#Google Indexing API with Python

[marsdevs]

How to Index Webpages & Backlinks in 5 Minutes by Google Indexing API in Hindi - (Free & Very Easy)

Get Your Webpages and Backlinks Indexed Immediately by Using Google Indexing API In this video, we have explained the step-by-step process of how you can use Google’s new indexing API to get your website’s pages and backlinks crawled immediately. The process of setting this up isn’t typically very easy, but if you watch this video carefully and follow the given steps, I am sure you can save your time and the internet. You can get higher ranking in Search Engine. So, without further delay – let’s watch the full video and get indexed your backlinks and webpages. I hope that you were able to make great use of this Video to help you get up and running with Google’s Indexing API. Indexing process and Code: https://docs.google.com/document/d/10lIOtorCubWV94Pzz0juHUOG7c1-pqEit6s94qgFd6s/edit#heading=h.vyd4fqe3e5al

[antonymarshall]

Top 7 Skills to Become A Full-Stack Developer in 2025

With the ever-increasing pace of technological change, the need for techies with multidisciplinary skills has never been higher. One of the most sought-after jobs in the tech field today is that of a Full-Stack Developer̶ one who could smartly trick both front and back-end development. By 2025, this position promises to be even more dynamic and skill-intensive than before, requiring the developers to be multi-talented, flexible, and always learning. Thus, whether you are just stepping into this profession or you're enhancing your skill set, full stack web development entails mastering as many skills as possible to stay relevant.

Let us check out the top 7 crucial skills that every full-stack developer should develop by 2025. Front-End Expertise The user interface is the first thing people see and interact with--that's why we call this front-end work fundamental. A full-stack developer must have a good working knowledge of HTML, CSS, and JavaScript, the trifecta of front-end development. For 2025, developers who know tools like React.js, Vue.js, and Next.js are in ever-increasing demand, as these frameworks can be used to develop dynamic, highly performant, and mobile-responsive web applications. One should also know a little about aspects such as responsive design and various browser compatibilities. Grasping concepts related to state management on the front end (for example, using Redux, Zustand, or the React Context API) boosts one's professional profile, with companies recognizing these competencies.

Strong Back-End Knowledge While the front-end sees what the user gets, the back-end makes things run in the background. Full-stack developers should command the lease on server-side programming using languages such as JavaScript (Node.js), Python (Django/Flask), Java (Spring Boot), or Ruby on Rails. You would need to know how to build RESTful APIs and work with user sessions, authentication, and authorization with communications to a database. Keeping in mind the social aspect of security and data integrity, it is also important for any practice to involve the rest-audit trail, validation, error handling, etc. Knowledge of cloud platforms like AWS, Google Cloud, or Azure would be an added advantage for deploying a scalable back end.

Database Management Every full-stack developer must have some hardcore database skills. It doesn't matter if it is the relational database world of MySQL or PostgreSQL or the advanced NoSQL world of MongoDB, you need to know how to work with schema design, efficient query writing, and database connection management. In 2025, a developer must know the difference between structured and unstructured data and know when to use what type of database. Indexing, normalization, and transactions will become especially critical to you as you build scalable applications. ORMs (Object Relational Mappers) like Sequelize or Mongoose are also crucial for streamlining code/database interaction.

Understanding of APIs APIs (Application Programming Interfaces) are the glue that binds together the various pieces of a system. A full-stack developer should be able to build and consume APIs. Although REST is still the most-used tool, GraphQL has emerged as an alternative technology due to its speed and flexibility. To properly build and solve any API issues, an understanding of Postman or Insomnia as tools is necessary. Familiarity with authentication methods, such as OAuth2.0, JWT (JSON Web Tokens), and API key management, secures your applications while they communicate with the different services.

Version Control Working on software projects without version control is akin to tight-rope walking without a safety net. Developers can use Git tools to track changes or collaborate more efficiently and roll back to previous versions and full-stack developers should know Git well enough to create branches and merge code to resolve conflicts and manage pull requests. Beyond 2025, GitHub, GitLab, and Bitbucket will be more relevant to the work process of the teams. Apart from collaboration, knowing Git shows the power and the discipline in practice concerning coding.

Performance Optimization Your web app must not just work, but also work fast. Performance optimization is nowadays inevitable in an era where user experience rules. On the front ends, such performance optimization encompasses reduced render time, reduced bundle size, lazy loading of components, or using CDNs. Back-end-side optimizations include the use of caching systems such as Redis, optimization in database query usage, and using effective server-side rendering methodologies. A full-stack developer should know how to use performance monitoring tools, such as Lighthouse and Google Web Vitals, and also backend profiling tools to identify and resolve bottlenecks.

Problem-Solving & Soft Skills: While technical skills are backbone assets in development, soft skills with problem-solving capabilities do much more to separate the wheat from the chaff in talented developers. This also includes proficiency in debugging codes and high-level thinking with systematic approaches toward solving problems in everyday development. Just as essential are communication, working as a team, and working in an agile environment. More and more, employers are looking for people who work as teammates but can also adjust easily to keep pace with ever-changing requirements, while contributing positively to the dynamics of a team.

Take up a Course: If in 2025, you really want to be a Full-Stack Developer, going for a regular course is going to be an accelerator in your skills. Make sure you find one that comes with hands-on projects, industry tools, and mentorship from seasoned pros. The course should be extensive — everything from HTML and JavaScript to back-end programming and deployment. Practical experience is the name of the game; the course should emphasize building an entire web application from scratch.

Conclusion Being a full-stack developer in 2025 will entail much more than just coding: it means knowing how every part of a web application fits together-from the user interface to the database. Mastering the above-mentioned seven basic skills will ensure your position as a really well-capable and competitive developer in today's technology-enriched world.

[gloriousfestgentlemen02]

```markdown

Enhancing SEO Strategies with Google Search Console API Scripts

Google Search Console (GSC) is a powerful tool that provides webmasters with insights into how their website performs on Google's search engine. One of the lesser-known but highly effective features of GSC is its API, which allows for automation and deeper analysis through scripts. In this article, we will explore how to leverage Google Search Console API scripts to enhance your SEO strategies.

Understanding the Basics of Google Search Console API

Before diving into the specifics of using scripts, it's important to understand what the Google Search Console API offers. The API provides programmatic access to data available in the Google Search Console, including:

Search Query Data: Information about the queries users are using to find your site.

Coverage Reports: Details about indexing issues and coverage of your site.

Links to Your Site: Data about external links pointing to your site.

Security Issues: Alerts about security problems on your site.

By automating the retrieval and processing of this data, you can gain valuable insights and make informed decisions to improve your site's visibility and performance.

Benefits of Using Google Search Console API Scripts

1. Time Efficiency

One of the primary benefits of using GSC API scripts is the time saved. Instead of manually checking reports in the console, scripts can automate the process, allowing you to focus on other aspects of your SEO strategy.

2. Customized Reporting

Scripts allow for customized reporting tailored to your specific needs. You can set up automated emails or dashboards that provide you with the exact data you need at regular intervals.

3. Deeper Analysis

With scripts, you can perform more complex analyses than what is possible through the standard GSC interface. For example, you can aggregate data over multiple sites, compare performance across different time periods, or even integrate GSC data with other tools like Google Analytics.

How to Get Started with Google Search Console API Scripts

Step 1: Enable the Google Search Console API

First, you need to enable the Google Search Console API in your Google Cloud Platform account. This involves creating a project, enabling the API, and setting up authentication credentials.

Step 2: Write Your Script

Once the API is enabled, you can start writing your script. There are several programming languages you can use, including Python, JavaScript, and PHP. Here’s a simple example of a Python script that retrieves search query data:

```python

from googleapiclient.discovery import build

from oauth2client.service_account import ServiceAccountCredentials

Set up credentials

credentials = ServiceAccountCredentials.from_json_keyfile_name('path/to/your/keyfile.json', ['https://www.googleapis.com/auth/webmasters'])

Build the service

service = build('webmasters', 'v3', credentials=credentials)

Define the request

request = {

'startDate': '2022-01-01',

'endDate': '2022-01-31',

'dimensions': ['query'],

'rowLimit': 10

}

Execute the request

response = service.searchanalytics().query(siteUrl='https://example.com/', body=request).execute()

Print the response

print(response)

```

Step 3: Automate and Integrate

After testing your script, you can automate it using tools like cron jobs or Google Apps Script. You can also integrate the data with other platforms for comprehensive analysis.

Conclusion

The Google Search Console API opens up a world of possibilities for SEO professionals. By leveraging scripts, you can automate tasks, gain deeper insights, and make more informed decisions. As you experiment with different scripts and integrations, you'll likely discover new ways to optimize your site and improve its performance on Google's search engine.

What are some specific use cases or challenges you've encountered when working with the Google Search Console API? Share your thoughts and experiences in the comments below!

