#file system vs dbms

[rishabhtpt]

Why Choose a DBMS Over a Traditional File System?

Remember the early days of personal computing? Perhaps you recall organizing your digital life by creating folders for "Documents," "Photos," and "Spreadsheets." Inside these folders, you meticulously saved individual files like "Budget_2023.xlsx" or "CustomerList.txt." This approach, relying on a traditional file system, felt intuitive and effective for managing a small, personal collection of information. But what happens when your data grows from a handful of files to millions, shared by hundreds of users across multiple applications? This is where the fundamental differences between a simple file system and a robust Database Management System (DBMS) become glaringly apparent.

While a file system excels at storing individual files in a hierarchical structure, much like filing cabinets in an office, it quickly buckles under the demands of modern, complex data management. For businesses, organizations, or even advanced personal projects, the limitations of a file system can quickly lead to a data management nightmare. This begs the crucial question: Why is a DBMS almost always the superior choice for handling anything beyond the most basic data storage needs?

The Traditional File System: A Trip Down Memory Lane

Before the widespread adoption of DBMS, many organizations managed their data using collections of application programs that each processed their own separate files. Imagine a sales department having its customer list in one spreadsheet, while the marketing department maintains another, slightly different list in a text file. Each program would have its own set of files defined and managed independently.

This seemed straightforward at first. You'd have a file for customer names, another for orders, and perhaps another for product inventories. For simple, isolated tasks, it got the job done. However, as businesses grew and data became interconnected, this approach started to creak under the pressure, exposing numerous inherent weaknesses.

The Growing Pains: Where File Systems Fall Short

Using traditional file systems for anything beyond simple, isolated data management quickly introduces a host of problems:

Data Redundancy and Inconsistency: When the same data (e.g., customer address) is stored in multiple files, it leads to redundancy. If a customer moves, their address needs to be updated in every file. Forgetting even one update leads to inconsistent data – a prime source of error and confusion.

Difficulty in Data Access: Want to find all customers who bought product X and live in city Y? In a file system, you'd likely have to write a custom program to search through multiple files, extract information, and then piece it together. This is cumbersome, inefficient, and prone to errors. There's no standardized way to query across disparate files.

Lack of Data Sharing: It's hard for multiple users or applications to access and modify the same data concurrently without conflicts. Imagine two sales reps trying to update the same customer record simultaneously in a text file – chaos would ensue, potentially leading to data corruption or lost updates.

Data Dependence: Applications are heavily tied to the specific file formats. If the format of a customer file changes (e.g., adding a new column for email address), every application that uses that file needs to be modified and recompiled. This makes system evolution incredibly rigid and expensive.

No Built-in Data Integrity and Security: File systems offer very limited mechanisms for enforcing data rules. There's nothing to stop you from entering "XYZ" into a numeric "Price" field, or deleting a customer record even if there are active orders associated with them. Security often relies on operating system permissions (read/write/execute), which are too coarse-grained for controlling access to specific pieces of data.

Concurrency Issues: When multiple users or processes try to read from and write to the same files simultaneously, it can lead to race conditions, data corruption, and deadlock situations. File systems offer basic locking mechanisms, but these are insufficient for complex, multi-user environments.

Recovery Problems: What happens if your system crashes in the middle of an update? In a file system, you might end up with partial updates, corrupted files, or lost data. Recovering to a consistent state after a system failure is extremely difficult and often requires manual intervention.

No Atomicity: Transactions (a sequence of operations treated as a single logical unit) are not atomic. This means that if an operation fails mid-way, the entire set of operations is not guaranteed to be rolled back or fully completed, leaving the data in an inconsistent state.

Enter the DBMS: The Modern Data Powerhouse

This is where the power of a DBMS shines through. A DBMS is a sophisticated software system designed specifically to manage and organize large volumes of data efficiently and securely. It addresses all the shortcomings of traditional file systems by providing a structured, controlled, and powerful environment for data management:

Reduced Redundancy and Improved Consistency: A DBMS centrally manages data, reducing redundancy by allowing data to be stored once and referenced multiple times. Built-in constraints (like primary and foreign keys, as discussed in our previous blog) ensure data consistency by enforcing rules across related tables.

Efficient Data Access: DBMS provides powerful query languages (like SQL - Structured Query Language) that allow users and applications to retrieve, manipulate, and analyze data quickly and flexibly, without needing to write custom programs for every query. Indexing further speeds up retrieval.

Enhanced Data Sharing and Concurrency Control: A DBMS is designed for multi-user access. It employs sophisticated concurrency control mechanisms (like locking and transaction management) to ensure that multiple users can access and modify data concurrently without conflicts or data corruption.

Data Independence: DBMS provides multiple levels of data abstraction, meaning changes to the physical storage structure (e.g., moving data to a different disk, changing indexing) do not necessarily require changes to the applications that access the data. This makes systems far more adaptable.

