In the dynamic landscape of data processing, Apache Kafka stands out as a robust and scalable distributed event streaming platform. This blog post aims to demystify Kafka, guiding you through its installation process step by step, and unraveling the concepts of topics, producers, and consumers. Understanding Kafka: 1. What is Kafka? Apache Kafka is an open-source distributed streaming platform that excels in handling real-time data feeds. Originally developed by LinkedIn, Kafka has evolved into a powerful solution for building scalable and fault-tolerant data pipelines. Installing Kafka: 2. Step-by-Step Installation Guide: Let's dive into the installation process for Kafka: Prerequisites: Before installing Kafka, ensure you have Java installed on your machine, as Kafka is built on Java. Download Kafka: Visit the official Apache Kafka website (https://kafka.apache.org/) and download the latest stable release. Unzip the downloaded file to your preferred installation directory. Start Zookeeper: Kafka relies on Zookeeper for distributed coordination. Navigate to the Kafka installation directory and start Zookeeper: bin/zookeeper-server-start.sh config/zookeeper.properties Start Kafka Broker: Open a new terminal window and start the Kafka broker: bin/kafka-server-start.sh config/server.properties Congratulations! You now have Kafka up and running on your machine. Kafka Concepts: 3. Topics: Definition: In Kafka, a topic is a category or feed name to which messages are published by producers and from which messages are consumed by consumers. Creation: Create a topic using the following command: kafka-topics.bat --create --topic MyTopics --bootstrap-server localhost:9092 --partitions 3 --replication-factor 1 List out Topics : To see all the topics will use following command : \bin\windows\kafka-topics.bat --list --bootstrap-server localhost:9092 4. Producers and Consumers: Producers: Producers are responsible for publishing messages to Kafka topics. You can create a simple producer using the following command: bin/kafka-console-producer.sh --topic myTopic --bootstrap-server localhost:9092 Consumers: Consumers subscribe to Kafka topics and process the messages. Create a consumer with: bin/kafka-console-consumer.sh --topic myTopic --bootstrap-server localhost:9092 --from-beginning Conclusion: Apache Kafka is a game-changer in the world of real-time data processing. By following this step-by-step guide, you've successfully installed Kafka and gained insights into key concepts like topics, producers, and consumers. Stay tuned for more in-depth Kafka tutorials as you explore the vast possibilities of this powerful streaming platform.
Setting up replication in SQL Server can be a powerful way to ensure data consistency and availability across multiple servers. In this step-by-step guide, we'll walk through the process of configuring replication on SQL Servers. Step 1: Understand Replication Types Before diving into configuration, it's crucial to understand the types of replication available in SQL Server. Snapshot Replication: Takes a snapshot of the data at a specific point in time. Transactional Replication: Replicates changes in real-time as they occur. Merge Replication: Allows bidirectional data synchronization between servers. Choose the replication type that aligns with your specific needs and database architecture. Step 2: Prepare Your Environment Ensure that your SQL Server environment is ready for replication. This involves verifying that you have the necessary permissions and establishing proper connectivity between the SQL Server instances. Remember that replication involves three key components: Publisher, Distributor, and Subscribers. The Distributor can be on the same server as the Publisher or a separate server. Step 3: Configure Distributor If a Distributor isn't already set up, proceed to configure one. This involves specifying the server that will act as the Distributor and setting up distribution databases. Use either SQL Server Management Studio (SSMS) or T-SQL scripts for this configuration. Step 4: Enable Replication on the Publisher 1. Open SSMS and connect to the Publisher. 2. Right-click on the target database and choose "Tasks" > "Replication" > "Configure Distribution." 3. Follow the wizard, specifying the Distributor configured in Step 3. Step 5: Choose Articles Define the articles by selecting the tables, views, or stored procedures you want to replicate. This step allows you to fine-tune your replication by specifying data filters, choosing columns to replicate, and configuring additional options based on your specific requirements. Step 6: Configure Subscribers 1. Connect to the Subscribers in SSMS. 2. Right-click on the Replication folder and choose "Configure Distribution." 3. Follow the wizard, specifying the Distributor and configuring additional settings based on your chosen replication type. Step 7: Configure Subscription With the Distributor and Subscribers configured, it's time to set up subscriptions. 1. In SSMS, navigate to the Replication folder on the Publisher. 2. Right-click on the Local Publications and choose "New Subscriptions." 3. Follow the wizard to configure the subscription, specifying the Subscribers and defining any additional settings. Step 8: Monitor and Maintain Regular monitoring and maintenance are essential for a healthy replication environment. - Use the Replication Monitor in SSMS to view the status of publications, subscriptions, and any potential errors. - Implement routine maintenance tasks such as backing up and restoring the replication databases. Conclusion Configuring replication in SQL Server involves a series of well-defined steps. By understanding your replication needs, preparing your environment, and carefully configuring each component, you can establish a robust and reliable replication setup. Regular monitoring and maintenance ensure the ongoing efficiency and performance of your replication environment.