```

加飞机@yuantou2048

蜘蛛池出租

谷歌留痕

[learning-code-ficusoft]

How to Deploy Your Full Stack Application: A Beginner’s Guide

Deploying a full stack application involves setting up your frontend, backend, and database on a live server so users can access it over the internet. This guide covers deployment strategies, hosting services, and best practices.

1. Choosing a Deployment Platform

Popular options include:

Cloud Platforms: AWS, Google Cloud, Azure

PaaS Providers: Heroku, Vercel, Netlify

Containerized Deployment: Docker, Kubernetes

Traditional Hosting: VPS (DigitalOcean, Linode)

2. Deploying the Backend

Option 1: Deploy with a Cloud Server (e.g., AWS EC2, DigitalOcean)

Set Up a Virtual Machine (VM)

bash

ssh user@your-server-ip

Install Dependencies

Node.js (sudo apt install nodejs npm)

Python (sudo apt install python3-pip)

Database (MySQL, PostgreSQL, MongoDB)

Run the Server

bash

nohup node server.js & # For Node.js apps gunicorn app:app --daemon # For Python Flask/Django apps

Option 2: Serverless Deployment (AWS Lambda, Firebase Functions)

Pros: No server maintenance, auto-scaling

Cons: Limited control over infrastructure

3. Deploying the Frontend

Option 1: Static Site Hosting (Vercel, Netlify, GitHub Pages)

Push Code to GitHub

Connect GitHub Repo to Netlify/Vercel

Set Build Command (e.g., npm run build)

Deploy and Get Live URL

Option 2: Deploy with Nginx on a Cloud Server

Install Nginx

bash

sudo apt install nginx

Configure Nginx for React/Vue/Angular

nginx

server { listen 80; root /var/www/html; index index.html; location / { try_files $uri /index.html; } }

Restart Nginx

bash

sudo systemctl restart nginx

4. Connecting Frontend and Backend

Use CORS middleware to allow cross-origin requests

Set up reverse proxy with Nginx

Secure API with authentication tokens (JWT, OAuth)

5. Database Setup

Cloud Databases: AWS RDS, Firebase, MongoDB Atlas

Self-Hosted Databases: PostgreSQL, MySQL on a VPS

bash# Example: Run PostgreSQL on DigitalOcean sudo apt install postgresql sudo systemctl start postgresql

6. Security & Optimization

✅ SSL Certificate: Secure site with HTTPS (Let’s Encrypt) ✅ Load Balancing: Use AWS ALB, Nginx reverse proxy ✅ Scaling: Auto-scale with Kubernetes or cloud functions ✅ Logging & Monitoring: Use Datadog, New Relic, AWS CloudWatch

7. CI/CD for Automated Deployment

GitHub Actions: Automate builds and deployment

Jenkins/GitLab CI/CD: Custom pipelines for complex deployments

Docker & Kubernetes: Containerized deployment for scalability

Final Thoughts

Deploying a full stack app requires setting up hosting, configuring the backend, deploying the frontend, and securing the application. 

Cloud platforms like AWS, Heroku, and Vercel simplify the process, while advanced setups use Kubernetes and Docker for scalability.

WEBSITE: https://www.ficusoft.in/full-stack-developer-course-in-chennai/

[jprie]

Full Stack Developer Roadmap: Skills, Tools, and Best Practices

Creating a Full Stack Developer Roadmap involves mapping out the essential skills, tools, and best practices required to become proficient in both front-end and back-end development. Here's a comprehensive guide to help you understand the various stages in the journey to becoming a Full Stack Developer:

1. Fundamentals of Web Development

Before diving into full-stack development, it's essential to understand the core building blocks of web development:

1.1. HTML/CSS

HTML: The markup language used for creating the structure of web pages.

CSS: Used for styling the visual presentation of web pages (layouts, colors, fonts, etc.).

Best Practices: Write semantic HTML, use CSS preprocessors like Sass, and ensure responsive design with media queries.

1.2. JavaScript

JavaScript (JS): The programming language that adds interactivity to web pages.

Best Practices: Use ES6+ syntax, write clean and maintainable code, and implement asynchronous JavaScript (promises, async/await).

2. Front-End Development

The front end is what users see and interact with. A full-stack developer needs to master front-end technologies.

2.1. Front-End Libraries & Frameworks

React.js: A popular library for building user interfaces, focusing on reusability and performance.

Vue.js: A progressive JavaScript framework for building UIs.

Angular: A platform and framework for building single-page client applications.

Best Practices: Use state management tools (like Redux or Vuex), focus on component-based architecture, and optimize performance.

2.2. Version Control (Git)

Git: Essential for tracking changes and collaborating with others.

GitHub/GitLab/Bitbucket: Platforms for hosting Git repositories.

Best Practices: Commit often with meaningful messages, use branching strategies (like GitFlow), and create pull requests for review.

3. Back-End Development

The back end handles the data processing, storage, and logic behind the scenes. A full-stack developer must be proficient in server-side development.

3.1. Server-Side Languages

Node.js: JavaScript runtime for server-side development.

Python (Django/Flask): Python frameworks used for building web applications.

Ruby (Rails): A full-stack framework for Ruby developers.

PHP: Widely used for server-side scripting.

Java (Spring Boot): A powerful framework for building web applications in Java.

3.2. Databases

SQL Databases (e.g., PostgreSQL, MySQL): Used for relational data storage.

NoSQL Databases (e.g., MongoDB, Firebase): For non-relational data storage.

Best Practices: Design scalable and efficient databases, normalize data for SQL, use indexing and query optimization.

4. Web Development Tools & Best Practices

4.1. API Development and Integration

REST APIs: Learn how to create and consume RESTful APIs.

GraphQL: A query language for APIs, providing a more flexible and efficient way to retrieve data.

Best Practices: Design APIs with scalability in mind, use proper status codes, and document APIs with tools like Swagger.

4.2. Authentication & Authorization

JWT (JSON Web Tokens): A popular method for handling user authentication in modern web applications.

OAuth: Open standard for access delegation commonly used for logging in with third-party services.

Best Practices: Implement proper encryption, use HTTPS, and ensure token expiration.

4.3. Testing

Unit Testing: Testing individual components of the application.

Integration Testing: Testing how different components of the system work together.

End-to-End (E2E) Testing: Testing the entire application workflow.

Best Practices: Use testing libraries like Jest (JavaScript), Mocha, or PyTest (Python) and ensure high test coverage.

4.4. DevOps & Deployment

Docker: Containerization of applications for consistency across environments.

CI/CD Pipelines: Automating the process of building, testing, and deploying code.

Cloud Platforms: AWS, Azure, Google Cloud, etc., for deploying applications.

Best Practices: Use version-controlled deployment pipelines, monitor applications in production, and practice continuous integration.

4.5. Performance Optimization

Caching: Use caching strategies (e.g., Redis) to reduce server load and speed up response times.

Lazy Loading: Load parts of the application only when needed to reduce initial loading time.

Minification and Bundling: Minimize JavaScript and CSS files to improve load time.

5. Soft Skills & Best Practices

Being a full-stack developer also requires strong problem-solving skills and an ability to work collaboratively in teams.

5.1. Communication

Communicate effectively with team members, clients, and stakeholders, especially regarding technical requirements and issues.

5.2. Agile Development

Understand Agile methodologies (Scrum, Kanban) and work in sprints to deliver features incrementally.

5.3. Code Reviews & Collaboration

Regular code reviews help maintain code quality and foster learning within teams.

Practice pair programming and collaborative development.