Robust Data Integrity and Security: Beyond physical security, a DBMS offers fine-grained access control, allowing administrators to define who can access what data and perform specific operations. It also enforces data integrity rules through various constraints, ensuring data quality at the database level.

Automatic Recovery and Backup: DBMS includes built-in mechanisms for logging transactions, performing backups, and automatically recovering the database to a consistent state after a system failure, minimizing data loss and downtime.

Atomicity of Transactions (ACID Properties): DBMS supports ACID properties (Atomicity, Consistency, Isolation, Durability) for transactions. This guarantees that all operations within a transaction are either fully completed or entirely rolled back, ensuring data integrity even in the face of errors or system failures.

Scalability and Performance: DBMS are engineered to handle enormous volumes of data and high user traffic, offering optimized performance for complex queries and transactions, making them suitable for enterprise-level applications.

File System vs. DBMS: A Clear Winner for Complex Data

In the direct comparison of file system vs. DBMS, the latter clearly emerges as the superior choice for almost any real-world scenario involving interconnected, shared, and frequently updated data. While a traditional file system remains perfectly adequate for storing isolated personal documents or simple software configurations, it simply lacks the sophisticated features necessary for managing relational data, ensuring integrity, providing concurrent access, and handling failures gracefully.

[dotnettrickstraining]

DBMS vs RDBMS Which One Should You Learn for a Successful Database Career?

In the fast-evolving world of IT, databases are the backbone of every application. Whether it's managing customer information for an e-commerce platform or storing transaction details for a bank, databases are everywhere. If you’re aiming for a career in database management, you’ve likely encountered terms like DBMS and RDBMS. But what exactly are they, and which one should you focus on to build a successful career?

Understanding the difference between DBMS and RDBMS is crucial for making the right choice. While DBMS lays the foundation for database management, RDBMS takes it to the next level, handling complex relationships between data. If you’ve faced DBMS interview questions, you know the importance of grasping these concepts to excel in the job market.

In this blog, we’ll break down DBMS and RDBMS, explore their key features, and help you decide which technology aligns better with your career goals.

2. What is DBMS?

DBMS, or Database Management System, is a software tool that helps users store, retrieve, and manage data efficiently. It’s the most basic form of database technology, ideal for small-scale applications where data relationships are not complex.

Key Features of DBMS:

Data is stored in files or collections without enforcing strict relationships.

Limited support for data consistency and integrity.

Focuses on storing and retrieving data without advanced functionalities.

Examples of DBMS:

Microsoft Access.

XML Databases.

File-based systems.

Primary Use Cases: DBMS is perfect for small-scale systems like personal data management or lightweight applications with simple data requirements.

Imagine you’re managing a local library’s book inventory. A DBMS can store details like book titles, authors, and availability, but it doesn’t establish relationships between them, such as which books are loaned to which members.

While DBMS provides an excellent starting point for database management, its simplicity can become a limitation as your application scales or requires more complex data handling.

3. What is RDBMS?

RDBMS, or Relational Database Management System, is an advanced version of DBMS where data is stored in a tabular format with rows and columns. What sets RDBMS apart is its ability to enforce relationships between data, ensuring consistency and integrity.

Key Features of RDBMS:

Follows a structured approach using tables to store data.

Supports SQL (Structured Query Language) for querying and managing data.

Enforces data integrity through primary keys, foreign keys, and constraints.

Adheres to ACID properties (Atomicity, Consistency, Isolation, Durability) for reliable transactions.

Examples of RDBMS:

MySQL.

PostgreSQL.

Microsoft SQL Server.

Oracle Database.

Primary Use Cases: RDBMS is ideal for large-scale applications requiring complex data relationships, such as e-commerce systems, banking solutions, and healthcare databases.

Let’s revisit the library example, but this time with RDBMS. Using RDBMS, you can store books in one table, members in another, and use relationships to track which member has borrowed which book. This ensures data consistency, accuracy, and easier management as the library grows.

[database-integration]

Database vs. Application Migration: Which Data Migration Service Fits Your Needs?

In the world of IT, the idea of data migration is an essential yet sometimes overwhelming process. Whether you're migrating from one database to another, or moving applications between platforms, understanding the key differences between database and application migration can save you time, money, and unnecessary stress.

Choosing the right data migration service will depend on your unique needs, and in this article, we’ll dive into the crucial factors that make these processes distinct.

As the digital landscape evolves, data migration becomes a crucial part of your IT strategy. Let’s explore which data migration service is right for your business, and how a data migration expert can make the transition seamless and secure. We'll also introduce Augmented Systems, a renowned data migration services company based in the USA and India, known for their exceptional service and experience in this field.

What Is Data Migration?