In this blog, I will guide you on the power of CI/CD in GitHub with a step-by-step guide. Learn to set up automated workflows, boost project efficiency, and streamline development processes for better code quality and faster deployments. Certainly! It seems that I've encountered a challenge in my current development workflow when deploying minor code changes. The existing process involves manually publishing code from Visual Studio, creating backups of the current code on the server, and then replacing it with the new code. To address this, it's advisable to transition to an automated solution utilizing a Continuous Integration/Continuous Deployment (CI/CD) pipeline. By implementing a CI/CD pipeline, you can streamline and automate the deployment process, making it more efficient and reducing the risk of manual errors. The CI/CD pipeline will handle tasks such as code compilation, testing, and deployment automatically, ensuring that the latest changes are seamlessly deployed to the desired environment. This transition will not only save time but also enhance the reliability of your deployment process, allowing your team to focus more on development and less on manual deployment tasks. For additional information, refer to the steps outlined below for guidance. Step 1: Go to your repository and click on the Actions tab Step 2: Now, Select the workflow according to your development. Here I am using .NET workflow. Step 3: Now you can see the default pipeline as below. In that, you can change your branch as per your requirement. Step 4: You can now incorporate three new sections as outlined below to build the code and publish the folder as an artifact. - name: Build and publish run: | dotnet restore dotnet build dotnet publish -o publish - name: Zip output run: | cd publish zip -r ../output . - name: Upload zip archive uses: actions/upload-artifact@v2 with: name: test path: ./publish Upon integrating this code, your YAML file will now appear as follows. In the code above, you have the flexibility to rename the zip file or the publish folder according to your preferences. Build and Publish : This step is responsible for building and publishing the code. Commands: dotnet restore: Restores the project's dependencies. dotnet build: Compiles the project. dotnet publish -o publish: Publishes the project output to the 'publish' folder. Zip Output : This step involves compressing the contents of the 'publish' folder into a zip file. Commands: cd publish: Changes the working directory to the 'publish' folder. zip -r ../output .: Creates a zip file named 'output' containing the contents of the 'publish' folder. Upload Zip Archive :This step uploads the zip archive to the workflow run as an artifact. Using: The actions/upload-artifact@v2 GitHub Action. Configuration: name: test: Specifies the name of the artifact as 'test'. path: ./publish: Indicates the path of the folder to be archived and uploaded. By using the given code, you receive a finalized published folder prepared for deployment on the server. However, the deployment process on the server requires manual intervention. To access the published folder, navigate to the "Actions" tab. Click on the "test" workflow, and you can download the published folder from there. Step 5: In the steps mentioned above, you previously followed a manual process, but now you have transitioned to an automatic process. To automate the process, you'll need to install a self-hosted runner on the virtual machine where your application is hosted. What is Self-hosted runner? self-hosted runner is a system that you deploy and manage to execute jobs from GitHub Actions on GitHub.com. To install the self-hosted runner, follow the basic steps. Under your repository name, click Settings. If you cannot see the "Settings" tab, select the dropdown menu, then click Settings. In the left sidebar, click Actions, then click Runners and then click on New self-hosted runner. Select the operating system image and architecture of your self-hosted runner machine. Open a shell on your self-hosted runner machine and run each shell command in the order shown. For more details you can visit https://docs.github.com/en/enterprise-cloud@latest/actions/hosting-your-own-runners/managing-self-hosted-runners/adding-self-hosted-runners Step 6: To automate the process, you can remove the last two sections, "Zip Output" and "Upload Zip Archive," and replace them with the following code. - name: Backup & Deploy run: | $datestamp = Get-Date -Format "yyyyMMddHHmmss" cd publish Remove-Item web.config Remove-Item appsettings.json Remove-Item appsettings.Development.json Stop-WebSite 'DemoGitHubPipeline' Compress-Archive D:\Published\DemoGitHubPipeline D:\Published\Backup\Backup_$datestamp.zip Copy-Item * D:\Published\DemoGitHubPipeline -Recurse -Force Start-WebSite 'DemoGitHubPipeline' Backup & Deploy : This step is responsible for