6. Continuous Learning

The tech industry is always evolving, so it’s essential to stay up to date with new tools, languages, and frameworks.

Follow Blogs & Podcasts: Stay updated with the latest in full-stack development.

Contribute to Open Source: Engage with the developer community by contributing to open-source projects.

Build Side Projects: Continuously apply what you've learned by working on personal projects.

7. Additional Tools & Technologies

Webpack: A module bundler to optimize the workflow.

GraphQL: For efficient data fetching from APIs.

WebSockets: For real-time communication in web applications.

Conclusion

Becoming a proficient full-stack developer requires a combination of technical skills, tools, and a strong understanding of best practices. By mastering both front-end and back-end technologies, keeping up with industry trends, and continuously learning, you'll be equipped to build modern, scalable web applications.

Fullstack course in chennai

Fullstack development course in chennai

Fullstack training in chennai

[govindhtech]

Boost AI Production With Data Agents And BigQuery Platform

Data accessibility can hinder AI adoption since so much data is unstructured and unmanaged. Data should be accessible, actionable, and revolutionary for businesses. A data cloud based on open standards, that connects data to AI in real-time, and conversational data agents that stretch the limits of conventional AI are available today to help you do this.

An open real-time data ecosystem

Google Cloud announced intentions to combine BigQuery into a single data and AI use case platform earlier this year, including all data formats, numerous engines, governance, ML, and business intelligence. It also announces a managed Apache Iceberg experience for open-format customers. It adds document, audio, image, and video data processing to simplify multimodal data preparation.

Volkswagen bases AI models on car owner’s manuals, customer FAQs, help center articles, and official Volkswagen YouTube videos using BigQuery.

New managed services for Flink and Kafka enable customers to ingest, set up, tune, scale, monitor, and upgrade real-time applications. Data engineers can construct and execute data pipelines manually, via API, or on a schedule using BigQuery workflow previews.

Customers may now activate insights in real time using BigQuery continuous queries, another major addition. In the past, “real-time” meant examining minutes or hours old data. However, data ingestion and analysis are changing rapidly. Data, consumer engagement, decision-making, and AI-driven automation have substantially lowered the acceptable latency for decision-making. The demand for insights to activation must be smooth and take seconds, not minutes or hours. It has added real-time data sharing to the Analytics Hub data marketplace in preview.

Google Cloud launches BigQuery pipe syntax to enable customers manage, analyze, and gain value from log data. Data teams can simplify data conversions with SQL intended for semi-structured log data.

Connect all data to AI

BigQuery clients may produce and search embeddings at scale for semantic nearest-neighbor search, entity resolution, semantic search, similarity detection, RAG, and recommendations. Vertex AI integration makes integrating text, photos, video, multimodal data, and structured data easy. BigQuery integration with LangChain simplifies data pre-processing, embedding creation and storage, and vector search, now generally available.

It previews ScaNN searches for large queries to improve vector search. Google Search and YouTube use this technology. The ScaNN index supports over one billion vectors and provides top-notch query performance, enabling high-scale workloads for every enterprise.

It is also simplifying Python API data processing with BigQuery DataFrames. Synthetic data can replace ML model training and system testing. It teams with Gretel AI to generate synthetic data in BigQuery to expedite AI experiments. This data will closely resemble your actual data but won’t contain critical information.

Finer governance and data integration

Tens of thousands of companies fuel their data clouds with BigQuery and AI. However, in the data-driven AI era, enterprises must manage more data kinds and more tasks.

BigQuery’s serverless design helps Box process hundreds of thousands of events per second and manage petabyte-scale storage for billions of files and millions of users. Finer access control in BigQuery helps them locate, classify, and secure sensitive data fields.

Data management and governance become important with greater data-access and AI use cases. It unveils BigQuery’s unified catalog, which automatically harvests, ingests, and indexes information from data sources, AI models, and BI assets to help you discover your data and AI assets. BigQuery catalog semantic search in preview lets you find and query all those data assets, regardless of kind or location. Users may now ask natural language questions and BigQuery understands their purpose to retrieve the most relevant results and make it easier to locate what they need.

It enables more third-party data sources for your use cases and workflows. Equifax recently expanded its cooperation with Google Cloud to securely offer anonymized, differentiated loan, credit, and commercial marketing data using BigQuery.

Equifax believes more data leads to smarter decisions. By providing distinctive data on Google Cloud, it enables its clients to make predictive and informed decisions faster and more agilely by meeting them on their preferred channel.

Its new BigQuery metastore makes data available to many execution engines. Multiple engines can execute on a single copy of data across structured and unstructured object tables next month in preview, offering a unified view for policy, performance, and workload orchestration.

Looker lets you use BigQuery’s new governance capabilities for BI. You can leverage catalog metadata from Looker instances to collect Looker dashboards, exploration, and dimensions without setting up, maintaining, or operating your own connector.

Finally, BigQuery has catastrophe recovery for business continuity. This provides failover and redundant compute resources with a SLA for business-critical workloads. Besides your data, it enables BigQuery analytics workload failover.

Gemini conversational data agents

Global organizations demand LLM-powered data agents to conduct internal and customer-facing tasks, drive data access, deliver unique insights, and motivate action. It is developing new conversational APIs to enable developers to create data agents for self-service data access and monetize their data to differentiate their offerings.

Conversational analytics

It used these APIs to create Looker’s Gemini conversational analytics experience. Combine with Looker’s enterprise-scale semantic layer business logic models. You can root AI with a single source of truth and uniform metrics across the enterprise. You may then use natural language to explore your data like Google Search.

LookML semantic data models let you build regulated metrics and semantic relationships between data models for your data agents. LookML models don’t only describe your data; you can query them to obtain it.

Data agents run on a dynamic data knowledge graph. BigQuery powers the dynamic knowledge graph, which connects data, actions, and relationships using usage patterns, metadata, historical trends, and more.

Last but not least, Gemini in BigQuery is now broadly accessible, assisting data teams with data migration, preparation, code assist, and insights. Your business and analyst teams can now talk with your data and get insights in seconds, fostering a data-driven culture. Ready-to-run queries and AI-assisted data preparation in BigQuery Studio allow natural language pipeline building and decrease guesswork.

Connect all your data to AI by migrating it to BigQuery with the data migration application. This product roadmap webcast covers BigQuery platform updates.

Read more on Govindhtech.com

[uwteam]

9 sierpnia 2024

◢ #unknownews ◣

Najnowszy przegląd subiektywnie najciekawszych treści z branży IT z mijającego tygodnia jest już dostępny - zapraszam do lektury :)

1) Każdy może uzyskać dostęp do danych z usuniętych i do prywatnych repozytoriów na GitHub https://trufflesecurity.com/blog/anyone-can-access-deleted-and-private-repo-data-github INFO: Odkryto poważną lukę bezpieczeństwa w systemie GitHub, nazwaną Cross Fork Object Reference (CFOR), która umożliwia dostęp do danych z usuniętych forków, usuniętych repozytoriów, a nawet prywatnych repozytoriów. Artykuł demonstruje, jak poprzez znajomość hasha commita można uzyskać dostęp do wrażliwych danych, które teoretycznie powinny być niedostępne, co stanowi ogromne zagrożenie dla organizacji korzystających z GitHub. Autorzy podkreślają, że problem ten wynika z celowej architektury GitHub, ale większość użytkowników nie jest świadoma tych mechanizmów, co może prowadzić do niezamierzonego ujawnienia poufnych informacji, np. kluczy API.

2) Bazy wektorowe - szybkie wprowadzenie do pracy z nimi (film, 15m) https://youtu.be/vcZK6B61teY INFO: Skrajnie skondensowany wstęp do pracy z bazami wektorowymi na przykładzie Qdranta. Jak stworzyć bazę, jak nakarmić ją własnymi danymi i jak odpytywać o konkretne dane z użyciem języka naturalnego. Film prezentuje korzystanie z dwóch API - tego od OpenAI (do generowania samych wektorów) i tego natywnego, od Qdranta.

3) CTF od Wiz - oszukaj AI i zdobądź bilet za darmo https://promptairlines.com/ INFO: To gra online, w której rozmawiasz z chatbotem pracującym w liniach lotniczych. Twoim zadaniem jest zmuszenie go do sprzedania (ofiarowania) ci biletu za darmo. Oczywiście, aby to osiągnąć, trzeba posłużyć się technikami prompt injection.

