DEV Community: Balram Prasad

Elevate Your REST API: Add Robust Validation with Fluent Validation in Azure App Service

Balram Prasad — Mon, 18 Mar 2024 06:00:39 +0000

In the digital era, REST APIs are the backbone of web services, powering web and mobile applications alike. Ensuring data integrity and security through robust validation is not just recommended; it's essential. This tutorial guides you through adding powerful validation to your REST API using Fluent Validation and deploying your application to Azure App Service for a seamless, cloud-based solution.

Why Choose Fluent Validation?

Fluent Validation is a .NET library that allows for the creation of strongly-typed validation rules. Its popularity stems from its flexibility, ease of use, and integration capabilities with ASP.NET. Unlike traditional data annotations, Fluent Validation rules are centralized, easily reusable, and can be tested independently from your UI.

Setting Up Your Project

Start by creating a new ASP.NET Core Web API project in Visual Studio or VS Code. Ensure your project targets .NET 5 or later for the best compatibility with Fluent Validation.

Installing Fluent Validation

Add Fluent Validation to your project via NuGet Package Manager or the CLI:

dotnet add package FluentValidation.AspNetCore

Creating Your First Validator

Assume we have a simple Movie model. Let's create a validator for it:

using FluentValidation;

public class MovieValidator : AbstractValidator<Movie>
{
    public MovieValidator()
    {
        RuleFor(movie => movie.Title).NotEmpty().WithMessage("Title is required.");
        RuleFor(movie => movie.ReleaseYear).InclusiveBetween(1900, DateTime.Now.Year);
        // Add more rules as needed
    }
}

Integrating Fluent Validation with ASP.NET Core
In your Startup.cs or Program.cs (for .NET 6+), add the following line to configure services:

services.AddControllers().AddFluentValidation(fv => fv.RegisterValidatorsFromAssemblyContaining<MovieValidator>());

This automatically applies Fluent Validation to your models when they're bound to a request.

Testing Validation Locally

Before deploying, test your API locally. Use tools like Postman or Swagger to ensure your validation rules are triggered as expected when you make requests to your API endpoints.

Deploying to Azure App Service

Azure App Service offers a fully managed platform for deploying and scaling web applications. Deploy your API to Azure App Service directly from Visual Studio or use the Azure CLI:

az webapp up --name <your-app-name> --resource-group <your-resource-group> --runtime "DOTNET|5.0" --plan <your-app-service-plan>

Replace placeholders with your actual app name, resource group, and service plan details.

Exploring Fluent Validation Documentation
Dive deeper into Fluent Validation by exploring the official documentation. It offers extensive guides, best practices, and advanced scenarios to further enhance your API validation logic.

Watch the Tutorial for More Insights

Prefer a visual guide? Watch our detailed tutorial, "Elevate Your REST API: Add Robust Validation with Fluent Validation in Azure App Service," where we cover everything from setting up Fluent Validation to deploying on Azure, with real-world examples and best practices.

Watch the Full Tutorial Here

This video complements the guide, offering step-by-step visuals to ensure you have all the tools you need to implement robust validation in your REST APIs and deploy confidently with Azure App Service.

Conclusion

Validating input data is a cornerstone of secure and reliable API development. With Fluent Validation and Azure App Service, you're equipped to elevate your APIs, ensuring they're not only robust and scalable but also secure and compliant with industry standards.

Got questions or feedback? Drop a comment below or in the video comments section. Happy coding!

Master Real-Time Fraud Detection with Azure Stream Analytics

Balram Prasad — Tue, 05 Mar 2024 01:43:02 +0000

In the digital age, real-time fraud detection isn't just a luxury—it's a necessity. That's why I'm thrilled to share a step-by-step tutorial on harnessing Azure Stream Analytics and Event Hubs for detecting fraud in phone call metadata.