Data migration refers to the process of transferring data from one system to another. This is often done when businesses need to upgrade their systems, move to the cloud, or consolidate multiple data sources into a single repository. It’s essentially the process of ensuring that your valuable data continues to flow smoothly between platforms or databases, without compromising its integrity or accessibility.

Understanding the Data Migration Process

The data migration process generally involves several steps:

Assessment – Understanding the scope of the migration, what data needs to be moved, and how it will be mapped to the new system.

Planning – Creating a strategy to move the data, ensuring minimal disruption to daily operations.

Execution – The actual movement of data, often involving a migration software or tool.

Testing – Ensuring that the data has been correctly transferred and is fully functional in the new environment.

Go-Live – Making the new system operational and integrating it into your business processes.

Why Do Businesses Need Data Migration?

Why is data migration necessary for businesses? Well, data migration isn’t just about moving data from one place to another—it’s about enabling businesses to upgrade their infrastructure, improve system performance, and increase efficiency. Whether it’s shifting to cloud services for scalability or upgrading outdated databases, data migration allows businesses to keep up with technological advancements and avoid system bottlenecks.

Database Migration Explained

Database migration focuses specifically on moving data from one database management system (DBMS) to another. This process involves extracting, transforming, and loading (ETL) the data into a new structure. In simple terms, it’s like moving your valuable data from an old filing cabinet into a more organized, efficient one.

However, database migration isn’t just about transferring raw data. It includes ensuring that all the necessary relationships, indexes, and database structures are preserved so the new system operates smoothly. It also involves ensuring that the data remains intact, accurate, and accessible during the transition.

Application Migration Explained

On the other hand, application migration involves moving entire applications or systems, including the associated data, to a new environment. This could mean moving an application from on-premises infrastructure to the cloud, or shifting from one operating system to another.

Application migration is often more complex than database migration because it requires the seamless integration of both data and the software’s functionality in the new environment. This means ensuring that applications perform as expected once migrated, including any necessary code updates or configurations.

Key Differences Between Database and Application Migration

While both database and application migrations serve similar goals—moving data and systems to a better environment—there are significant differences:

Scope: Database migration focuses on the data itself, while application migration deals with both the data and the software that processes it.

Complexity: Application migration is generally more complex because it involves moving entire systems, not just data.

Tools and Expertise: A data migration expert may use different tools for database and application migration, with the latter often requiring more specialized knowledge in terms of application architecture and code.

Choosing the Right Migration Service for Your Needs

When deciding between data migration services, it’s important to evaluate your specific needs. Are you moving just the data, or are you also relocating the applications that run on that data? Each scenario will require a different approach.

For database migration, you’ll need a service with expertise in data migration software and tools that can handle large volumes of data. For application migration, you’ll need experts who understand the complexities of moving entire systems, including the data.

How a Data Migration Software Helps?

Data migration software plays a key role in automating and simplifying the migration process. These tools help reduce manual intervention, minimize errors, and accelerate the overall process. Some migration software is specifically designed for database migration, while others are built to handle full-scale application migrations.

Using the right migration software can significantly improve the success rate of your migration project, ensuring that your data remains safe, intact, and accessible in the new system.

The Role of a Data Migration Expert

A data migration expert brings valuable experience and insights into the migration process. These professionals are skilled in troubleshooting, risk management, and ensuring a smooth transition. Their expertise is crucial in identifying potential issues before they arise and ensuring that the migration happens with minimal disruption to your business operations.

Risks and Challenges in Data Migration

Data migration may seem like a straightforward process, but it comes with its challenges:

Data loss: If the migration is not planned or executed properly, important data may be lost.

Downtime: Some migrations require temporary downtime, which can impact business productivity.

Compatibility issues: The new system might not be fully compatible with the old data or applications, causing delays.

Understanding these risks is crucial, as it allows businesses to take proactive steps to mitigate them, often with the help of a data migration services company.

The Benefits of Working with a Data Migration Services Company

Working with a data migration services company can help streamline the process. These companies bring experience, expertise, and tools to ensure the migration is carried out efficiently and securely. They can also help you plan the migration, test the new system, and manage any post-migration issues that may arise.

If you're looking for a trusted partner in data migration, consider Augmented Systems—a leader in the field with extensive experience in handling both database and application migrations.

Why You Should Consider Augmented Systems for Data Migration

Augmented Systems is a leading data migration services company with a reputation for delivering high-quality migration solutions across the USA and India. With a team of seasoned data migration experts, they have successfully helped businesses move data and applications with minimal disruption. Their expertise in both database and application migration, along with their customized approach, makes them a go-to choice for any business looking to make the move to a new platform.

Best Practices for a Smooth Migration

To ensure your migration is successful, follow these best practices:

Plan thoroughly: Understand your needs and map out the entire process.

Test thoroughly: Always test the new system before going live.

Backup your data: Ensure you have a backup plan in case something goes wrong.