4) Wyciek sekretów przez skompilowany plik Pythona https://jfrog.com/blog/leaked-pypi-secret-token-revealed-in-binary-preventing-suppy-chain-attack/ INFO: Interesujący przypadek wycieku tokena dostępowego do repozytoriów GitHuba, Pythona, PyPI i Python Software Foundation został wykryty przez zespół JFrog w publicznym kontenerze Docker, co mogło potencjalnie prowadzić do katastrofalnego "ataku na łańcuch dostaw". Zobacz, jak namierzono wyciek i jak zareagowała na niego dotknięta wyciekiem organizacja.

5) Metody omijania mechanizmów antyphishingowych - jak to działa? https://posts.specterops.io/like-shooting-phish-in-a-barrel-926c1905bb4b INFO: Jak ominąć mechanizmy ochrony przed phishingiem, takie jak crawlery linków, tego dowiesz się z artykułu. Autor przedstawia różne techniki obejścia tych zabezpieczeń. Omawia metody takie jak wykorzystanie CAPTCHA, wielokrotne przekierowania, wykrywanie botów poprzez fingerprinting przeglądarki, blokowanie ASN czy wykorzystanie alertów JavaScript, podkreślając jednocześnie wady samych crawlerów linków. Interesująca lektura, zwłaszcza dla ludzi z branży cybersecurity.

6) Techniki ucieczki z kontenerów w środowiskach chmurowych https://unit42.paloaltonetworks.com/container-escape-techniques/ INFO: Artykuł omawia techniki ucieczki z kontenerów, ich potencjalne skutki i pokazuje, jak wykrywać takie techniki ataku.

7) Jak Google obsługuje JavaScript w procesie indeksowania stron? https://vercel.com/blog/how-google-handles-javascript-throughout-the-indexing-process INFO: Czy Google może renderować stronę z JavaScriptem? Czy traktuje różnie strony z dużą ilością JavaScriptu? Odpowiedzi na te pytania są kluczowe dla optymalizacji stron pod kątem wyszukiwarek. Artykuł przedstawia wyniki badań, które miały na celu wyjaśnić, jak Google obsługuje JavaScript w procesie indeksowania.

8) Przegląd nowości technologicznych z lipca - od Fireship (film, 9m) https://youtu.be/l0e9i8zXcIs?si=V3tk1E75adeo7utV INFO: YouTuber Fireship w swoim specyficznym, humorystycznym stylu omawia głośne wydarzenia z branży IT, które miały miejsce w lipcu. Jest coś o AI od Google, o zmianach w NodeJS, o błędach w procesorach Intela i o kilku innych ważnych wydarzeniach. Łącznie omówił kilkanaście newsów.

9) Zatrudnianie i zarządzanie inżynierami z pasją https://newsletter.posthog.com/p/hiring-and-managing-cracked-engineers INFO: Zbiór cennych wskazówek dotyczących pracy z wyjątkowo utalentowanymi inżynierami, określanymi jako "cracked engineers". Artykuł definiuje cechy charakterystyczne, które pozwolą Ci łatwiej wytypować takich pracowników (albo samemu się do nich zaliczyć). Artykuł przedstawia sześć kluczowych strategii efektywnego zarządzania takimi ludźmi, podkreślając znaczenie entuzjazmu, autonomii i tworzenia odpowiedniego środowiska pracy. Lektura zdecydowanie dla osób na liderskich stanowiskach.

10) Zarządzanie ludźmi osiągającymi niewystarczające wyniki w pracy https://jackdanger.com/managing-underperformers/ INFO: Zarządzanie pracownikami o niskiej wydajności to niezłe wyzwanie dla menedżerów. Artykuł wyróżnia dwa główne typy niskiej wydajności: odmowę dostosowania się do celów firmy oraz niezdolność do wykonywania zadań, przedstawiając przy tym konkretne strategie radzenia sobie z każdym z nich.

11) Sztuka pisania promptów - 13 dobrych rad https://frontbackgeek.com/prompt-writing-essentials-guide/ INFO: W świecie sztucznej inteligencji i uczenia maszynowego umiejętność pisania promptów stała się czymś kluczowym. W tym artykule znajdziesz zbiór kilkunastu porad, które pozwolą Ci tworzyć lepsze zapytania i lepiej zrozumieć, jak porozumiewać się z modelami językowymi.

12) Sztuczna inteligencja zwiększa obciążenie pracą i nie spełnia oczekiwań menadżerów? https://www.forbes.com/sites/bryanrobinson/2024/07/23/employees-report-ai-increased-workload/ INFO: Nowe badanie przeprowadzone na 2500 pracownikach i menedżerach na całym świecie ujawnia, że wdrożenie sztucznej inteligencji w miejscu pracy przynosi nieoczekiwane rezultaty. Wbrew oczekiwaniom kadry zarządzającej, 77% pracowników korzystających z AI twierdzi, że technologia ta zwiększyła ich obciążenie pracą i utrudniła osiągnięcie oczekiwanego wzrostu produktywności. Badanie wskazuje na rozbieżność między optymistycznymi oczekiwaniami menedżerów a rzeczywistymi doświadczeniami pracowników, podkreślając potrzebę lepszego zrozumienia i wdrożenia AI w środowisku pracy.

13) Jak wdrożyć sprytną politykę obsługi długu technologicznego? https://zaidesanton.substack.com/p/how-to-implement-20-for-tech-debt- INFO: Artykuł omawia koncepcję przeznaczania 20% czasu pracy na spłatę długu technicznego w zespołach programistycznych. Autor przedstawia praktyczne wskazówki, jak wdrożyć taką zasadę, podkreślając przy okazji, jak ważne znaczenie ma systematyczne praktykowanie tej zasady. W artykule znajdziesz konkretne sugestie skutecznych działań, które można wprowadzić w celu zmniejszania długu technologicznego.

14) Jak lepiej zrozumieć i zapamiętać złożone zagadnienia? https://learnhowtolearn.org/how-to-understand-and-retain-any-concept-10x-better/ INFO: Autor prezentuje innowacyjną technik�� nauki, która obiecuje dziesięciokrotnie lepsze zrozumienie i zapamiętywanie dowolnego materiału. Metoda ta opiera się na prostym, ale niezwykle skutecznym podejściu do przyswajania wiedzy. Artykuł zawiera praktyczne wskazówki, jak wdrożyć tę technikę w codziennym życiu.

15) Jak Postgres przechowuje dane na dysku? https://drew.silcock.dev/blog/how-postgres-stores-data-on-disk/ INFO: W artykule autor szczegółowo wyjaśnia, w jaki sposób PostgreSQL przechowuje dane na dysku, omawiając przy tym strukturę katalogów, plików i stron (pages) używanych przez bazę danych. Autor wyjaśnia także koncepcje, takie jak sterta (heap), strony danych oraz mechanizm MVCC (Multiversion Concurrency Control). Omawiane zagadnienia zawierają użyteczne i praktyczne przykłady. Lektura dla ludzi lubiących bardzo dogłębnie poznawać zasadę działania każdej technologii.

16) Raport o bezpieczeństwie aplikacji internetowych - od Cloudflare https://blog.cloudflare.com/application-security-report-2024-update INFO: Firma Cloudflare przedstawia przegląd stanu bezpieczeństwa aplikacji internetowych w 2024 roku, zwracając uwagę na trendy w branży security. Najpopularniejszy wektor ataku to nadal ataki DDoS. Jakieś 7% całego ruchu webowego jest wyłapywane przez wszelkiego rodzaju systemy bezpieczeństwa jako złośliwe zapytania. Ogromna część (ponad 60%) dynamicznego ruchu webowego to zapytania API. Te i inne ciekawostki znajdziesz w raporcie.

17) Wykorzystanie LLM-ów przy wyszukiwaniu błędów w oprogramowaniu https://engineering.razorpay.com/secure-code-reviewer-copilot-e4f575f42591 INFO: Firma Razorpay opowiada o swoim doświadczeniu z integracją modeli językowych z wewnętrznym procesem code review, aby zwiększyć tym samym bezpieczeństwo swoich aplikacji. Zobacz, jak LLM-y mogą pomóc w wykrywaniu luk w zabezpieczeniach i podnieść bezpieczeństwo aplikacji.