What You'll Learn:

Setting up Azure Event Hub for real-time data ingestion
Creating and configuring an Azure Stream Analytics job
Writing effective queries using tumbling windows for fraud detection
Outputting results to Azure Blob Storage for further analysis
This guide is designed for developers and IT professionals looking to leverage Azure for advanced data processing tasks. I've included plenty of code snippets, configuration tips, and insights into Azure's powerful analytics capabilities.

Dive into the tutorial here:

Your feedback and experiences with Azure for real-time processing would be great to hear!

How to Deploy a RESTful API with .NET Core to Azure App Service Using Azure SQL and Entity Framework

Balram Prasad — Mon, 04 Mar 2024 05:43:54 +0000

Hey Dev.to community!

I'm excited to share with you all a comprehensive guide that walks through the process of building and deploying a RESTful API using .NET Core, integrated with Azure SQL via Entity Framework, and then deploying it to Azure App Service — all within Visual Studio.

What You'll Learn:

Creating a RESTful API with .NET Core, detailed from scratch.
Integrating Azure SQL Database using Entity Framework for smooth and efficient data management.
Effortless Deployment from Visual Studio to Azure App Service, making your API live and accessible to the world.
Testing Your API both locally using Swagger UI and in the cloud post-deployment.
This tutorial is aimed at developers of all levels who are interested in stepping into the world of cloud services with Azure, or those looking to consolidate their knowledge by applying it to a practical project.

Why This Matters:

Deploying applications to the cloud is a critical skill for modern developers. This guide aims not only to teach you how to deploy an application but also to give you a clear understanding of working with Azure services and .NET Core.

Check out the full video tutorial here:

I'm looking forward to your feedback, questions, or insights in the comments below! Let's make the cloud our playground. 🚀

NETCore #Azure #RESTfulAPI #CloudComputing #DeveloperTutorial

Unlock the Power of Azure: A Video Playlist for Aspiring Data Engineers and Analysts

Balram Prasad — Thu, 29 Feb 2024 05:56:59 +0000

Dive into the world of Azure with our comprehensive video playlist designed for data engineers and analysts. From real-time analytics to data wrangling and beyond, this series provides you with the knowledge to leverage Azure's full suite of data services. Enhance your skills, whether you're a beginner or looking to expand your expertise with Azure.

Explore Our Curated Azure Video Series

Our playlist includes in-depth tutorials and demos across various Azure services. Here are 20 must-watch videos from our collection:

Why This Playlist?

Diverse Azure Solutions: From analytics to data processing, explore a wide range of Azure's capabilities.
Hands-On Learning: Each video includes practical demonstrations, making learning engaging and effective.
For All Skill Levels: Whether you're just starting out or looking to deepen your Azure knowledge, there's something for everyone.

Join Our Azure Learning Community

Dive into our playlist and start your Azure mastery journey today. Share your progress, insights, and questions in the comments below. Let's grow together in harnessing the power of Azure for data engineering and analytics.

Master the Art of Azure Data Factory

Balram Prasad — Mon, 20 Feb 2023 02:23:02 +0000

Excited to share latest video series on Azure Data Factory! Learn the basics of ADF, how to debug pipelines and activities, and various techniques to copy data from different sources to different destinations. Whether you're a beginner or an experienced user, you're sure to learn something new! Check out the full playlist here

The playlist is focused on teaching various aspects of Azure Data Factory, which is a cloud-based data integration service that allows you to create, schedule and manage data pipelines. It includes videos covering the basics of Azure Data Factory, how to create data pipelines, how to debug pipelines and activities, how to do data wrangling, how to use data flows, how to create and use a self-hosted integration runtime, and how to copy data from various sources to destinations like Azure Blob Storage, Snowflake, and Azure Cosmos DB. There are also videos on how to connect Azure Data Factory with other services like Log Analytics and Azure DevOps, and how to run SSIS packages and R scripts using Azure Data Factory. The playlist covers a range of topics that will be useful for those looking to learn more about data integration in the cloud with Azure Data Factory.

