#programmed database

[kindalikerackham]

tumblr wait!! your public boop-a-meter digits are too low!!! you're going to create an integer overflow boop vacuum!!!!!!!!

[nixcraft]

Learn database with anime style 🤣

source

[randomly-generated-posts-machine]

percy jackson gaslighting srinklo is so ooga chaka baby

[akaicodes]

we started learning about databases in class!! alsooo we have a huge project that just started for our 2nd semester & i’m so excited to begin, this time i’m in a group with my twinsister @niyacodes 🩷

[sensationally-senseless]

🌟 Help Me Out with a Quick Click! 🌟

Hey everyone! I’m currently participating in a super exciting challenge sponsored by Microsoft as part of my role as a Student Ambassador at my university. 🎓✨ This is an amazing opportunity for me, but I need a little bit of help from all of you to make it count!

Here’s the deal: I need to hit a target by getting clicks on a few links to earn points. It’s really simple—just click on the links below and then hit back. That’s all you need to do! 🖱️🔗

Why is this important? 🤔 If I don’t meet the target, I won’t be able to earn the university credits I need for this challenge. Your clicks can make a huge difference in helping me succeed!

How to Help:

Click on each of the links below.

Hit back to return to this post.

Repeat for each link!

Links to Click: 1. Azure 2. VSCode 3. DevBlogs 4. DotNet 5. Microsoft Developer 6. ImagineCup 7. Microsoft Learn 8. Microsoft Cloud 9. StartUps 10. MVP 11. Microsoft Tech Community

It’s a small favor that could mean a lot for my academic journey. If you can spare a moment, it would really mean the world to me! 💖🙏

Thank you so much for your support! 🚀💪

#UniversityChallenge #Microsoft #StudentAmbassador #HelpNeeded #QuickClick #Support #TechChallenge #UniversityLife #StudentSupport #Credits #ClickForPoints #ThankYou

[miniminuit]

today the phds are going in the time out corner

[devhubby]

How to Get Data from a Database in Laravel?

Read more at: https://elvanco.com/blog/how-to-get-data-from-a-database-in-laravel

[skelayton-lord]

@angelofthemornings Making this a proper reply-post because as a reply it might end up being too big.

The field is very vast, but the most common would be the person that runs the tests a physician asks you to and signs the paper to validate the results - we can be found doing your bloodwork, urine, biochem analysis (which all falls under clinical analysis/clinical pathology), and many other fields like radiology (x-rays, tomography, etc), acupuncture, embryology... here in Brazil we can have up to 29 licenses, each one corresponding to a different field. Some of these you can come out of university ready to work due to experience in your internship, but some others will require a specialization course or a master's - like acupuncture and radiology.

My license is in clinical pathology, but I've also spent a year in a specialization/expertise program (after getting my bachelor's degree) in laboratorial surveillance for diseases of public health interest, with a focus on diagnostic tools of immunology (serology tests) and molecular biology (mostly real time Polymerase Chair Reaction - PCR).

So I'm both licensed to work in any hospital or particular lab to run tests (with the objective of a diagnosis), and to work with the government in public health surveillance (where the objective here isn't a diagnosis, it is to confirm cases of a disease in a certain population and keep an eye out on how it behaves throughout the year, epidemiology).

I was about to decide between a Master's or a "direct" Doctorate (a Doctorate that "skips" a Master's and lasts longer than the usual Doctorate program) but then the pandemic came and many internship deals crashed, unless you were in virology, as that became the main and only focus of research at the time. I tried for a spot in the State's Strategic Lab, but it was interview-only and for only 1 candidate - I was placed 4th.

With that, my grandma's health also began to deteriorate, so I've been staying home since then to help care for her.

But 4 years later, I've started to job hunt again, and I'm now afraid I might be overqualified (and thus, "more expensive") to employers, as so far I havent gotten any return from my applications. I quite miss and wish to return to academia, sometimes, and with the State's lab, but I have a love-hate relationship with academia and it takes them forever to open up sign-ins for employees. Working in public health was my best time though, really, so if the opportunity arises, I'll be trying my chance there again - I did leave in good terms, and a lot o the chief researchers there wanted to work with me.

[neonhellscape]

Im not kidding, your magos biologis is the (catalyst) reason i am deciding to go on t and get top surgery

god im so with you on this one. good luck on your mission boss

[scvnthorpe]

That time I restored a Database view

Recently at work we've been migrating an old database system to a new platform to save money - this kind of shit is what makes your business processes faster, cheaper and more correct - and this has entailed sifting through a lot of tables, views, and views made of tables and views!

As it happens the finance guy who does all the payroll and expenses is a great guy to work with and basically the one person who knows all the relevant business rules, but also basically treats databases like they're excel workbooks. As such you have a bunch of bits stitched to each other and we're just figuring out how to first move everything and then ease into a well-oiled relational model with no duplication and all together on a single database.