18) Jak Digital Service Act (DSA) zmienia Internet i co to oznacza dla Ciebie? https://webmetric.com/wiedza/inna-perspektywa/jak-digital-service-act-zmienia-internet-i-co-to-znaczy-dla-kazdego-kto-dziala-w-sieci/ INFO: Nowy unijny przepis, czyli Digital Services Act (DSA), mocno miesza w internetowym świecie, wszystko oczywiście dla naszego bezpieczeństwa. DSA każe platformom internetowym wziąć się do roboty - muszą szybko kasować nielegalne treści, pokazać, jak działają ich algorytmy polecające treści, i lepiej chronić dzieciaki online. Artykuł dokładnie tłumaczy, o co chodzi w DSA, jak to wpłynie na różne firmy i zwykłych ludzi w necie.

19) Ankieta deweloperów Stack Overflow 2024 - omówienie https://survey.stackoverflow.co/2024/ INFO: Omówienie wyników corocznego badania od Stack Overflow. Jakie języki są najpopularniejsze, z jakich baz korzystają programiści, jak bardzo angażują w swoją pracę sztuczną inteligencję, co ich najbardziej wkurza i cieszy w pracy. Tego i wielu innych rzeczy dowiesz się z omówienia wyników ankiety.

20) Wykrywacz fałszywych pamięci Flash (karty/pendrive) https://fight-flash-fraud.readthedocs.io/en/latest/introduction.html INFO: Kupiłeś w azjatyckim sklepie za grosze pamięć flash mającą 4 TB, ale jakimś cudem nie możesz na nią wrzucić nawet pliku 4 GB? Prawdopodobnie ktoś majstrował przy tej pamięci i zmienił sposób, w jaki wykrywana jest ona w systemie. Ta aplikacja potrafi po pierwsze wykryć takie oszustwo, a po drugie potrafi nadpisać zmodyfikowane wartości pamięci flash, sprawiając, że da się z niej normalnie korzystać.

21) Jak usunąć elementy z tablic w JavaScript (9 sposobów) https://jsdev.space/howto/remove-from-array/ INFO: Niby prosta operacja, a jednak usuwanie elementów z tablicy w JS może sprawiać pewne problemy. Artykuł przedstawia aż dziewięć różnych metod na wykonanie tej operacji - od popularnych jak pop() i shift(), przez bardziej zaawansowane jak splice() i filter(), aż po te mniej znane techniki wykorzystujące operatory delete czy pętle.

22) Tekst dynamicznie dopasowany do szerokości - CSS https://kizu.dev/fit-to-width/ INFO: Artykuł przedstawia innowacyjne rozwiązanie problemu dopasowywania tekstu do szerokości kontenera w CSS, wykorzystując nową właściwość text-wrap: balance. Autor szczegółowo omawia różne scenariusze zastosowania tej techniki, pokazując, jak można ją wykorzystać do poprawy czytelności tekstu na stronach.

23) Sztuczki terminalowe - kompilacja jednolinijkowców https://github.com/onceupon/Bash-Oneliner INFO: Zbiór przydatnych poleceń i sztuczek terminalowych do przetwarzania danych i obsługi systemu Linux. Są to tzw. jednolinijkowce, które mogą przydać Ci się w codziennej pracy, a przy okazji mogą podnieść skilla związanego z pracą w terminalu.

24) Projektowanie bazy danych na przykładzie Google Calendar https://kb.databasedesignbook.com/posts/google-calendar/ INFO: To kolejny artykuł z serii związanej z projektowaniem oprogramowania. Tym razem autor stara się zaprojektować bazę danych pod projekt zbliżony do Google Kalendarza. To dobry materiał dla osób zainteresowanych inżynierią oprogramowania i projektowaniem baz danych.

25) Audapolis - edytor audio z automatyczną transkrypcją tekstu https://github.com/bugbakery/audapolis INFO: To otwartoźródłowa aplikacja bardzo podobna do komercyjnego Descript. Jeśli edytujesz plik dźwiękowy z mową ludzką (np. wykład), to widzisz jego transkrypcję. Usuwając słowa z transkrypcji, usuwasz je także z pliku dźwiękowego. Można więc żartobliwie powiedzieć, że jest to tekstowy edytor dźwiękowy.

26) Google NIE będzie blokować zewnętrznych cookiesów w Chrome https://stackdiary.com/google-will-not-phase-out-tracking-cookies-in-chrome-after-all/ INFO: Firma Google niespodziewanie zmieniła swoje plany dotyczące blokowania ciasteczek stron trzecich w przeglądarce. Miało to podnieść bezpieczeństwo użytkowników i mocno ograniczyć możliwość śledzenia. Jednak, zamiast całkowitej blokady takich ciastek, Google zamierza dać użytkownikom możliwość wyboru, czy chcą zezwolić na ich stosowanie, jednocześnie kontynuując prace nad alternatywnym rozwiązaniem, czyli Privacy Sandbox. To ważna decyzja, zwłaszcza dla branży reklamowej. Ciekawi mnie tylko, jak ta zgoda będzie przez użytkowników wyrażana i czy aby nie będzie to kolejne wkurzające pytanie przy wchodzeniu na każdą stronę.

== LINKI TYLKO DLA PATRONÓW ==

27) Pytania rekrutacyjne dla działów Red Team https://uw7.org/un_1c7cfd075ef37 INFO: Obszerny spis pytań i tematów związanych z działaniami zespołów Red Team, obejmujący szeroki zakres zagadnień od absolutnych podstaw, przez ataki na systemy Windows, aż po zaawansowane techniki omijania zabezpieczeń. Materiał może służyć zarówno jako przewodnik do przygotowania się do rozmów kwalifikacyjnych w zespołach Red Team, jak i jako zbiór tematów do poszerzania swoich kompetencji.

28) Wstęp do tworzenia exploitów - poradnik https://uw7.org/un_58620ef142d09 INFO: Poradnik przedstawia kompleksową ścieżkę nauki dla osób zainteresowanych wykorzystywaniem luk w zabezpieczeniach oprogramowania, ze szczególnym uwzględnieniem exploitacji binarnej. Autor szczegółowo omawia wymagane umiejętności, linkuje do materiałów dodatkowych oraz podaje kolejność ich przerabiania, koncentrując się na takich tematach jak programowanie w C, asembler x86-64, podstawy Linuxa oraz wprowadza kilka zaawansowanych technik ataku na aplikacje. Warto podążyć za linkami, ponieważ zawierają one między innymi zadania, których wykonanie zdecydowanie zwiększy Twoje zrozumienie tematu.

[godigiinfotech1]

The Foundation of Web Applications - An Complete Guide to Back-End Development

Front-end developer focus on the user interface & Back-end developer manage the server-side logic & database management that drive web applications. We will look at important technologies, suggested methods for aspiring full stack engineers, and the basic concepts of back-end development in this blog.

Understanding of Back-End Development

Building and maintaining the server, database, and application logic are all part of back-end development. It guarantees that data is appropriately processed, accessed, and saved, offering the capability required for front-end users to interact with .

Key Back-End Technologies

Server-Side Languages:

Node.js :

Purpose -the purpose is JavaScript runtime built on Chrome’s V8 engine, used for building fast & scalable server-side applications.

Key Concepts - Event-driven architecture, non-blocking I/O, Express framework.

Best Practices - Use middleware effectively, manage errors, optimize performance.

Python :

Purpose - Advanced interpreted language that is famous for being fast and understandable.

Key Concepts - ORM (Object-Relational Mapping), RESTful APIs, and the Flask and Django frameworks.

Best Practices - Create clean code, implement virtual environments, and follow to PEP 8 principles.

Ruby:

Purpose - Dynamic, object-oriented language designed for simplicity and productivity.

Key Concepts - Ruby on Rails framework, MVC architecture, Active Record.

Best Practices: Use gems judiciously, follow the Ruby style guide, test extensively.

