Author: admin yep

When developing web applications with Laravel, a common task is to store form data in a database. In this tutorial, we will guide you through the process of capturing user input from a Laravel form and storing it in a database using Laravel’s powerful ORM, Eloquent.

Step 1: Setting Up the Database

Before diving into form submission, ensure that you have a database connection configured correctly in your Laravel application. Open the .env file and verify the database credentials (e.g., DB_CONNECTION, DB_HOST, DB_PORT, DB_DATABASE, DB_USERNAME, DB_PASSWORD). If needed, make any necessary adjustments to match your database configuration. The.env file should look similar to the one below.

APP_NAME=Laravel
APP_ENV=local
APP_KEY=base64:PZNdti8R6gIx0XGfXZpUA9gX4uiHyboi+DrozytCEwY=
APP_DEBUG=true
APP_URL=http://your-domain.test

LOG_CHANNEL=stack
LOG_DEPRECATIONS_CHANNEL=null
LOG_LEVEL=debug

DB_CONNECTION=mysql
DB_HOST=127.0.0.1
DB_PORT=3306
DB_DATABASE=your-database
DB_USERNAME=root
DB_PASSWORD=mrsid

Step 2: Creating a Migration

We will now create a migration that will contain the definition of the structure of our database table. Run the following command to instruct Laravel to generate it:

php artisan make:migration create_user_comments_table

Laravel has now added a basic migration file to the folder database\migrations, which we can edit to suit our requirements.

Step 3: Editing the Migration

Now open the newly generated migration file and define the table schema within the up method. Here you can specify the columns you need to store your form data. In our example, we’ll make a form with fields like name, email, and message, so our migration file might look like this:

<?php

use Illuminate\Database\Migrations\Migration;
use Illuminate\Database\Schema\Blueprint;
use Illuminate\Support\Facades\Schema;

return new class extends Migration
{
    /**
     * Run the migrations.
     */
    public function up(): void
    {
        Schema::create('user_comments', function (Blueprint $table) {
            $table->id();
            $table->string('name');
            $table->string('email');
            $table->text('message');
            $table->timestamps();
        });
    }

    /**
     * Reverse the migrations.
     */
    public function down(): void
    {
        Schema::dropIfExists('user_comments');
    }
};

Step 4: Running the Migration

To create the table in the database, run the migration using the following command:

This command will execute all pending migrations in order of creation date. In the example shown you can see Laravel first runs its 4 default migrations and then runs our newly added migration which creates the table user_comments:

Step 5: Creating a Model

Next, run the following command to create a model UserComment that represents the table you just created:

php artisan make:model UserComment

Step 6: Creating Form in Blade

Now create a blade file in your /resources/views folder and copy and paste the code below to add the HTML code of the form.

<form action="" method="post">
    @csrf
    <table>
        <tr>
            <td>Name</td>
            <td><input type="text" name="name" value=""></td>
        </tr>
        <tr>
            <td>Email</td>
            <td><input type="text" name="email" value=""></td>
        </tr>
        <tr>
            <td>Message</td>
            <td><textarea name="message"></textarea></td>
        </tr>
        <tr>
            <td></td>
            <td>
                <input type="submit" />
            </td>
        </tr>
    </table>
</form>

Note that we used a named route: “comment.store”, we’ll define this later in our routes/web.php

Step 7: Creating a Controller

Next, run the following artisan command to make a controller named CommentController:

php artisan make:controller CommentController

Step 8: Creating the Routes

Now we will add 2 routes. We will name the first route “comment.index”, this will display our page containing the form. The other route will be named “comment.store”, this will receive the form and store it in the database.

Open your web.php file and add the following code:

Route::get('/comment', [App\Http\Controllers\CommentController::class, 'index'])->name('comment.index');
Route::post('/comment', [App\Http\Controllers\CommentController::class, 'store'])->name('comment.store');

This will indicate that Laravel should:

• expect a GET to URL “/comment" and needs to call the function “index” in CommentController
• expect a POST to URL “/comment" and needs to call the function “store” in CommentController

Step 9: Showing the Form

Now edit the CommentController and add a function called index by pasting the code below:

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;

class CommentController extends Controller
{
    public function index()
    {
        return view('comment');
    }
}

You should now be able to see the comment form when you open your browser:

Step 10: Processing Form Data

We will now make changes to our CommentController by adding a store() function to handle the (post) form submission defined in our web.php route file. Within this function, we will create a new instance of the UserComment model and assign the input values from the form to the respective model attributes: name, email, and message.