Introduction to azure data factory | What is Azure Data Factory | Create Data Factory First Pipeline

How to Debug Pipeline and Activity in Azure Data Factory

Azure Data Factory - Incrementally load data from Azure SQL to Azure Data Lake using Watermark

How to create and use data flow in Azure Data Factory

Azure Data factory Data wrangling | How to do Data wrangling in Azure Data Factory

Azure Data Factory - Incremental Load From SQL Managed Instance to Lake using Change data Capture

Azure Data Factory-Incrementally copy files based on LastModifiedDate by using the Copy Data tool

How to create Self-hosted Integration runtime in Azure Data Factory and Azure Synapse analytics

Copy data from a On Premise SQL Server database to Azure Blob storage using azure data factory

Azure Data Factory-Incrementally copy files based on time partitioned file name using Copy Data tool

How to use Azure Data Factory with snowflake | Copy data from Snowflake to Azure Blob using ADF

Microsoft Azure SQL Server Database to Snowflake using azure data factory in minutes

Copy Data from Snowflake to Azure Cosmos DB using azure data factory

Load data from Azure Blob to Snowflake using Azure Data Factory

How to Connect Azure Data Factory with Log Analytics and setup alerts

Create an Azure Data Factory, Storage ,Linked Service , Datasets , Pipeline using Bicep

Create an Azure Data Factory, Linked Services, Datasets, Pipeline , Run and Monitor using PowerShell

How to use Azure Data Factory with managed virtual network and private endpoints

How to connect azure devops repo with azure data factory

Create an Azure-SSIS integration runtime in Azure Data Factory or Azure Synapse Analytics

How to Run an SSIS package in Azure Data Factory

Run R scripts as part of a pipeline through Azure Data Factory using Azure Batch

How to Run an Azure Databricks Notebook with Azure Data Factory

AzureDataFactory #CloudDataIntegration #DataWrangling #DataEngineering

Create an Azure Data Factory, Storage ,Linked Service , Datasets , Pipeline using Bicep

Balram Prasad — Tue, 27 Dec 2022 05:44:27 +0000

This Demo describes how to use Bicep to create an Azure data factory. The pipeline you create in this data factory copies data from one folder to another folder in an Azure blob storage.

Basic Role and User Management using SQL in snowflake

Balram Prasad — Mon, 21 Nov 2022 20:08:06 +0000

IoT remote monitoring and notifications using Azure IotHub, Service Bus and Raspberry and Logic Apps

Balram Prasad — Mon, 21 Nov 2022 19:49:48 +0000

IoT remote monitoring and notifications with Azure Logic using IotHub, Service Bus and Raspberry

Azure IoT Hub provides a cloud-hosted solution back end to connect virtually any device. Extend your solution from the cloud to the edge with per-device authentication, built-in device management, and scaled provisioning. Security-enhanced communication channel for sending and receiving data from IoT devices.

Azure Service Bus is a fully managed enterprise message broker with message queues and publish-subscribe topics (in a namespace). Service Bus is used to decouple applications and services from each other, providing the following benefits:

Load-balancing work across competing workers
Safely routing and transferring data and control across service and application boundaries
Coordinating transactional work that requires a high-degree of reliability

The Azure IoT explorer is a graphical tool for interacting with devices connected to your IoT hub. This article focuses on using the tool to test your IoT Plug and Play devices. After installing the tool on your local machine, you can use it to connect to a hub. You can use the tool to view the telemetry the devices are sending, work with device properties, and invoke commands.

Azure Logic Apps is a cloud-based platform for creating and running automated workflows that integrate your apps, data, services, and systems. With this platform, you can quickly develop highly scalable integration solutions for your enterprise and business-to-business (B2B) scenarios. As a member of Azure Integration Services, Azure Logic Apps simplifies the way that you connect legacy, modern, and cutting-edge systems across cloud, on premises, and hybrid environments.

The Raspberry Pi is a low cost, credit-card sized computer that plugs into a computer monitor or TV, and uses a standard keyboard and mouse. It is a capable little device that enables people of all ages to explore computing, and to learn how to program in languages like Scratch and Python. It’s capable of doing everything you’d expect a desktop computer to do, from browsing the internet and playing high-definition video, to making spreadsheets, word-processing, and playing games.