creating a backup, making necessary modifications, and deploying the application. Commands: $datestamp = Get-Date -Format "yyyyMMddHHmmss": Retrieves the current date and time in the specified format. cd publish: Changes the working directory to the 'publish' folder. Remove-Item web.config: Deletes the 'web.config' file. Remove-Item appsettings.json: Deletes the 'appsettings.json' file. Remove-Item appsettings.Development.json: Deletes the 'appsettings.Development.json' file. Stop-WebSite 'DemoGitHubPipeline': Stops the website with the specified name. Compress-Archive D:\Published\DemoGitHubPipeline D:\Published\Backup\Backup_$datestamp.zip: Creates a compressed archive (zip) of the existing deployment with proper timestamp. Copy-Item * D:\Published\DemoGitHubPipeline -Recurse -Force: Copies all contents from the 'publish' folder to the deployment directory. Start-WebSite 'DemoGitHubPipeline': Restarts the website with the specified name. Note: Ensure that the paths and folder structures match the actual locations in your setup. Adjust the website name and paths based on your specific configuration. Conclusion: In summary, implementing a CI/CD pipeline in GitHub is a pivotal step towards achieving efficiency, reliability, and accelerated development cycles. The integration of CI/CD streamlines the software delivery process by automating testing, building, and deployment, leading to consistent and high-quality releases. GitHub Actions, with its native CI/CD capabilities, provides a powerful and flexible platform for orchestrating workflows. Leveraging its features, development teams can not only automate repetitive tasks but also ensure rapid feedback on code changes, enabling early detection of issues and facilitating collaboration.
In the ever-evolving landscape of business intelligence (BI), the need for seamless interaction with data is paramount. Imagine a world where you could effortlessly pose natural language questions to your datasets and receive insightful answers in return. Welcome to the future of BI, where the power of conversational interfaces meets the robust capabilities of Domo. This blog post serves as your comprehensive guide to implementing a BI ChatBot within the Domo platform, a revolutionary step towards making data exploration and analysis more intuitive and accessible than ever before. Gone are the days of wrestling with complex queries or navigating through intricate dashboards. With the BI ChatBot in Domo, users can now simply articulate their questions in plain language and navigate through datasets with unprecedented ease. Join us on this journey as we break down the process into manageable steps, allowing you to harness the full potential of BI ChatBot integration within the Domo ecosystem. Whether you're a seasoned data analyst or a business professional seeking data-driven insights, this guide will empower you to unlock the true value of your data through natural language interactions. Get ready to elevate your BI experience and transform the way you interact with your datasets. Let's dive into the future of business intelligence with the implementation of a BI ChatBot in Domo. Prerequisites: ChatGPT API Key: Prepare for the integration of natural language to SQL conversion by obtaining a ChatGPT API Key. This key will empower your system to seamlessly translate user queries in natural language into SQL commands. DOMO Access: Ensure that you have the necessary access rights to create a new application within the Domo platform. This step is crucial for configuring and deploying the BI ChatBot effectively within your Domo environment. 1: Integrate the HTML Easy Bricks App. Begin the process by incorporating the HTML Easy Bricks App into your project. Navigate to the AppStore and add the HTML Easy Bricks to your collection. Save it to your dashboard for easy access. Upon opening the App for the first time, it will have a default appearance. To enhance its visual appeal and functionality, customize it by incorporating the HTML and CSS code. This transformation will result in the refined look illustrated below. Image 1: DOMO HTML Easy Brick UI 2: Map/Connect the Dataset to the Card. In this phase, establish a connection between the dataset and the card where users will pose their inquiries. Refer to the image below, where the "Key" dataset is linked to "dataset0." Extend this mapping to accommodate up to three datasets. If your project involves more datasets, consider using the DDX-TEN-DATASETS App instead of HTML Easy Bricks for a more scalable solution. This ensures seamless integration and accessibility for users interacting with various datasets within your Domo environment. Image 2: Attach Dataset With Card 3: Execute the Query on the Dataset for Results. In this phase, you'll implement the code to execute a query on the dataset, fetching the desired results. Before this, initiate a call