While we in the dev team were figuring out how to do this for finance we were recently testing out a modified version of a view built on top of the old version and accidentally deleted the old version and not the modified testing version.

Mistakes are bound to happen, but we needed to figure out how to either restore it or at least figure out how to work without it because finance people love their data views and reports. There are probably clever things you can do with any DBMS to find shit you just dropped and restore it from backup, but I then realised that I'd been tasked with generating all the scripts for the database objects. There had to be a script laying around!

Sure enough I went to dig up the build script for the dropped view, and I ran it.

I queried it, and everything was back in place.

Shit goes wrong sometimes, but having the right failsafes can really make a difference.

Script your shit, use backups, use version control!

[appmasterd]

Top 5 Common Database Design patterns in Laravel

In the world of Laravel development, a well-structured database is the bedrock of a robust and scalable application. While Laravel's Eloquent ORM provides a powerful abstraction layer for interacting with your data, understanding common database design patterns can significantly enhance your development process. 

These patterns not only promote code organization and maintainability but also enable you to adapt your database structure to the unique needs of your application. By mastering these patterns, you can build efficient, reliable, and easily maintainable Laravel applications that can handle diverse data requirements.

1. Active Record Pattern:

This is the most common pattern used by Eloquent ORM in Laravel. It encapsulates database logic within model classes, allowing you to interact with the database using object-oriented methods.

Application

This pattern is well-suited for projects of any size and complexity. It simplifies database operations, making them easier to understand and maintain.

Example:

Advantages:

Simplicity: Easy to understand and implement.

Code Reusability: Model methods can be reused throughout your application.

Relationship Management: Built-in support for relationships between models.

Disadvantages:

Tight Coupling: Model logic is tightly coupled to the database, making it harder to test independently.

Complexity: Can become complex for large applications with complex data structures.

2. Data Mapper Pattern:

This pattern separates data access logic from domain logic. It uses a dedicated "mapper" class to translate between domain objects and database records.

Application

This pattern is useful for large-scale applications with complex domain models, as it allows for greater flexibility and modularity. It is particularly useful when working with multiple data sources or when you need to optimize for performance.

Example:

Advantages:

Flexibility: Easily change the database implementation without affecting business logic.

Testability: Easy to test independently from the database.

Modularity: Promotes a modular structure, separating concerns.

Disadvantages:

Increased Complexity: Requires more code and might be overkill for simple applications.

3. Repository Pattern:

This pattern provides an abstraction layer over the data access mechanism, offering a consistent interface for interacting with the database.

Application

This pattern promotes loose coupling and simplifies testing, as you can easily mock the repository and control the data returned. It is often used in conjunction with the Data Mapper pattern.

Example:

Advantages:

Loose Coupling: Decouples business logic from specific data access implementation.

Testability: Easy to mock repositories for testing.

Reusability: Reusable interface for accessing different data sources.

Disadvantages:

Initial Setup: Can require more setup compared to Active Record.

4. Table Inheritance Pattern:

This pattern allows you to create a hierarchical relationship between tables, where child tables inherit properties from a parent table.

Application

This pattern is useful for creating polymorphic relationships and managing data for different types of entities. For example, you could have a User table and separate tables for AdminUser and CustomerUser that inherit from the parent table.

Example:

Advantages:

Polymorphism: Enables handling different types of entities using a common interface.

Code Reusability: Reuses properties and methods from the parent table.

Data Organization: Provides a structured way to organize data for different types of users.

Disadvantages:

Increased Database Complexity: Can lead to a more complex database structure.

5. Schema-less Database Pattern:

This pattern avoids the use of a predefined schema and allows for dynamic data structures. This is commonly used with NoSQL databases like MongoDB.

Application

This pattern is suitable for projects that require highly flexible data structures, such as social media platforms or analytics systems.

Example:

Advantages:

Flexibility: Easily adapt to changing data structures.

Scalability: Suitable for high-volume, rapidly changing data.

High Performance: Efficient for specific use cases like real-time analytics.

Disadvantages:

Increased Complexity: Requires a different approach to querying and data manipulation.

Data Consistency: Can be challenging to maintain data consistency without a schema.

Choosing the Right Pattern:

The best pattern for your project depends on factors like project size, complexity, performance requirements, and your team's experience. It is important to choose patterns that align with the specific needs of your application and ensure long-term maintainability and scalability.

Conclusion:

This exploration of common database design patterns used in Laravel has shed light on the importance of strategic database structuring for building robust and scalable applications. From the simplicity of the Active Record pattern to the sophisticated capabilities of the Data Mapper and Repository patterns, each pattern offers distinct benefits that cater to specific project needs. 