Note that we have also added some validation rules which Laravel will check. This way we can ensure that the entered form fields contain the expected data.

<?php

namespace App\Http\Controllers;

use App\Models\UserComment;
use Illuminate\Http\Request;

class CommentController extends Controller
{
    public function index()
    {
        return view('comment');
    }

    public function store(Request $request)
    {
        $request->validate([
            'name' => 'required|max:32',
            'email' => 'required|email',
            'message' => 'required|string|required|max:255'
        ]);
        $userComment = new UserComment();
        $userComment->name = $request->input('name');
        $userComment->email = $request->input('email');
        $userComment->message = $request->input('message');
        $userComment->save();

        // Additional logic or redirection after successful data storage

        return redirect()->back()->with('success', 'Comment stored successfully!');
    }
}

Step 11: Displaying Feedback to the User

To ensure proper user feedback in cases of incomplete or invalid input, as well as after successfully storing the data, it’s important to provide informative messages.

When redirecting back to the form page, we can use the ->with() function to set a success message. Additionally, if there are any validation errors, Laravel will populate an $errors variable with the corresponding error messages.

To ensure that both the success message and validation errors are displayed when applicable, we need to include the following code snippet just above the comment form in your view file:

@if(session()->has('success'))
    <p>
        
    </p>
@endif

@if ($errors->any())
    <ul>
        @foreach ($errors->all() as $error)
            <li></li>
        @endforeach
    </ul>
@endif

Now, when attempting to save the form with missing or invalid inputs, we will encounter validation errors:

However, if we fill in valid values and resubmit the form, the input will be successfully saved:

After saving the form, the browser will automatically redirect back to the form page and display a success message:

Now, if you view the database you can see the data has been stored correctly. You might use a tool like phpMyAdmin, or my personal favorite the free MySQL tool: HeidiSQL which will show:

Further reading

We can further improve and shorten our Controller code by using the following steps:

1. Moving the validation rules into a Form Request Class
2. Enable Mass Assign of values on the properties in the Model UserComment (name, email, message)
3. Call Eloquent create method and mass assign the values of all validated fields.

public function store(UserCommentStoreRequest $request)
{
    UserComment::create($request->validated());
    return redirect()->back()->with('success', 'Comment stored successfully!');
}

To learn exactly how to apply Mass Assignment and a Form Request to achieve this read our detailed guide: Why Use Laravel Form Request and How? (BIG Code improvement)

Conclusion

In this tutorial, we explored the step-by-step process of storing form data in a Laravel database using Laravel’s ORM, Eloquent. We covered essential steps such as setting up the database connection, creating a migration to define the table structure, running the migration to create the table, creating a model to represent the table, creating a form in Blade, creating a controller to handle form submission, defining routes for the form, processing the form data, and providing user feedback in the form of success messages or validation error messages.

By following these steps, you now have a solid understanding of how to capture user input from a Laravel form and store it in a database efficiently and securely.

Whether you’re conducting market research, building your portfolio as an analyst, or seeking insights to expand your market reach, valuable and reliable data is essential for informed decision-making.

However, searching the internet for free, reliable, and accessible data has some challenges. This article will make your data-hunting quest less challenging by introducing you to some of the top websites to access free data.

Google Trends is a free service developed by Google that provides users with unfiltered data samples of search requests made to Google. While this service displays time series data from 2004 to the present at both global and city-level scales, it doesn’t show the personal details of the search engine users.

You can also restrict the data to focus on categories, languages, entities, or trending searches on Google with Google Trends. Examples of available data include daily search trends and real-time search trends, which show data for the past seven days.

FiveThirtyEight is a data journalism website that has data about poll analysis, sports, pop culture, politics, science, and economic happenings.

The great thing about the website is that you can download the data from their website or their official GitHub repository and use your data visualization tools to create captivating data journalism visuals for your audience. A few examples of interesting data available include the world cup predictions and 2022-23 NHL predictions data.

BuzzFeed News is an American breaking news and original reporting platform that reports everything from journalism, tech, entertainment, celebrity news, culture, and DIY hacks to health and politics.

On its GitHub, BuzzFeed News makes its dataset, tools, and analysis from BuzzFeed’s newsroom open-source, accessible, and available. An example includes the FBI NICS firearm background check data.

Data.gov is the United States government’s open data website that hosts over 250,000 publicly available, well-documented datasets from international and multiple federal government agencies. The idea behind this initiative was to provide an open and transparent government.