to the ChatGPT API to dynamically generate an SQL query based on the user's natural language question. It's essential to note that the below code is designed to only accept valid column names in the query, adhering strictly to MySQL syntax. To facilitate accurate query generation from ChatGPT, create a prompt that includes the dataset schema and provides clear guidance for obtaining precise SQL queries. Here is a call to the ChatGPT API to get SQL Query. VAR GPTKEY = 'key' VAR Prompt = 'Write effective prompt' $.ajax({ url: 'https://api.openai.com/v1/chat/completions', headers: { 'Authorization': 'Bearer ' + GPTKEY, 'Content-Type': 'application/json' }, method: 'POST', data: JSON.stringify({ model: 'gpt-3.5-turbo', messages: Prompt, max_tokens: 100, temperature: 0.5, top_p: 1.0, frequency_penalty: 0.0, presence_penalty: 0.0 }), success: function (response) { //Write code to store the Query into the variable } }); Refer to the code snippet below for executing the query on Domo and retrieving the results. var domo = window.domo; var datasets = window.datasets; domo.post('/sql/v1/'+ 'dataset0', SQLQuery, {contentType: 'text/plain'}).then(function(data) { //Write your Java or JQuery code to print data. }); The above code will accept the SQL queries generated by ChatGPT. It's important to highlight that, in the code, there is a hardcoded specification that every query will be applied to the dataset mapped as 'dataset0'. It's advisable to customize this part based on user selection. The code is designed to accept datasets with names such as 'dataset0', 'dataset1', and so forth. Ensure that any modifications align with the chosen dataset for optimal functionality, you can also use the domo.get method to get data for more information visit here. The outcome will be presented in JSON format, offering flexibility for further processing. You can seamlessly transfer this data to a table format and display or print it as needed. Conclusion Incorporating a BI ChatBot in Domo revolutionizes data interaction, seamlessly translating natural language queries into actionable insights. The guide's step-by-step approach simplifies integration, offering both analysts and business professionals an intuitive and accessible data exploration experience. As datasets effortlessly respond to user inquiries, this transformative synergy between ChatGPT and Domo reshapes how we extract value from data, heralding a future of conversational and insightful business intelligence. Dive into this dynamic integration to propel your decision-making processes into a new era of efficiency and accessibility.
In this blog, I will be sharing insights on how to effectively manage Conditional Authorization and Swagger Customization. Case 1 I'm currently working on a problem our QA team found while testing our website. Specifically, there's an issue with one of the features in the application that uses an API. In the QA environment, we need to allow access without authentication, but in the production environment, authentication is required. To fix this, I added a feature called Conditional Authorize Attribute with help of Environment Variable. This feature lets us control access to the API based on the environment. It allows anonymous access when necessary. In my situation, I've added a environment variable setting called "ASPNETCORE_ENVIRONMENT" to "QA" in the testing site's pipeline. Because of this, I can use the API on the QA server without requiring authentication. This method also helps specific authorization rules for the API based on the environment. Case 2 Additionally, I've added Swagger requests into a value object to meet specific requirements on swagger. By extending the Swashbuckle Swagger IOperationFilter, I integrated logic tailored to our needs. This approach allows us to customize requests in Swagger for all APIs directly. Furthermore, I've implemented a middleware designed to handle responses and here's how it works. In my case, there are three kinds of response class in my code that specify the response type (like ApiErrorResponse, ValidatorResponse, ResponseModel). According to the requirements, when we get a 200-status code with the correct response class model, I need to wrap the response object in a value format. I created a middleware for this. It figures out which endpoint we're dealing with through the HttpContext. Using that endpoint, I grab the metadata related to the ProducesResponseTypeAttribute class and check for a status code of OK (Metadata Extraction). If I manage to get the metadata with a status code of 200, I include that response in value format. Otherwise, I stick with the same model response. This helps you to modify the response as per needed outcome. These implementations provide a flexible solution for conditionally authorizing API access and wrapping request/response in an object according to specified requirements.