By understanding the strengths and applications of these patterns, Laravel developers can choose the optimal approach for their projects, ensuring a well-organized, efficient, and maintainable database architecture. Ultimately, mastering these patterns empowers you to create Laravel applications that are not only functional but also adaptable to evolving data requirements and future growth.

[venomgender]

NOT THE HTML HELP MEEEE

[nixcraft]

[uthra-krish]

The Skills I Acquired on My Path to Becoming a Data Scientist

Data science has emerged as one of the most sought-after fields in recent years, and my journey into this exciting discipline has been nothing short of transformative. As someone with a deep curiosity for extracting insights from data, I was naturally drawn to the world of data science. In this blog post, I will share the skills I acquired on my path to becoming a data scientist, highlighting the importance of a diverse skill set in this field.

The Foundation — Mathematics and Statistics

At the core of data science lies a strong foundation in mathematics and statistics. Concepts such as probability, linear algebra, and statistical inference form the building blocks of data analysis and modeling. Understanding these principles is crucial for making informed decisions and drawing meaningful conclusions from data. Throughout my learning journey, I immersed myself in these mathematical concepts, applying them to real-world problems and honing my analytical skills.

Programming Proficiency

Proficiency in programming languages like Python or R is indispensable for a data scientist. These languages provide the tools and frameworks necessary for data manipulation, analysis, and modeling. I embarked on a journey to learn these languages, starting with the basics and gradually advancing to more complex concepts. Writing efficient and elegant code became second nature to me, enabling me to tackle large datasets and build sophisticated models.

Data Handling and Preprocessing

Working with real-world data is often messy and requires careful handling and preprocessing. This involves techniques such as data cleaning, transformation, and feature engineering. I gained valuable experience in navigating the intricacies of data preprocessing, learning how to deal with missing values, outliers, and inconsistent data formats. These skills allowed me to extract valuable insights from raw data and lay the groundwork for subsequent analysis.

Data Visualization and Communication

Data visualization plays a pivotal role in conveying insights to stakeholders and decision-makers. I realized the power of effective visualizations in telling compelling stories and making complex information accessible. I explored various tools and libraries, such as Matplotlib and Tableau, to create visually appealing and informative visualizations. Sharing these visualizations with others enhanced my ability to communicate data-driven insights effectively.

Machine Learning and Predictive Modeling

Machine learning is a cornerstone of data science, enabling us to build predictive models and make data-driven predictions. I delved into the realm of supervised and unsupervised learning, exploring algorithms such as linear regression, decision trees, and clustering techniques. Through hands-on projects, I gained practical experience in building models, fine-tuning their parameters, and evaluating their performance.

Database Management and SQL

Data science often involves working with large datasets stored in databases. Understanding database management and SQL (Structured Query Language) is essential for extracting valuable information from these repositories. I embarked on a journey to learn SQL, mastering the art of querying databases, joining tables, and aggregating data. These skills allowed me to harness the power of databases and efficiently retrieve the data required for analysis.

Domain Knowledge and Specialization

While technical skills are crucial, domain knowledge adds a unique dimension to data science projects. By specializing in specific industries or domains, data scientists can better understand the context and nuances of the problems they are solving. I explored various domains and acquired specialized knowledge, whether it be healthcare, finance, or marketing. This expertise complemented my technical skills, enabling me to provide insights that were not only data-driven but also tailored to the specific industry.

Soft Skills — Communication and Problem-Solving

In addition to technical skills, soft skills play a vital role in the success of a data scientist. Effective communication allows us to articulate complex ideas and findings to non-technical stakeholders, bridging the gap between data science and business. Problem-solving skills help us navigate challenges and find innovative solutions in a rapidly evolving field. Throughout my journey, I honed these skills, collaborating with teams, presenting findings, and adapting my approach to different audiences.

Continuous Learning and Adaptation

Data science is a field that is constantly evolving, with new tools, technologies, and trends emerging regularly. To stay at the forefront of this ever-changing landscape, continuous learning is essential. I dedicated myself to staying updated by following industry blogs, attending conferences, and participating in courses. This commitment to lifelong learning allowed me to adapt to new challenges, acquire new skills, and remain competitive in the field.

In conclusion, the journey to becoming a data scientist is an exciting and dynamic one, requiring a diverse set of skills. From mathematics and programming to data handling and communication, each skill plays a crucial role in unlocking the potential of data. Aspiring data scientists should embrace this multidimensional nature of the field and embark on their own learning journey. If you want to learn more about Data science, I highly recommend that you contact ACTE Technologies because they offer Data Science courses and job placement opportunities. Experienced teachers can help you learn better. You can find these services both online and offline. Take things step by step and consider enrolling in a course if you’re interested. By acquiring these skills and continuously adapting to new developments, they can make a meaningful impact in the world of data science.

[bagheerita]

Nicholas Rush, Stargate Universe: "Air"

[meteor-mp3]

me doing my homework: woooah im just like chat gpt