Work with experts: Hire a data migration expert to guide the process.

The Future of Data Migration

As businesses increasingly move to cloud-based systems, the future of data migration will involve more automation and smarter tools. Cloud migration, hybrid systems, and AI-driven migration tools will continue to evolve, making the process faster, safer, and more efficient.

Conclusion and Key Takeaways

Choosing the right migration service—whether for databases or applications—is crucial for your business's digital transformation. With a well-planned migration process and the right tools, you can ensure a smooth transition with minimal disruption. Consider working with a trusted data migration services company like Augmented Systems to ensure your migration is handled professionally and efficiently.

[enterprisewired]

Structured vs. Unstructured Data: What’s The Difference?

Businesses and organizations generate and analyze vast amounts of information. This information can be broadly classified into two categories: structured vs. unstructured data. Understanding the differences between these types of data and their respective applications is crucial for effective data management and analysis. This article explores the characteristics, benefits, challenges, and use cases of structured and unstructured data.

What is Structured Data?

Structured data is organized and formatted in a way that makes it easily searchable and analyzable by computers. This type of data is typically stored in databases and spreadsheets, where it can be systematically arranged in rows and columns. Examples of structured data include:

Relational Databases: Customer information, transaction records, and product inventories.

Spreadsheets: Financial data, sales figures, and employee details.

Characteristics of Structured Data

Organized Format: Structured data is highly organized, usually in tables with defined columns and rows.

Easily Searchable: Due to its organization, structured data can be easily queried and retrieved using database management systems (DBMS).

Fixed Schema: Structured data follows a predetermined schema, meaning the data types and relationships are defined in advance.

Quantitative: Structured data is often numerical or categorical, allowing for straightforward statistical analysis.

Benefits of Structured Data

Ease of Analysis: Structured data can be easily analyzed using SQL queries, data mining tools, and business intelligence software.

Efficiency: The organized nature of structured data allows for efficient storage, retrieval, and management.

Accuracy: Structured data tends to have high accuracy and consistency due to its adherence to a defined schema.

Challenges of Structured Data

Rigidity: The fixed schema of structured data makes it less flexible when dealing with changes or additions to the data structure.

Limited Scope: Structured data is often limited to numerical and categorical information, excluding more complex data types like text, images, and videos.

What is Unstructured Data?

Unstructured data lacks a predefined format or organization, making it more complex to process and analyze. This type of data is generated in a variety of formats, including text, images, videos, and audio files. Examples of unstructured data include:

Text Documents: Emails, social media posts, and web pages.

Multimedia: Images, videos, and audio recordings.

Sensor Data: Data from IoT devices, such as temperature readings and GPS coordinates.

Characteristics of Unstructured Data

Lack of Structure: Unstructured data does not follow a specific format or organization.

Diverse Formats: It can exist in various formats, including text, images, audio, and video.

Qualitative: Unstructured data is often qualitative, containing rich information that requires advanced techniques to analyze.

Benefits of Unstructured Data

Rich Information: Unstructured data provides a wealth of information that can offer deep insights into behaviors, trends, and patterns.

Flexibility: It can capture complex and diverse data types, making it suitable for a wide range of applications.

Real-World Relevance: Much of the data generated in the real world is unstructured, making it highly relevant for many use cases.

Challenges of Unstructured Data

Complexity: Analyzing unstructured data requires advanced techniques such as natural language processing (NLP), image recognition, and machine learning.

Storage and Management: Unstructured data requires more storage space and sophisticated management systems compared to structured data.

Searchability: Retrieving specific information from unstructured data can be challenging without proper indexing and search algorithms.

Applications of Structured and Unstructured Data

Structured Data Applications

Business Intelligence: Structured data is the backbone of business intelligence systems, providing actionable insights through data analytics and reporting.

Financial Analysis: Financial institutions use structured data for analyzing transactions, risk assessment, and regulatory compliance.

Customer Relationship Management (CRM): Structured data helps businesses manage customer information, track interactions, and improve customer service.

Unstructured Data Applications

Sentiment Analysis: Companies analyze social media posts, reviews, and other text data to understand customer sentiment and improve products or services.

Multimedia Content Analysis: Unstructured data from images and videos is used in facial recognition, video surveillance, and content recommendation systems.

IoT and Sensor Data: Unstructured data from IoT devices is used for predictive maintenance, smart city applications, and environmental monitoring.

Integrating Structured vs. Unstructured Data

To fully leverage the power of both structured and unstructured data, businesses are increasingly adopting hybrid approaches that combine these data types. Techniques such as data warehousing, data lakes, and big data platforms enable the integration and analysis of diverse data sources. Here are some ways to achieve this integration:

Data Lakes: Data lakes store structured and unstructured data in its raw form, allowing for flexible analysis and processing using big data technologies.