Hello, here I am going to explain what are the ways to publish a Power BI report on the Power BI Service which is the Cloud solution provided by Power BI and on the on-premise solution, keep reading this blog and, you will be able to publish the report on the Power BI Service as well as in on-premise. Power BI Service(Cloud Solution) Prerequisites: Power BI Service Account:- You must have a Power BI Service Account in order to publish the report to the service. Power BI Desktop:- Install the Power BI desktop on your system. Note:- Before publishing the report, login in power bi desktop with your Power BI service account. There are simply three types: Publish from Power BI Desktop. Publish from Power BI Server. Publish using Power BI Rest API. 1: Publish from Power BI Desktop Publish from the Power BI Desktop is the easiest way to publish a Power BI report, First, open the report that you want to publish in Power BI Desktop then click on the publish icon. Figure 1: Power BI Desktop When you click on it, it will ask you to select the workspace that you want to publish on. Then click on the select button, Bingo!! You successfully published the report. Figure 2: Power BI Desktop 2: Publish from Power BI Service This is a second method to publish a Power BI Report, For that first log into the Power BI service and, open the workspace where you want to publish the report. After, Click on the Upload icon then browse your report, That’s it for the second method. Figure 3: Power BI Service 3: Publish Using Power BI Rest API Here, We are using a PowerShell script that will work based on Power BI Rest API to publish the Power BI report. It is really simple to use Power BI Rest API with PowerShell script to deploy the report. To use Power BI API in PowerShell Script we need to install the MicrosoftPowerBIMgmt module. Install-Module -Name MicrosoftPowerBIMgmt Here are the steps to deploy the Power BI report using PowerShell Script. Step 1: Create a required parameter Before publishing the report we need the following things that we will assign as parameter PBI Username:- Assign the Username of the Power BI service Account. PBI Password:- Assign Password of Power BI service Account. WorkspaceName:- Assign workspace name. Report Name:- Assign Report name. Power BI File path:- Provide full Power BI File path where is located Workspace object:- In This variable, we assign the workspace id from the workspace name Step 2: Make the password secure and create a credential variable. $SecurePassword= ConvertTo-SecureString -String $PBIPassword -AsPlainText -Force $Creds = New-Object Management.Automation.PSCredential -ArgumentList ($PBIUserName, $SecurePassword) Step 3: Connect to Power BI Service Connect-PowerBIServiceAccount -Credential $Creds Step 4: Publish the Report This is the final and very important step in this step we will publish the report. New-PowerBIReport -Path $pbixPath -Name $ReportName -Workspace $workspaceObject -ConflictAction CreateOrOverwrite On-Premise Solution Prerequisites: Power BI report server:- You must have a Power BI report server that is installed in your system with fully configured and, it must be set up with a Database, web URL, and web portal. Power BI desktop report server:- Install the Power BI desktop report server in your system. Note:- This is different than normal Power BI desktop There are simply two types: Using Power BI Desktop. Using Power BI Report Server. 1: Using Power BI Desktop. Open the report that you want to deploy then Open the “File” navigation option(situated in the upper-left corner) and, click on “save as”. Figure 4- Power BI Desktop File panel Note:- When you click on “Power BI Report Server” you will get the below message where you have to give the web portal URL URL from the step of configuring the report server. Figure 5:- Adding Report Server Address(Source:- Radacad) After successful deployment, you will see a message with a link to the report. Figure 6:- Successfully published report(Source: Radacad). 2: Using Power BI Report Server. This is a different way to deploy the report so, first you have to open Power BI Report Server. Figure 7:- Power BI Report Server(Source: Radacad) Click on “Upload” as mentioned in the above picture and upload Power BI Report from your files Conclusion I Hope I’ve Shown You How Easy It Is To deploy Power BI reports on the Report server as well as on-premise, So far we have learned the step-by-step process of it.