Databases :

SQL Databases:

Examples - MySQL, PostgreSQL.

Key Concepts - Structured query language, relational tables, ACID properties.

Best Practices - Normalize databases, use indexes, backup regularly.

NoSQL Databases:

Examples - MongoDB, CouchDB.

Key Concepts - Document stores, key-value pairs, schema flexibility.

Best Practices - Optimize for read/write performance, use appropriate data models, ensure data integrity.

Back-End Frameworks

Express.js (Node.js):

Purpose - Minimalist web framework for Node.js.

Key Concepts - Middleware, routing, request/response handling.

Best Practices - Modularize routes, use environment variables, handle errors gracefully.

Django (Python):

Purpose - High level web framework that promotes efficient development & clean, pragmatic design.

Key Concepts - ORM, URL routing, template engine.

Best Practices - Follow the Django project structure, use Django’s built-in admin, secure your application.

Ruby on Rails:

Purpose - Server-side web application framework written in Ruby.

Key Concepts - Convention over configuration, Active Record, RESTful design.

Best Practices - Adhere to Rails conventions, use strong parameters, implement caching.

APIs and RESTful Services

Purpose: API - Application Programming Interfaces allows different software systems to communicate. REST API is common approach to create APIs.

Key Concepts - HTTP methods (GET, POST, PUT, DELETE), endpoints, JSON data format.

Best Practices - Design intuitive endpoints, use proper HTTP status codes, document your API.

Authentication and Security

Authentication Methods:

Session Based - Storing user session data on the server.

Token Based - Using tokens (example JWT) to authenticate requests.

OAuth - Third-party authentication (example logging in with Google).

Security Best Practices:

Data Encryption - Use SSL/TLS for secure communication.

Access Control - Implement proper user roles and permissions.

For online applications to be secure, trustworthy & effective, back-end development is important. You can guarantee the smooth and secure operation of your apps by become an expert in server-side languages, databases, frameworks, and best practices. Maintaining proficiency in a continuously developing sector needs continuous learning and practice.

Build Your Dream Project : Start Your Full Stack Path Today

[websourceitsolution]

🚀 Looking for the best web design services in Mohammadpur, Dhaka? Look no further! 🚀

🚀 Looking for the best web design services in Mohammadpur, Dhaka? Look no further! 🚀

Web Source IT Solution offers top-notch website design and development solutions tailored to meet your specific needs. Whether you're in Mohammadpur, Lalmatia, Sankar, Adabor, or Shyamoli, our team of expert web designers is here to elevate your online presence!

🌐 Why Choose Web Source IT Solution? 🌐

🔹 Front-End Development:

HTML/CSS: Create responsive, accessible web layouts using HTML5, CSS3, Flexbox, and CSS Grid.

JavaScript: Skilled in vanilla JS and frameworks like React, Angular, Vue.js. Expertise in building interactive UIs and managing state with Redux.

Responsive Design: Ensure mobile-friendly websites with Bootstrap and Tailwind CSS.

Cross-Browser Compatibility: Guarantee consistency across different browsers and platforms.

🔹 Back-End Development:

Languages: Proficient in Node.js, Python (Django, Flask), PHP (Laravel), Ruby on Rails.

APIs: Design and consume RESTful APIs and GraphQL.

Database Management: Expert in SQL (MySQL, PostgreSQL) and NoSQL (MongoDB). Proficient in database design, indexing, and optimization.

Security: Implement secure authentication with JWT, OAuth, and session-based methods.

🔹 Full-Stack Development:

MEAN/MERN Stack: Full-stack development with MongoDB, Express.js, Angular/React, and Node.js.

LAMP Stack: Develop web applications using Linux, Apache, MySQL, and PHP.

Testing & Debugging: Use Jest, Mocha, Selenium for testing. Skilled in debugging with browser developer tools and server-side logging.

🔹 DevOps and Deployment:

Version Control: Proficient with Git and GitHub/GitLab.

CI/CD: Set up CI/CD pipelines using Jenkins, CircleCI, GitHub Actions.

Cloud Services: Experienced with AWS, Google Cloud, Azure. Proficient with services like EC2, S3, Lambda, Firebase.

Containerization: Utilize Docker and Kubernetes.

🔹 UI/UX Design:

Design Tools: Proficient in Adobe XD, Figma, Sketch.

User Experience: Apply user-centered design principles and conduct usability testing.

🔹 Project Management and Collaboration:

Agile Methodologies: Experience with Scrum, Kanban. Use tools like Jira, Trello.

Team Collaboration: Strong communication for cross-functional teamwork.

🔹 Security Best Practices:

Secure Coding: Knowledgeable about OWASP Top 10 vulnerabilities.

SSL/TLS: Implement secure data transmission.

Example Projects:

E-commerce Websites: Full-featured platforms with user authentication, product management, shopping cart, and payment integration.

Social Media Applications: Real-time messaging, notifications, recommendation engines.

CMS: Customizable content management systems with user-friendly admin panels and rich text editing.

Technologies and Tools:

Languages: HTML, CSS, JavaScript, TypeScript, PHP, Python, Ruby, SQL.

Frameworks/Libraries: React, Angular, Vue.js, Node.js, Django, Flask, Laravel, Ruby on Rails.

Databases: MySQL, PostgreSQL, MongoDB, Redis.

Tools: Git, Docker, Jenkins, AWS, Google Cloud, Adobe XD, Figma.

Contact Us: 📞 Phone & WhatsApp: 01947203573 ☎️ Hotline: +8809638606772 📧 E-mail: info@websourcebd.com 🌐 Website: websourcebd.com 🏠 Address: House #1283, Road #11, Avenue #2, Mirpur DOHS, Dhaka-1216, Bangladesh

Hire the best web designers in Mohammadpur today and take your business to new heights with our exceptional web design and development services! 🌟

#WebDesign #Mohammadpur #Dhaka #WebDevelopment #WebSourceITSolution #ResponsiveDesign #FullStackDevelopment #UIUXDesign #GraphicDesign #LogoDesign #MobileAppDevelopment #WebDesignCompany

[earningtrends]

Automate Bulk URL Indexing with Google Indexing API Using Python

Automate Bulk URL Indexing: Google Indexing API and Python Introduction: The vast expanse of the internet can feel like a crowded marketplace for websites. How do you ensure your website stands out and gets discovered by the right audience? Search engine optimization (SEO) plays a crucial role, and a fundamental aspect of SEO is getting your website’s URLs indexed by search engines.…

[scherbiustechnology03]

Mastering the Art of Full Stack Development

Full Stack development has become increasingly popular in the tech industry due to its versatility and efficiency. It refers to the ability to work on both the front-end and back-end aspects of web development, making developers proficient in multiple programming languages, frameworks, and technologies. Mastering this art requires a blend of technical skills, problem-solving abilities, and a continuous thirst for learning. Let’s delve into the key components and strategies for becoming a proficient Full Stack Developer.

1. Understanding Front-end Technologies

Mastering front-end development involves proficiency in languages like HTML, CSS, and JavaScript, along with knowledge of frameworks such as React, Angular, or Vue.js. Understanding user interface (UI) principles, responsive design, and cross-browser compatibility are essential for creating engaging and accessible web applications.

2. Navigating Back-end Technologies

On the back end, developers work with server-side languages like Node.js, Python, Ruby, or PHP, along with frameworks like Express.js, Django, Ruby on Rails, or Laravel. They manage databases, handle server logic, and ensure smooth communication between the server and client-side components. Additionally, knowledge of RESTful APIs, authentication mechanisms, and security protocols is crucial for building robust back-end systems.

3. Database Management

Proficiency in database management is fundamental for Full Stack Developers. They should be familiar with relational databases like MySQL, PostgreSQL, or SQL Server, as well as NoSQL databases like MongoDB or Firebase. Understanding data modeling, querying, indexing, and database optimization techniques is essential for building scalable and efficient applications.

4. Version Control and DevOps

Full Stack Developers should be proficient in version control systems like Git, enabling collaborative development, code management, and tracking changes over time. Moreover, familiarity with DevOps practices, continuous integration/continuous deployment (CI/CD) pipelines, and cloud platforms like AWS, Azure, or Google Cloud facilitates seamless deployment and scaling of applications.