Data Warehousing: Traditional data warehouses can be extended to incorporate unstructured data by using data transformation and integration tools.

Big Data Analytics: Platforms like Hadoop and Spark provide the infrastructure to process and analyze large volumes of structured and unstructured data.

Conclusion

Understanding the differences between structured vs. unstructured data is crucial for effective data management and analysis. While structured data offers ease of analysis and efficiency, unstructured data provides rich, qualitative insights that can drive innovation and competitive advantage.

By integrating and leveraging both types of data, businesses can unlock new opportunities, enhance decision-making, and achieve a comprehensive view of their operations and market dynamics. As technology continues to evolve, the ability to manage and analyze structured and unstructured data will become increasingly important for organizations aiming to thrive in the data-driven era.

[fortunatelycoldengineer]

What is Data?

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for more information and tutorial

check the above link

[ugcnetprep]

UGC NET Computer Science | RDBMS vs DBMS vs File System | Puneet Mam | N...

[mmorgcam]

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[nehaarticles]

[rishabhtpt]

https://australianewsnetwork.com/dbms-vs-file-system-a-detailed-showdown/

A file system manages individual files in a hierarchical structure, focusing on storage and retrieval. In contrast, a DBMS is a software system managing structured data in databases, offering features like data integrity, security, and efficient querying through languages like SQL. DBMS excels in handling complex relationships and large datasets, while file systems are simpler for basic file organization.

[fortunatelycoldengineer]

[electrogeekarg]

¿Cuales son las diferencias entre ESP32 y ESP8266?

Veamos cuál es la diferencia entre los chips ESP32 y ESP8266. Tanto el ESP32 como el ESP8266 son SOC (Systems on Chip) basados en WiFi. Ambos tienen un procesador de 32 bits, el ESP32 es un CPU de doble núcleo de 80Mhz a 240MHz y el ESP8266 es un procesador de núcleo único de 160MHz. A continuación vamos a compararlos en profundidad.

Comparativa según características

Característica ESP32 ESP8266 Módulo CPU Xtensa Dual-Core 32-bit LX6 con 600 DMIPS Xtensa Single-core 32-bit L106 Velocidad del WiFi 802.11n hasta 150 Mbps Hasta 72,2 Mbps Protocolo WiFi 802,11 b/g/n (2,4 Ghz) 802,11 b/g/n (2,4 Ghz) GPIO 36 17 Bluetooth SÍ NO DAC Dos canales DAC de 8 bits NO ADC SAR de 12 bits SAR de 10 bits Canales ADC 8 Canales Un solo canal Referencia del CAD V 1100mV 1100mV SPI/I2C/I2S/UART 4/2/2/3 2/1/2/2 Modos WiFi Station/SoftAP/SoftAP+Station/P2P Station/SoftAP/SoftAP+Station/P2P Sensor táctil SÍ (8-Canales) NO Sensor de temperatura SÍ NO Sensor de efecto Hall SÍ NO SRAM 520 kB (8 kB de SRAM en RTC) Tamaño de la RAM < 50 kB FLASH (externo) 4Mbytes (también disponible más alto) 4Mbytes ROM 448 kB de ROM para el arranque y las funciones básicas No hay ROM programable Protocolos de red IPv4, IPv6, SSL, TCP/UDP/HTTP/FTP/MQTT IPv4, TCP/UDP/HTTP/MQTT Interfaz periférica UART/SDIO/SPI/I2C/I2S/IR Control RemotoGPIO/ADC/DAC/Touch/PWM/LED UART/SDIO/SPI/I2C/I2S/IR Control RemotoGPIO/ADC/PWM/LED Rango de temperatura de funcionamiento -40°C ~ +85°C -40°C ~ 125°C Tensión de funcionamiento 2.5V ~ 3.6V 2.5V ~ 3.6V Corriente operativa Promedio: 80 mA Valor medio: 80 mA Precio 4 € – 10 € €3 – €6

¿Qué es el módulo ESP32?

El ESP32 está altamente integrado con interruptores de antena incorporados, balun de RF, amplificador de potencia, amplificador de recepción de bajo ruido, filtros y módulos de gestión de potencia. El ESP32 añade una funcionalidad y versatilidad inestimables a sus aplicaciones con unos requisitos mínimos de placa de circuito impreso (PCB).

¿Qué es el tablero ESP32 devKit?

El DOIT Esp32 DevKit v1 es uno de los tableros de desarrollo creados por DOIT para evaluar el módulo ESP-WROOM-32. Se basa en el microcontrolador ESP32 que cuenta con Wifi, Bluetooth, Ethernet y soporte de bajo consumo, todo en un solo chip. ESP32 DevKit contiene ESP32-WROOM, fuente de alimentación de 3,3V necesaria para el ESP32 y un convertidor USB a serie para facilitar la programación. Con esta placa y un cable USB puedes empezar a desarrollar el ESP32.