Here, I will explain how to restrict users from using expired tokens in a .NET Core application. Token expiration checks are crucial for ensuring the security of your application. Here's a general outline of how you can achieve this: 1. Configure Token Expiration: When generating a token, such as a JWT, set an expiration time for the token. This is typically done during token creation. For example, when using JWTs, you can specify the expiration claim: var tokenDescriptor = new SecurityTokenDescriptor { Expires = DateTime.Now.AddMinutes(30) // Set expiration time }; 2. Token Validation Middleware: Create middleware in your application to validate the token on each request. This middleware should verify the token's expiration time. You can configure this middleware in the startup or program file on the .NET side. public void Configure(IApplicationBuilder app, IHostingEnvironment env) { app.UseMiddleware<TokenExpirationMiddleware>(); } 3. Token Expiration Middleware: Develop middleware to validate the token's expiration time. Take note of the following points: ValidateIssuerSigningKey: Set to true, indicating that the system should validate the issuer signing key. IssuerSigningKey: The byte array represents the secret key used for both signing and verifying the JWT token. ValidateIssuer and ValidateAudience: Set to false, indicating that validation of the issuer and audience is skipped. By setting ClockSkew to TimeSpan.Zero, you specify no tolerance for clock differences. If the current time on the server or client is not precisely within the token's validity period, the token is considered expired. public class TokenExpirationMiddleware { private readonly RequestDelegate _next; public TokenExpirationMiddleware(RequestDelegate next) { _next = next; } public async Task Invoke(HttpContext context) { // Check if the request has a valid token var token = context.Request.Headers["Authorization"].FirstOrDefault()?.Split(" ").Last(); if (token != null) { var tokenHandler = new JwtSecurityTokenHandler(); var key = Encoding.ASCII.GetBytes("YourSecretKey"); // Replace with your actual secret key of Issuer var tokenValidationParameters = new TokenValidationParameters { ValidateIssuerSigningKey = true, IssuerSigningKey = new SymmetricSecurityKey(key), ValidateIssuer = false, ValidateAudience = false, ClockSkew = TimeSpan.Zero }; try { // Validate the token var principal = tokenHandler.ValidateToken(token, tokenValidationParameters, out var securityToken); // Check if the token is expired if (securityToken is JwtSecurityToken jwtSecurityToken) { if (jwtSecurityToken.ValidTo < DateTime.Now) { // Token is expired context.Response.StatusCode = (int)HttpStatusCode.Unauthorized; return; } } } catch (SecurityTokenException) { // Token validation failed context.Response.StatusCode = (int)HttpStatusCode.Unauthorized; return; } } await _next(context); } } Working fine with proper token time. Here is an example: I am providing an expired token, and it will result in a 401 Unauthorized status. You can also check the token in https://jwt.io/ for time expired (exp) . By following these steps, you can effectively implement checks to ensure that users are not able to use expired tokens within your .NET Core application.