5. Continuous Learning and Adaptation

Technology is constantly evolving, so Full Stack Developers must embrace a mindset of continuous learning and adaptation. Staying updated with the latest trends, tools, and best practices through online courses, workshops, and community involvement is essential for staying competitive in the field. Additionally, actively participating in open-source projects, attending meetups, and collaborating with peers can enhance skills and broaden horizons.

Conclusion

Becoming a masterful Full Stack Developer requires dedication, perseverance, and a holistic understanding of web development principles. By honing skills in front-end and back-end technologies, mastering database management, embracing version control and DevOps practices, and committing to lifelong learning, developers can excel in this multifaceted role. With the demand for Full Stack Developers on the rise, mastering this art opens doors to exciting career opportunities and enables individuals to make significant contributions to the ever-evolving world of technology.

[iwebscrapingblogs]

How to Scrape Google Reviews Using Google Maps API & Python?

In the digital age, online reviews play a pivotal role in shaping consumer decisions. For businesses, understanding customer sentiment on platforms like Google Reviews is crucial. Harnessing the power of Python and the Google Maps API, we can automate the process of scraping Google Reviews to gain valuable insights. In this blog, we'll walk you through the steps to scrape Google Reviews efficiently.

Step 1: Set Up Your Google Cloud Platform (GCP) Account

Before diving into the code, you need to set up a Google Cloud Platform (GCP) account and create a new project. Enable the Google Maps JavaScript API and obtain an API key. This key acts as your passport to access Google Maps services.

Step 2: Install Required Libraries

Fire up your Python environment and install the necessary libraries. Use the following commands to install googlemaps and pandas:pip install googlemaps pip install pandas

These libraries will help you interact with the Google Maps API and manage data efficiently.

Step 3: Write the Python Script

Create a new Python script and import the required libraries. Initialize the Google Maps API client with your API key.import googlemaps import pandas as pd api_key = 'YOUR_API_KEY' gmaps = googlemaps.Client(key=api_key)

Step 4: Retrieve Place Details

Choose the location for which you want to scrape reviews. You'll need the place ID, which you can obtain using the places API.place_id = 'YOUR_PLACE_ID' place_details = gmaps.place(place_id=place_id, fields=['name', 'rating', 'reviews'])

Step 5: Extract and Store Reviews

Now, you can extract reviews from the obtained place details and store them in a pandas DataFrame for further analysis.reviews = place_details['reviews'] df_reviews = pd.DataFrame(reviews) df_reviews.to_csv('google_reviews.csv', index=False)

This snippet saves the reviews in a CSV file for easy access and sharing.

Step 6: Analyze and Visualize

With your reviews in hand, you can perform sentiment analysis, aggregate ratings, or visualize the data. Utilize Python's data manipulation and visualization tools to gain insights into customer sentiments.# Example: Calculate average rating average_rating = df_reviews['rating'].mean() print(f'Average Rating: {average_rating}')

Step 7: Respect Terms of Service

While scraping Google Reviews is powerful, it's crucial to respect Google's Terms of Service. Ensure that your usage complies with the policies to avoid any legal repercussions.

Conclusion

Scraping Google Reviews using the Google Maps API and Python opens up a world of possibilities for businesses and researchers. From understanding customer sentiments to making data-driven decisions, the insights gained can be invaluable. By following the steps outlined in this guide, you can embark on a journey of automating the extraction and analysis of Google Reviews, putting the power of Python and the Google Maps API to work for you.

Remember, ethical use and compliance with terms of service are paramount in the world of web scraping. Happy coding!

[digitalcreationsllc]

Python Package Index Faces Security Crisis With Validated Leaks

Security researchers have discovered a total of 3938 unique secrets on PyPI, the official third-party package management system for the Python community, across all projects, with 768 of them validated as authentic.  Notably, 2922 projects contained at least one unique secret. Among the leaked secrets were various credentials, including AWS Keys, Redis credentials, Google API keys and various…

[exactlyautomaticcollective]

Mastering Data Engineering

In the era of big data, organizations are increasingly recognizing the critical role of data engineering in enabling data-driven decision-making. Data engineers are in high demand as businesses seek professionals with the skills to design, build, and manage the infrastructure and processes that support data analytics. In this article, we provide a comprehensive guide to understanding the role of a data engineer, their responsibilities, required skills, and the steps to embark on a rewarding career in this field.

1. Defining the Role of a Data Engineer:

A data engineer is a technical professional responsible for the design, development, and maintenance of data systems that facilitate the collection, storage, and analysis of large volumes of data. They collaborate closely with data scientists, analysts, and stakeholders to ensure data availability, reliability, and accessibility. Data engineer training is essential for professionals seeking to acquire the necessary skills and knowledge to design and develop efficient data pipelines, data warehouses, and data lakes.

2. Key Responsibilities of a Data Engineer:

Data engineers have a wide range of responsibilities, including:

- Data Integration: Data engineers integrate data from multiple sources, including databases, APIs, and streaming platforms, into a unified and usable format.

- Data Transformation: Data engineer courses provide individuals with the opportunity to gain expertise in data cleansing, validation, and transformation techniques, including ETL processes and handling diverse data formats.

- Database Design: Data engineers design and optimize database schemas, choosing the appropriate data storage solutions such as relational databases, NoSQL databases, or distributed file systems like Hadoop.

- Data Pipeline Development: They build and maintain data pipelines that automate the movement of data from source to destination, ensuring data is processed, transformed, and loaded efficiently.

- Performance Optimization: Data engineers optimize data processing performance by fine-tuning queries, implementing indexing strategies, and leveraging parallel computing frameworks like Apache Spark.

- Data Governance and Security: They establish data governance policies, implement access controls, and ensure data security and compliance with regulations like GDPR or HIPAA.

3. Essential Skills for Data Engineers:

To excel as a data engineer, proficiency in the following skills is crucial:

- Programming Languages: Strong programming skills in languages such as Python, Java, or Scala are essential for data engineering tasks, including data manipulation, scripting, and automation.

- SQL and Database Management: Proficiency in SQL, as well as data engineer certification, is necessary for querying and managing relational databases. Understanding database concepts, optimization techniques, and query performance tuning is also important.

- Big Data Technologies: Familiarity with big data frameworks like Apache Hadoop, Apache Spark, or Apache Kafka enables data engineers to handle large-scale data processing and streaming.

- Data Modeling and Warehousing: Knowledge of data modeling techniques, dimensional modeling, and experience with data warehousing solutions such as Snowflake or Amazon Redshift, in a data engineer institute are valuable to earn skills for data engineers.

- Cloud Computing: Proficiency in cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) is increasingly important as organizations adopt cloud-based data infrastructure.

4. Educational Path and Career Development:

Data engineering roles typically require a strong educational background in computer science, data science, or a related field. A bachelor's or master's degree in a relevant discipline provides a solid foundation. Pursuing certifications in data engineering or cloud platforms, along with data engineer training courses, can enhance job prospects and demonstrate expertise in the field. Continuous learning through online courses, workshops, and industry conferences is crucial to staying updated with evolving technologies and best practices.  

5. Industry Demand and Career Opportunities:

The demand for skilled data engineers is rapidly growing across industries. Organizations are seeking professionals who can help them leverage the power of data for insights and competitive advantage. Data engineers can find opportunities in various sectors, including technology, finance, healthcare, e

-commerce, and consulting. As organizations invest more in data-driven strategies, the career prospects for data engineers are promising, with potential for growth into leadership or specialized roles such as data architect or data engineering manager.

Refer this article: How much is the Data Engineer Course Fee in India?

End Note:

In an era driven by data, the role of a data engineer is indispensable for organizations aiming to harness the power of their data assets. With a strong foundation in programming, database management, big data technologies, and cloud computing, data engineers have the potential to shape the future of businesses. By embracing continuous learning, staying updated with emerging technologies, and honing their skills, aspiring data engineers can embark on a rewarding career at the forefront of the data revolution.

Certified Data Engineer Course

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Data Engineering Fundamentals Every Data Engineer Should Know

Data engineering is essential for modern data-driven organizations. A data engineer’s expertise in collecting, transforming, and preparing data is fundamental to extracting meaningful insights and driving strategic initiatives. Data engineering is a field that is constantly evolving, and it is important to stay up-to-date on the latest trends and technologies. In this article, we delve into the foundational concepts that every data engineer should be well-versed in.

