resiliency– Built-in resilience policies handle transient connection issues.

EF Core works with relational databases but can also bridge to NoSQL stores. Seek out .NET developers familiar with EF Core features to assess their depth of knowledge.

NoSQL Databases

While relational databases remain prevalent, NoSQL databases have risen in popularity – especially for cloud-native applications that require flexible schemas, elastic scaling, and high availability. .NET provides multiple approaches for working with NoSQL stores:


The open-source MongoDB document database is extremely popular backend for web and mobile apps thanks to its flexibility and horizontal scalability. Microsoft offers excellent .NET support:

  • Official C# Driver– Fully featured, idiomatic driver maintained by MongoDB.
  • LINQ– LINQ queries can be composed to find documents based on structured criteria.
  • Aggregation Framework– Special API for defining complex data aggregations like groupby, join, graph operations.
  • GridFS– Integrated solution for storing and retrieving large binary files in chunks.
  • GeoSpatial– API for querying location-based geospatial data and indexes.

For developers with prior MongoDB experience, they’ll feel right at home using MongoDB from .NET.

Azure CosmosDB

Azure CosmosDB is Microsoft’s globally distributed database service for mission-critical applications. As a multi-model DB, it natively supports document, key-value, wide-column, and graph data models. Key integrations include:

  • Turnkey Global Distribution– Easy replication across any number of Azure regions worldwide.
  • Multiple APIs– Compatible SQL, MongoDB, Cassandra, Gremlin/Graph APIs to migrate apps.
  • Change Feed– Built-in Change Feed API for reacting to data changes.
  • LINQ– Full LINQ support for querying documents.
  • Serverless– Consume as a fully managed DB requiring no server provisioning.

CosmosDB is a leading choice for cloud-native apps that require elastic scalability and global reach. Seek out .NET developers well versed in CosmosDB.

Amazon DynamoDB

Amazon DynamoDB is a fully managed NoSQL database service from AWS. DynamoDB simplifies working with key-value and document data in the cloud. Integrations include:

  • Managed Service– DynamoDB runs exclusively on AWS infrastructure – no servers to provision.
  • High Performance– Optimized for SSD storage and in-memory caching.
  • Global Tables– Multi-region redundancy for high availability.
  • Security– Encryption at rest + fine grained identity/access controls.
  • Transactions– ACID transactions for consistency across items.
  • Streaming– React to data changes via DynamoDB Streams.
  • Caching– DynamoDB Accelerator (DAX) for microsecond latency.

For teams already familiar with AWS, DynamoDB is appealing for its tight cloud integration. It is best to hire .NET developers with AWS experience.

Key-Value Stores

For simple data access patterns, fast key-value stores offer outsized performance gains. Top options include:

  • Redis– Open source in-memory data store – very fast with .NET client.
  • Azure Cache for Redis– Fully managed Redis hosted in Azure.
  • Memcached– In-memory caching system with .NET client libraries.

Caching data in distributed in-memory KV stores is a proven performance optimization. When evaluating .NET developers, comfort working with diverse storage systems – relational and NoSQL – is advantageous.


This comprehensive guide underscores why data access design and implementation represent one of the most crucial considerations for any impactful .NET application. From architecting seamless connectivity using ORMs to applying proven data access patterns and optimizing performance at scale, how you work with data can have oversized influence on end user experience. Leverage the database connectivity in react by opting for dedicated development team who deeply understand modern best practices for accessing both relational and non-relational stores is key. For modern distributed applications, also consider engaging specialized data engineering teams to collaborate on building performant, scalable data access architecture. By mastering database connectivity in .NET, your development teams gain a major competitive advantage in crafting responsive, flexible applications able to fluidly evolve.