¿Qué es el kit ESP8266 NodeMCU?

El kit de desarrollo basado en ESP8266, integra GPIO, PWM, IIC, 1-Wire y ADC, todo en un solo tablero. Potencie su desarrollo de la manera más rápida combinando con el firmware de NodeMCU.

Más GPIOs en el ESP32

El ESP32 tiene más GPIOs que el ESP8266, y puedes decidir qué pines son UART, I2C, SPI – sólo tienes que poner eso en el código. Esto es posible gracias a la función de multiplexación del chip ESP32 que permite asignar múltiples funciones al mismo pin.

PWM, ADC y más

Puedes configurar las señales PWM en cualquier GPIO con frecuencias configurables y el ciclo de trabajo fijado en el código.

Cuando se trata de los pines analógicos, estos son estáticos, pero el ESP32 soporta mediciones en 18 canales (pines habilitados para analógico) frente a un solo pin ADC de 10 bits en el ESP8266. El ESP32 también soporta dos canales DAC de 8 bits.

Además, el ESP32 contiene 10 GPIOs de detección capacitiva, que detectan el tacto y pueden ser usados para desencadenar eventos, o despertar al ESP32 del sueño profundo, por ejemplo.

Arduino IDE – ESP32 vs ESP8266

Hay muchas maneras de programar las placas ESP32 y ESP8266. Actualmente, ambas placas pueden ser programadas usando el entorno de programación IDE de Arduino.

Esto es algo bueno, especialmente para aquellos que están acostumbrados a programar el Arduino y están familiarizados con el “lenguaje de programación” de Arduino.

Empezar a usar el ESP32 o ESP8266 con el IDE de Arduino y tener tu primer proyecto en marcha es muy sencillo.

Aunque puedes programar ambas placas usando el IDE de Arduino, puede que no sean compatibles con las mismas bibliotecas y funciones. Algunas bibliotecas sólo son compatibles con uno de los tableros. Esto significa que la mayoría de las veces tu código ESP8266 no será compatible con el ESP32. Sin embargo, normalmente sólo necesitas hacer algunas modificaciones.

Tenemos una lista dedicada de tutoriales y proyectos gratuitos para las placas ESP32 y ESP8266 que utilizan el IDE de Arduino y que pueden serle útiles:

MicroPython Firwmare – ESP32 vs ESP8266

Otra forma habitual de programar las tarjetas ESP32 y ESP8266 es usando el firmware de MicroPython.

MicroPython es una reimplementación de Python 3 dirigida a microcontroladores y sistemas empotrados. La micro pitón es muy similar a la pitón normal. Así que, si ya sabes programar en Python, también sabes programar en MicroPython.

En MicroPython, la mayoría de los scripts de Python son compatibles con ambas placas (a diferencia de cuando se utiliza el IDE de Arduino). Esto significa que la mayoría de las veces puedes usar el mismo script para ESP32 y ESP8266.

¿ESP32 o ESP8266?

Así que, en este punto, puede que te estés preguntando: ¿Debería usa un ESP8266 o un ESP32?

Realmente depende de lo que quieras hacer. Hay espacio para ambas tablas, y ambas tienen pros y contras.

El ESP8266 es más barato que el ESP32. Aunque no tiene tantas funcionalidades, funciona bien para la mayoría de los proyectos sencillos. Sin embargo, tiene algunas limitaciones cuando se trata de la cartografía de GPIO, y puede que no tengas suficientes pines para lo que pretende hacer. Si ese es el caso, deberías usar un ESP32.

El ESP32 es mucho más potente que el ESP8266, viene con más GPIOs con múltiples funciones, Wi-Fi más rápido, y también soporta Bluetooth. Mucha gente piensa que el ESP32 es más difícil de manejar que el ESP8266 porque es más complejo. En nuestra opinión, es tan fácil programar el ESP32 como el ESP8266, especialmente si se pretende programar con el “lenguaje Arduino” o el MicroPython.

El ESP32 también tiene algunos inconvenientes. El ESP32 es más caro que el ESP8266. Así que, si estás construyendo un simple proyecto de IO, el ESP8266 podría valerte por un precio más bajo. Además, como el ESP8266 es “más antiguo” que el ESP32, algunas bibliotecas y características están mejor desarrolladas para el ESP8266 y encontrarás más recursos (foros, gente con los mismos problemas y cómo resolverlos, etc.). Sin embargo, con el paso del tiempo, el ESP32 está siendo ampliamente adoptado, y estas diferencias en términos de desarrollo y bibliotecas no serán notables.

En 2020, usamos casi exclusivamente el ESP32 para proyectos de IO. Es más versátil, y viene con muchas más funcionalidades como el Bluetooth, diferentes fuentes de despertador, muchos periféricos, y mucho más. Además, la diferencia de precio no es un gran problema, en nuestra opinión. Creemos que una vez que te muevas al ESP32, no querrás volver al ESP8266.