You can access data from the website based on topic and agency or organization. Some examples of data you can find on Data.gov is the national student loan data system and electric vehicle population data.

Kaggle is a public data playground acquired by Google that offers a wide range of datasets on various topics. This community platform allows you to share your codes, learn, collaborate with fellow data professionals, and skill up. Kaggle also hosts data science competitions where you can win various prizes.

This guide provides a beginner’s guide on how to get started with Kaggle for data science. An example is the Global YouTube Statistics 2023.

EarthData is a data initiative by NASA serving as a repository of earth data from 1994 to now. You can get data related from the remote satellite information to data about the Earth’s atmosphere, ocean, and Terrestrial Hydrosphere.

You can browse various topics and access data like extreme heat data. However, you will have to explore NASA’s planetary data system for non-earth data.

IMDb provides data about movies, TV series, home videos, podcasts, video games, streaming information, and celebrity content. An example is IMDb non-commercial datasets.

AWS Public Dataset is a website that hosts over 3000 data sets of datasets made publicly available through AWS services. Most of the datasets here are project-based. A few include the cancer genome atlas and Foldingathome COVID-19 Datasets.

Inside Airbnb is a watchdog website launched by Murray Cox. This website sources data publicly available from Airbnb, a platform that offers users budget-friendly rooms worldwide. You can use information from this site to carry out analytics like the rental analytics of Montreal.

Google Dataset Search is a dataset search engine created by Google that hosts over 20 million datasets. Like their search engine, you can get data from almost anything. A good example is the Canadian national long-term water quality monitoring data.

UC Irvine Machine Learning Repository is the home of 624 datasets for the machine learning community in the world. This website has a strong reputation in the community because the datasets are categorized based on the machine learning tasks they are suited for. An example is the Iris dataset, a famous classification and clustering model dataset.

Datahub as a platform has many datasets that cover a wide range of topics like the 10-year US Government Bond Yields (long-term interest rate). Besides the data, they also display data tools and toolkits that can come in for data professionals.

This is the first website on our list for exclusive health data. The Global Health Observatory serves as a data repository displaying health-related statistics for over 1000 indicators for the WHO’s 194 member states. The data are recorded to monitor these member states’ progress toward SDG goals. You can get data by filtering the theme, category, metadata, and indicator of the data.

This platform is really niche based. It shows research data and market intelligence information like the weekend box office figures and related data on the UK film industry.

GitHub is more than just the home of millions of collaborative and open-source projects. The platform also hosts many repositories that aim to hold free, public, and open-source datasets. Even BuzzFeedNews has an open-source GitHub repository.

Other examples are the Awesome Public Datasets repository and do you even lift the dataset. You can also contribute to these open-source projects on GitHub.

Data.world is a data community and collaborative platform that hosts data projects and datasets. While a few datasets are paid, majorly of the data on the platform, like Makeover Monday’s 2021/W16: monthly air passengers in America, are free and can be easily downloaded locally or accessed via their API.

World Bank Open Data is a catalog of global economic and development data. You can browse and filter the data, like the global statistics on the cost and affordability of healthy diets by indicator and country.

Nasdaq Data Link is for everything financial, economic, and alternative data. You can access data like the US federal reserve data releases via a spreadsheet like Excel or an API.

NYC Taxi and Limousine Commission data platform records and hosts information such as yellow and green taxi trip records across New York City. The great thing about this website is that it shows information about everything, from the pick-up/drop-off to the taxicab zone and trip fares.

Academic Torrents is a data catalog of over 127.15 TB of research data. It was built, as they say, for researchers and by researchers.

Explore and Learn

Hopefully, with this list, you can get data that can shape your business landscape, drive your market research, gain a competitive edge, and help you build that unique data portfolio free of charge. So embrace the opportunities, explore, and have a less challenging data-hunting quest.

Right on cue, Meta has shared its latest AI drop with the world, and this time, the company is letting anybody get their hands on a bot that will write, debug, and describe code in a multitude of coding languages.

As reports last week first hinted, Meta’s Code Llama is a full-on code-generating AI that uses natural language to create and describe code in multiple coding languages. Like most of the AI products Meta’s released as of late, the model is open source and is free to use both personally and commercially.

Meta tried to imply it wouldn’t completely replace programmers, instead calling it a tool to “make workflows faster” and “lower the barrier to entry for people who are learning to code.” The program can both create and debug code, it also can comprehend and provide text explanations for questions regarding different programming languages.