Introduction to IoT remote monitoring and notifications
Use case of IoT remote monitoring and notifications
Benefit of IoT remote monitoring and notifications
Create IOT Hub In Azure Portal
Create Devices in Azure Iot Hub
Azure Iot Hub Explorer
Raspberry Pi Simulator
Connect Device to Azure Iot Hub
Create Service Bus Namespace
Create Service Bus Queue
Add Custom Endpoint in Azure Iot Hub
Add Routes in Azure Iot Hub
Explore message with Service Bus Explorer
Create Logic App
Create Service Bus Connection in Logic App
Create Gmail Connection in Logic APP
Create Storage Account
Link Storage to Logic App

Weather Forecast from sensor data , Azure IOT Hub, Stream analytics and Machine Learning Studio

Balram Prasad — Fri, 18 Nov 2022 18:14:41 +0000

Weather Forecast from sensor data , Azure IOT Hub, Stream analytics and ML Studio
Machine learning is a technique of data science that helps computers learn from existing data to forecast future behaviors, outcomes, and trends. ML Studio (classic) is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. In this article, you learn how to use ML Studio (classic) to do weather forecasting (chance of rain) using the temperature and humidity data from your Azure IoT hub. The chance of rain is the output of a prepared weather prediction model. The model is built upon historic data to forecast chance of rain based on temperature and humidity.

Complete the Raspberry Pi online simulator tutorial or one of the device tutorials. For example, you can go to Raspberry Pi with Node.js or to one of the Send telemetry quickstarts. These articles cover the following requirements:
An active Azure subscription.
An Azure IoT hub under your subscription.
A client application that sends messages to your Azure IoT hub.
An ML Studio (classic) account.
An Azure Storage account, A General-purpose v2 account is preferred, but any Azure Storage account that supports Azure Blob storage will also work.

Introduction to Weather Forecasting using azure services
Use case of Weather Forecasting using azure services
Create IOT Hub In Azure Portal
Create Devices in Azure Iot Hub
Azure Iot Hub Explorer
Raspberry Pi Simulator
Connect Device to Azure Iot Hub
Create Azure Stream Analytics Job
Connect IOT to Azure Stream Analytics
Create Predictive Experiment in Azure Machine Learning Studio
Add Machine Learning web service as function in Azure Stream Analytics
Run Azure Stream Analytics Job

How to enable Azure Cosmos DB Data Discovery using Microsoft Purview

Balram Prasad — Sun, 13 Nov 2022 07:11:02 +0000

How to use PySpark and Spark SQL , MatPlotLib and Seaborn in Azure Synapse Analytics and Spark Pool

Balram Prasad — Sun, 13 Nov 2022 07:05:29 +0000

How to use PySpark and Spark SQL , MatPlotLib and Seaborn in Azure Synapse Analytics

PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core.

Spark SQL lets you query structured data inside Spark programs, using either SQL or a familiar DataFrame API. Usable in Java, Scala, Python and R.

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.

Create publication quality plots.
Make interactive figures that can zoom, pan, update.
Customize visual style and layout.
Export to many file formats.
Embed in JupyterLab and Graphical User Interfaces.
Use a rich array of third-party packages built on Matplotlib.

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated options—at scale. Azure Synapse brings these worlds together with a unified experience to ingest, explore, prepare, transform, manage, and serve data for immediate BI and machine learning needs.

What Is Web Scraping And how to do with azure Synapse and Python and Spark Pool

Balram Prasad — Sun, 13 Nov 2022 07:02:23 +0000

What is Web Scraping And how to do with azure Synapse and Python?

Web scraping is the process of gathering information from the Internet. Even copying and pasting the lyrics of your favorite song is a form of web scraping! However, the words “web scraping” usually refer to a process that involves automation.Automated web scraping can be a solution to speed up the data collection process. You write your code once, and it will get the information you want many times and from many pages.