1. Data Pipeline Architecture

At the heart of data engineering lies the design and construction of data pipelines. These pipelines serve as pathways for data to flow from various sources to destinations, often involving extraction, transformation, and loading (ETL) processes. Understanding different pipeline architectures, such as batch processing and real-time streaming, is essential for efficiently handling data at scale.

2. Big Data Foundations: SQL and NoSQL Databases

Data engineers should be familiar with both relational and NoSQL databases. Relational databases offer structured storage and support for complex queries, while NoSQL databases provide flexibility for unstructured or semi-structured data. Mastering database design, indexing, and optimization techniques is crucial for managing data effectively.

3. Python for Data Engineering

Python’s extensive libraries and packages make it a powerful tool for data engineering tasks. From data manipulation and transformation to connecting with APIs and databases, Python’s flexibility allows data engineers to perform a variety of tasks using a single programming language. Python is a powerful language for data engineering, with capabilities for automation, integration, exploration, visualization, API interaction, error handling, and community support.

4. Data Transformation

Raw data often requires cleaning and transformation to be useful. Data engineers should be skilled in data transformation techniques, including data normalization, aggregation, and enrichment. Proficiency in tools like Apache Spark or SQL for data manipulation is a fundamental aspect of this process.

5. Cloud Services: AWS Certified Data Analytics Specialty

As organizations shift towards cloud computing, data engineers must be well-versed in cloud services. Familiarity with platforms like AWS, Google Cloud, or Azure is essential for building scalable and cost-effective data solutions. Understanding how to set up and manage cloud-based data storage, computing, and processing is a key skill.

Become an AWS data analytics expert with Datavalley’s comprehensive course. Learn data collection, storage, processing, and pipelines with Amazon S3, Redshift, AWS Glue, QuickSight, SageMaker, and Kinesis. Prepare for the certification exam and unlock new career possibilities.

6. Data Modeling

Data modeling involves designing the structure of databases to ensure data integrity and efficient querying. Data engineers should be comfortable with conceptual, logical, and physical data modeling techniques. Properly designed data models facilitate optimized storage and retrieval of information.

7. Distributed Data Processing

In the age of big data, distributed data processing frameworks like Hadoop and Spark are essential tools for data engineers. Learning how to use these frameworks allows you to process large datasets in parallel efficiently. Learn distributed data processing with Big Data Hadoop, HDFS, Apache Spark, PySpark, and Hive. Gain hands-on experience with the Hadoop ecosystem to tackle big data challenges.

8. Data Quality and Validation

Ensuring data quality is paramount. Data engineers should know how to implement data validation checks to identify and rectify anomalies or errors. Proficiency in data profiling, outlier detection, and data cleansing techniques contributes to accurate and reliable analysis.

9. Version Control and Collaboration

Data engineering often involves collaboration within teams. Understanding version control systems like Git ensures efficient collaboration, code management, and tracking of changes. This is crucial for maintaining the integrity of data engineering projects.

10. Data Lake Table Format Framework

Data lakes are becoming increasingly prevalent. Exploring the table format framework within data lakes allows data engineers to efficiently organize and manage vast amounts of diverse data. Learn about Delta Lake and Hudi for data lake management. Delta Lake provides data consistency, reliability, and versioning. Hudi offers stream processing and efficient data ingestion. Work on real-world projects to elevate your expertise.

11. Scalability and Performance

Scalability is a core consideration in data engineering. Data engineers should comprehend techniques for horizontal and vertical scaling to handle growing data volumes. Optimizing query performance and database indexing contribute to efficient data processing.

12. Security and Compliance

Data security and compliance are paramount in data engineering. Data engineers should be well-versed in encryption, access control, and compliance regulations such as GDPR. Implementing robust security measures safeguards sensitive data.

Conclusion

In conclusion, every data engineer should have a thorough understanding of these fundamental concepts. Data professionals need expertise in specialized topics and DevOps principles to navigate data complexities, lead organizations to data-driven excellence, and remain at the forefront of innovation.

Data engineers can utilize their skills in creating efficient data pipelines and ensuring data quality and security to unlock the full potential of data for insights that drive organizational growth. Data engineers need to master essential skills to stay ahead of the data landscape and drive transformative insights.

Become a Data Engineer

Datavalley’s Big Data Engineer Masters Program helps you develop the skills necessary to become an expert in data engineering. It offers comprehensive knowledge in Big Data, SQL, NoSQL, Linux, and Git. The program provides hands-on training in big data processing with Hadoop, Spark, and AWS tools like Lambda, EMR, Kinesis, Athena, Glue, and Redshift. You will gain in-depth knowledge of data lake storage frameworks like Delta Lake and Hudi. Work on individual projects designed to equip the learners with hands-on experience. By the end of this course, you will have the skills and knowledge necessary to design and implement scalable data engineering pipelines on AWS using a range of services and tools.

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Python in Data Engineering: Powering Your Data Processes

Python is a globally recognized programming language, consistently ranking high in various surveys. For instance, it bagged the first position in the Popularity of Programming Language index and secured the second spot in the TIOBE index. Moreover, the Stack Overflow survey for 2021 saw Python as the most sought-after and third most adored programming language.

Predominantly regarded as the language of choice for data scientists, Python has also made significant strides in data engineering, becoming a critical tool in the field.

Data Engineering in the Cloud

Data engineers and data scientists often encounter similar challenges, particularly concerning data processing. However, in the realm of data engineering, our primary focus is on robust, reliable, and efficient industrial processes like data pipelines and ETL (Extract-Transform-Load) jobs, irrespective of whether the solution is for on-premise or cloud platforms.

Python has showcased its suitability for cloud environments, prompting cloud service providers to integrate Python for controlling and implementing their services. Major players in the cloud arena like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure have incorporated Python solutions in their services to address various problems.

In the serverless computing domain, Python is one of the few programming languages supported by AWS Lambda Functions, GCP Cloud Functions, and Azure Functions. These services enable on-demand triggering of data ETL processes without the need for a perpetually running server.

For big data problems where ETL jobs require heavy processing, parallel computing becomes essential. Python wrapper for the Spark engine, PySpark, is supported by AWS Elastic MapReduce (EMR), GCP's Dataproc, and Azure's HDInsight.

Each of these platforms offers APIs, which are critical for programmatic data retrieval or job triggering, and these are conveniently wrapped in Python SDKs like boto for AWS, google_cloud_* for GCP, and azure-sdk-for-python for Azure.

Python's Role in Data Ingestion

Business data can come from various sources like SQL and noSQL databases, flat files like CSVs, spreadsheets, external systems, APIs, and web documents. Python's popularity has led to the development of numerous libraries and modules for accessing these data, such as SQLAlchemy for SQL databases, Scrapy, Beautiful Soup, and Requests for web-originated data, and many more.

A noteworthy library is Pandas, which facilitates reading data into "DataFrames" from various formats, including CSVs, TSVs, JSON, XML, HTML, LaTeX, SQL, Microsoft, and open spreadsheets, and other binary formats.

Parallel Computing with PySpark

Apache Spark, an open-source engine for processing large volumes of data, leverages parallel computing principles in a highly efficient and fault-tolerant manner. PySpark, a Python interface for Spark, is extensively used and offers a straightforward way to develop ETL jobs for those familiar with Pandas.

Job Scheduling with Apache Airflow

Cloud platforms have commercialized popular Python-based tools as "managed" services for easier setup and operation. One such example is Amazon's Managed Workflows for Apache Airflow. Apache Airflow, written in Python, is an open-source workflow management platform, allowing you to author and schedule workflow processing sequences programmatically.

Conclusion

Python plays a significant role in data engineering and is an indispensable tool for any data engineer. With its ability to implement and control most relevant technologies and processes, Python has been a natural choice for Mindfire Solutions, allowing us to offer data engineering services and web development solutions in Python. If you're looking for data engineering services, please feel free to contact us at Mindfire Solutions. We're always ready to discuss your needs and find out how we can assist you in meeting your business goals.