Última actualización el 2020-06-17 / Enlaces de afiliados / Imágenes de la API para Afiliados

[enigmaticlearner]

“Data” is not simple as we think!

After a short period of time,hello again my friends! Today in our seventh blog article we will be talking about data controlling and few more new topics.

First lets learn what data and information is.Data is naturally unsorted things.

Data becomes information when they are sorted.That is when it becomes useful. Data can come in various formats like,

So data can can be stored, read, updated/modified, and deleted as we need to and thereby they can be organized in a useful manner.

At run time of software systems, data is stored in main memory, which is volatile Therefore data should be stored in non-volatile storage for persistence.

There are two main ways of storing data • Files •Databases

Out of these two types databases have proved to be much efficient. This is due to the advantages that are observed in databases.

Data independence –application programs are independent of the way the data is structured and stored. Efficient data access Enforcing integrity–provide capabilities to define and enforce constraints Ex: Data type for a name should be string Restricting unauthorized access Providing backup and recovery Concurrent access

There are many formats for storing data •Plain-text, XML, JSON, tables, text files, images, etc…

Digging more upto "data" related terms,lets take a brief look at the terms Database and Database Management System.

A database is a place where data is stored.More accurately a database is a collection of related data.Whereas a database management systems (DBMS) is a general-purpose software system that facilitates the processes of defining, constructing, manipulating, and sharing databases among various users and applications.

Also DBMSs are used to connect to the DB servers and manage the DBs and data in them •PHPMyAdmin •MySQL Workbench

In databases data can be arranged in the following manners.. •Un-structured Semi-structured data is data that has not been organized into a specialized repository, such as a database, but that nevertheless has associated information, such as metadata, that makes it more amenable to processing than raw data.

•Semi-structured Structured data is data that has been organized into a formatted repository, typically a database, so that its elements can be made addressable for more effective processing and analysis.

•Structured Unstructured data is information, in many different forms, that doesn't hew to conventional data models and thus typically isn't a good fit for a mainstream relational database.

*SQL-Structered Query Language

SQL-Structered Query Language is used to process data in a databases. Furthermore SQL can be categorized as DDL and DML.

DDL-Data definition language CRUD databases

DML-Data manipulation language CRUD data in databases

•Hierarchical databases •Network databases •Relational databases •Non-relational databases (NoSQL) •Object-oriented databases •Graph databases •Document databases are the types of databases to be found.

** Data warehouse and Big data

Data warehouse and Big data have become two popular topics in the new world.

Data warehouse a system used for reporting and data analysis, and is considered a core component of business intelligence.

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Big data was originally associated with three key concepts: volume, variety, and velocity.

So how do we use databases in day to day life? To process data in DB we use, •SQL statements •Prepared statements •Callable statements

                                               Connection statement codes

                                              Prepared statement codes

3.Callable statements

CallableStatement cstmt = con.prepareCall("{call anyProcedure(?, ?, ?)}"); cstmt.execute();

Other useful objects are, •Connection

•Statement

•Reader

•Result set

**ORM The mapping of relational objects (ORM, O / RM and O / R) in computer science is a programming technique for converting data between incompatible writing systems using object-oriented programming languages.

ORM implementations in JAVA • Java Beans • JPA

POJO stands for Plain Old Java Object, and would be used to describe the same things as a "Normal Class" whereas a JavaBean follows a set of rules. Most commonly Beans use getters and setters to protect their member variables, which are typically set to private and have a no-argument public constructor.

POJO stands for Plain Old Java Object, and would be used to describe the same things as a "Normal Class" whereas a JavaBean follows a set of rules. Most commonly Beans use getters and setters to protect their member variables, which are typically set to private and have a no-argument public constructor.

Beans Beans are special type of Pojos. There are some restrictions on POJO to be a bean.

POJO Vs Beans

All JavaBeans are POJOs but not all POJOs are JavaBeans. A JavaBean is a Java object that satisfies certain programming conventions: the JavaBean class must implement either Serializable or Externalizable; ... all JavaBean properties must have public setter and getter methods (as appropriate).

* Java Persistence API (JPA) Java Persistence API is a collection of classes and methods to persistently store the vast amounts of data into a database which is provided by the Oracle Corporation.

JPA can be used to reduce the burden of writing codes for relational object management. A programmer follows the ‘JPA Provider’ framework, which allows easy interaction with database instance. Here the required framework is taken over by JPA.

JPA is an open source API.Some of the products are, Eclipselink,Toplink,Hibernate.

**NoSQL SQL databases are commonly known as Relational databases(RDBMs),while NoSQL databases are called non-relational databases or distributed databases.

NoSQL comes in to need when semi-structured and un-structured data are needed to be processed.