It supports languages including C++, Java, PHP, Typescript, Bash, and C#. There is also a specialized version of the model called Code Llama – Python, which is custom-designed for programming in what’s become one of the most commonplace coding languages.

There’s also The Code Llama – Instruct model which is better at comprehending natural language instructions. Meta said those looking to generate code should use the Instruct model since it’s “fine-tuned to generate helpful and safe answers in natural language.” That emphasis on safety is interesting, as previous coding bots have had mixed results creating workable code, and that’s not even mentioning researchers who have proved other bots like ChatGPT and Bard have been manipulated to create malicious code. Meta does have an acceptable use policy for its AI about generating malicious code, malware, or computer viruses.

As for the dangers of using the AI to produce harmful content, Meta said it red-teamed the program in an attempt to force it to produce malicious code and found “Code Llama answered with safer responses” compared to ChatGPT running on GPT-3.5 Turbo.

The model is based on the framework of Meta’s Llama 2 language model. The company said it further trained the LLM on “code-specific datasets.” According to Meta’s blog post, the model accepts both code and language prompts. Users are also able to tell the model more about its existing codebase, which should turn out more personalized responses.

Code Llama’s three different versions of the AI scaled with more parameters, with 7 billion, 13 billion, and 34 billion parameter versions available. Parameters are usually a marker for an AI’s overall capabilities of producing accurate results. The smaller the model, the more easily it can run on single GPUs. Meta also mentioned that the smaller models are faster and may be better for “real-time code completion.”

According to the blog post, the Code Llama 34B parameter version scored similarly to OpenAI’s GPT-3.5 on several tests like HumanEval that evaluate the capabilities of LLMs. The AI was far below what GPT-4 could do on HumanEval, but it did better than some other open source coding-centric models like Palm-Coder and StarCoder.

The model is akin to the Microsoft-owned GitHub Copilot and Amazon’s CodeWhisperer, though Copilot costs money after a 30-day trial and CodeWhisperer is only free for individual use. These kinds of models are reportedly popular among programmers, with Microsoft claiming that 92% of programmers at large companies are using AI to some extent.

It’s not all gravy. Meta is specifically stopping short of saying what’s in Llama 2’s training data, and for good reason. Some developers have already sued Microsoft and GitHub alleging the company trained the AI on their code, ignoring their licenses.

A wealth of first-person shooters from the period’s golden era have seen remasters lately. Now comes one of the true greats: Star Wars: Dark Forces Remaster.

Nightdive Studios, which has been showing everybody how to do justice to classic shooter upgrades recently with its remasters of Quake II and System Shock, is using that same KEX Engine to give just enough modernization, but not too much, to the LucasArts title that was even better than its Doom-but-it’s-Star-Wars promise.

In the notes and footage of its reveal trailer, Nightdive promises 4K/120 fps gameplay, modern gamepad support, trophies and achievements, remastered cutscenes, and, of course, blasting Storm Troopers that have markedly better aim on a monitor than they do on film. The remaster is “coming soon” to PS4/5, Xbox One/X/S, Nintendo Switch, and Steam on PC, with “a release date announcement later this year.”

When LucasArts shut down in 2013, following Disney’s purchase of George Lucas’ empire, Lee Hutchinson offered his remembrance of Dark Forces:

Dark Forces was a fine shooter in its own right and looked amazing, but that Star Wars license rocketed its appeal right up into outer space. Dark Forces promised something irresistible to any geek: the ability to jump into the Star Wars universe and run around. Released in 1995, the game was LucasArts’ first foray into the nascent FPS genre. The company set the bar awfully high.

As Hutchinson noted, and which fans likely remember, there were only hints of Jedi-dom in Dark Forces; you never got your hands on a lightsaber, and you never force-pushed anyone off a ledge. The later Jedi Knight games fixed that. Dark Forces also faced the same memory and texture-resolution challenges as other shooters of its time, but it had the advantage of its setting. Imperial ships and bases had always looked stark, drab, and oftentimes quite empty in the Star Wars films (also due to certain constraints). So when a TIE Fighter hangar challenges you with only a handful of goons in a sterile space that looks like it could hold 300, that’s not a flaw; that’s George Lucas’ budget-minded used-future aesthetic!

Larry Kuperman of Nightdive told IGN that the game should still feel like the original felt, and that means difficult. The title should be “popularly priced,” Kuperman said, which indicates something well below the typical AAA $60/$70 mark.

We’ll keep an eye out for the first signs of a release date on this one. And we’ll bide our time until Jedi Knight II: Jedi Outcast makes it into the industry’s remaster/revenue queue.