In this blog, we are going to spread some awareness of how AI tools are replacing software engineers. Using these tools, we make our tasks much easier and smoother, but we don’t know how we are losing our dependency on the IT industry. Overview of AI Tools - AI tools, or artificial intelligence tools, offer numerous advantages across various domains. They streamline processes, enhance efficiency, and contribute to innovation. Perks include automation of repetitive tasks, data analysis for insights, improved decision-making, and the ability to handle complex computations at scale. AI tools also have applications in healthcare, finance, customer service, and more, making them invaluable for addressing challenges and driving progress. Cons. of AI Tools - While AI tools offer significant benefits, they also come with potential drawbacks. Concerns include less confidence for decision making, security for our code and other data, and Early replacement of your job. Ethics and morality are important human features that can be difficult to incorporate into an AI. The rapid progress of AI has raised several concerns that one day, AI will grow uncontrollably. And eventually, wipe out humanity. This moment is referred to as the AI singularity. The equivalent of 300 million full-time jobs could be lost to automation according to an April 2023 Report from Goldman Sachs Research. The author also estimates “that roughly two-thirds of U.S. occupations are exposed to some degree of automation by AI.” The story is complicated and tough. Economists and researchers have said many jobs will be eliminated by AI soon. These AI Tools are directly harmful to the economy of any country that is populated by the IT industry, for example, India’s IT industry has $255 billion of contribution to GDP and It could decrease now because of AI Tools and the recession that employees have to face. And it can make a country’s growth slower than other countries. Drawbacks of AI Tools Lack of creativeness. Privacy. Ethical Concerns. Lack of interpersonal skills. Unemployment. Job displacement. Increasing human laziness. Solution/Awareness to reduce usage of AI Tools Let’s search for one scenario in the AI tool and also search for that scenario in other websites which are not consist of AI Tools. Here I am using one AI Tool to develop one HTML Form with validation and design. I asked for it and it gave me the whole code which consists of all my requirements which I needed. It also provides design and script in one result. In short, it just has 2 steps copy the code and execute it, It fulfills all the requirements and sometimes it also increases some more features which shows a design that how you need and it can also give you these codes in separate files as per their usage. Here I analyze the requirements and research according to the requirements and I found these results which have all the things step-by-step. Firstly, it will explain the initial step for the HTML Form. Then it will explain the usage of all the tags that are going to be used in this process. This Image shows that how to implement the checkbox and drop-down to the HTML Form. This is the final Image, and this image shows the final result of the form with all the required things. In short, AI Tools are harmful for humans and to replace AI Tools, it just requires some observation and analysis on the particular task/project. I hope you find this blog informative and interesting so please get connected to us for more blogs like this.
Achievers Night, a highly anticipated annual event, took place recently, leaving attendees in awe with its grandeur and celebration of outstanding accomplishments. This blog aims to provide an in-depth review of this remarkable evening, highlighting the captivating performances, inspirational speeches, and overall atmosphere that made it an unforgettable experience. Inspiring Keynote Speeches: The event featured notable achievers from various fields who delivered thought-provoking and inspiring keynote speeches. These accomplished individuals shared their personal journeys, highlighting the challenges they faced and the perseverance that led to their success. Their words of wisdom and valuable insights resonated with the audience, igniting a sense of motivation and determination to pursue their own dreams and goals. Recognition of Excellence: Achievers Night served as a platform to acknowledge and honor exceptional individuals who have made significant contributions in their respective domains. The event presented awards in various categories to recognize the remarkable achievements of these deserving individuals. The atmosphere was filled with excitement and admiration as the achievers took the stage, sharing their gratitude, and inspiring others with their stories. Unforgettable Performances: The stage was set ablaze with mesmerizing performances by talented individuals, showcasing their skills and entertaining the audience throughout the night. From amazing and funny dance performances to a hilarious stand-up comedy, each act was meticulously curated. The performers demonstrated their unwavering passion, leaving the audience in awe of their incredible talents. Elegance and Ambiance: Achievers Night was not just about recognizing accomplishments; it was an enchanting experience from start to finish. The venue was adorned with elegant decor, creating a sophisticated and celebratory ambiance. From the beautifully arranged seating to the impeccable lighting and sound systems, every detail was meticulously planned to ensure a memorable evening for all attendees. Conclusion: Achievers Night surpassed all expectations, providing an enchanting and inspiring experience for everyone involved. From outstanding performances to insightful speeches and the recognition of excellence, the event celebrated the achievements of extraordinary individuals and ignited a spark of ambition within each attendee. It was an evening that left a lasting impression, reminding us that with passion, perseverance, and dedication, we can all become achievers in our own right.