Setting up an environment to practice SQL can be challenging for beginners. Even experienced programmers may want to run queries without setting up a database.

SQL online platforms are the best choice for this. It provides a development environment to run, manipulate, and test SQL queries. These free and interactive platforms provide development environments like that of a database. Here are the best online SQL playgrounds to run and test your SQL queries.

SQL Fiddle is one of the best choices for practicing SQL queries. It has a user-friendly interface that makes it easier to run SQL queries and commands. The interface has panels that separate the workspace and the output. It’s best for running short queries.

First, you must build a schema for the internal database by clicking the Build Schema button on the left panel. Then, write and run your queries on the right panel.

You will see the output at the bottom of the left panel. If there are errors in your code, SQL Fiddle notifies you to edit the code and rerun it.

You can also expand the screens to a preferred size and use the query terminators provided. At the bottom of the screen, you can view the structure of the database schema.

You can run queries for various SQL databases, including Oracle, PostgreSQL, MySQL, and SQLite. You can pick a database by selecting it from the drop-down menu on the navigation bar.

You can use DB Fiddle to test your SQL queries. The playground provides SQLite, MySQL, and PostgreSQL databases to work with.

The interface is simple and easy to use. They have sample queries to show you how to use their workspace. The panels separate the working environments and a navigation bar.

You can create your own schemas and SQL database tables. Then, run the queries with the click of a button. The panel at the bottom of the page displays your results.

You can collaborate with others on the platform in real-time. You can also change your code into Markdown format and fork code from other repositories.

DB Fiddle is free, but you can pay for the pro version to access advanced features like SQL assessments.

You can access the interface without having to sign-up. But you must create an account if you need to save your work. You can convert your workspace into private mode if you want to keep your work private.

DB Fiddle UK provides a simple and easy-to-use interface to run your queries. They support 10+ relational databases, including MySQL, PostgreSQL, and MariaDB. You are free to choose the version of the DB engine you want to work with.

You can quickly create a workspace by clicking the add batch button on the left of the page (with a plus sign on it). Then, you can run as many queries as you want. DB Fiddle UK allows hiding private data or leaving it public.

You can change your data into Markdown format on the interface. Also, you can highlight important parts of your code using their highlighting tool.

You don’t need to sign-up for the platform to interact with it; you can start working on it immediately.

SQLite Online provides a productive workspace for you to run SQL queries. You can work with three databases, namely MariaDB, PostgreSQL, and MySQL database engines. You can quickly write and run queries on the interface.

To work with a specific database, click on the database name provided on the left pane. SQLite Online will then open that workspace for you. If you have private data that you don’t wish to share with the public, you must sign up and save your work on the platform.

You can connect to your DB remotely and run queries in the workspace. You also have the option to connect to other online DBs.

SQLite Online allows you to import data sets to work with and equally export them as CSV, XML, JSON, or SQL schema format.

Paiza provides a dynamic playground to run and test MySQL queries. It supports over 20 programming languages, including PHP, Rust, Python, and Elixir. For beginners, this is a great platform to learn MySQL concepts.

Pick the language you want to run your queries, and the website will provide the workspace for it. The MySQL section provides a database engine to create tables, insert and select data.

You can use the workspace without signing up. But if you need a work record, register and create an account on the platform. You can import code from GitHub and run it on Paiza.

Also, you can collaborate on projects with your team on the platform. You can keep your workspace private or public for others to access. Paiza also provides a cloud platform to create and host your applications.

Programiz is a great platform to learn SQL interactively. The website provides everything you need to learn and practice SQL queries. As a beginner, you will learn from SQL basics to more advanced concepts while practicing on the interactive editor.

You don’t require prior knowledge; you can start learning from scratch. You can use the editor to create tables, insert new data, delete, and run other SQL operations.

Programiz tests your knowledge with sample data sets you can play with on the code editor. As a beginner, you can query the sample data code editor as you learn SQL.

The site has a comprehensive SQL course for which you can sign up and learn detailed SQL concepts. This site provides the guidance you need to begin your career as a database engineer.

How to Use Online SQL Playgrounds

Online SQL playgrounds are great platforms for learning and practicing SQL. These playgrounds might not fully replicate the complexity of real-world scenarios. But they give you an idea of how SQL works.

But you should be careful about the data you share on the platform. Don’t share any sensitive information that may be harmful if they get into the wrong hands. You should also set up a local instance and learn SQL concepts like string functions.