It is advantageous to use NoSQL databases as they have high performance, supports both semi-structured and un-structured data,scalability.

MongoDB, BigTable, Redis, RavenDB Cassandra, HBase, Neo4j and CouchDB are examples of NoSQL databases.

You can find more by this link..

https://searchdatamanagement.techtarget.com/definition/NoSQL-Not-Only-SQL

**Hadoop Hadoop is an open source framework implmented by Apache.It is Java based.It is used to process large datasets across clusters of computers distributedly.Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

Hadoop is consisted of two major layers,

   1.Processing/Computation layer (MapReduce), and    2.Storage layer (Hadoop Distributed File System).

Finally we'll turn into the topic Information retrieval (IR).

This is the activity of obtaining information system resources relevant to an information need from a collection. Searches can be based on full-text or other content-based indexing.

For better results, IR should have the following characteristics. 1. Fast/performance 2. Scalablitiy 3. Efficient 4. Reliable/Correct

References

[1] Wikipedia.com. “ Hadoop ”. [Accessed: April 10, 2019].

[2] TutorialsPoint.com. “JPA”. [Accessed: April 10 , 2019]

[3] TutorialsPoint.com. “Hadoop”. [Accessed: April 10 , 2019].

[lebronjamesuit1508]

Web application

https://web.stanford.edu/~ouster/cgi-bin/cs142-winter15/lectures.php

https://web.stanford.edu/~ouster/cgi-bin/cs142-fall10/lecture.php?topic=scale

Readings for this topic: none.

Scale of Web applications: 1000x anything previously built.

Load-balancing routers: replicate Web front ends.

How to handle session data?

Scaling the storage system:

Because of scalability problems, we are seeing many new approaches to storage:

Scaling issues make it difficult to create new Web applications:

DNS (Domain Name System) load balancing:

HTTP redirection (HotMail, now LiveMail):

Load-balancing switch:

Specify multiple targets for a given name

DNS servers rotate among those targets

Front-end machine accepts initial connections

Redirects them among an array of back-end machines.

All incoming packets pass through one switch, which dispatches them to one of many servers; once TCP connection established, load balancer will send all packets for that connection to the same server.

In some cases the switches are smart enough to inspect session cookies, so that the same session always goes to the same server.

Different requests may go to different servers

Individual servers may crash

Need for session data to move from server to server as necessary.

Solution #1: keep all session data in shared storage:

Solution #2: keep session data in cookies

Solution #3: cache session data in last server that used it

File system

Database

May be expensive to retrieve for each request

No server storage required

Cookies limit the amount of data that can be stored

Store server map in shared storage

If future request goes to different server, use map to find server holding session data, retrieve data from previous server.

Almost all Web applications start off using relational databases.

A single database instance doesn't scale very far.

Applications must partition data among multiple independent databases, which adds complexity.

Memcache: main-memory caching system

Key-value store (both keys and values are arbitrary blobs)

Used to cache results of recent database queries

Much faster than databases: 500-microsecond access time, vs. 10's of milliseconds

Problems:

Example: Facebook has 2000 memcache servers

Writes must still go to the DBMS, so no performance improvement for them

Must manage consistency in software (e.g., flush relevant memcache data when database gets modified)

RAMCloud: new storage system under development in a research project here at Stanford:

Store all data in DRAM permanently

Aggregate thousands of servers in a datacenter

Use disk to backup data for high durability and availability

32-64 GB per server

100-500 TB per system

Potentially very high performance:

5-10 microsecond access time

1 million operations/second/server

Initially, can't afford expensive systems for managing large scale.

But, application can suddenly become very popular ("flash crowd"); can be disastrous if scalability mechanisms are not in place.

Can take weeks or months to buy and install new servers.

Must become expert in datacenter management.

Each 10x growth in application scale typically requires new application-specific techniques.

Cloud computing:

Example #1: Amazon Web Services

Example #2: Google AppEngine

In the future we are going to see more systems like AWS and AppEngine, with more and more convenient high-level interfaces.

Separate scalability issues from application development.

Specialized providers offer scalable infrastructure.

Just pay for what you need.

Elastic Compute Cloud (EC2): rent CPUs in an Amazon datacenter for $0.10/CPU/hour

Scale up and down by hundreds of CPUs almost instantly

Simple Storage Service (S3): stores blobs of data inexpensively ($0.12-$0.15/GB/month).

AWS provides low-level facilities; users still have to worry about various management issues ("how do I know it's time to allocate more CPUs?")

Much higher level interface:

More constrained environment

You provide pieces of Python or Java code, URLs associated with each piece of code.

Google does the rest:

AppEngine also includes a scalable storage system

Allocate machines to run your code

Arrange for name mappings so that HTTP requests find their way to your code

Scale machine allocations up and down automatically as load changes

Must use Python or Java

Must use specialized Google storage system

[robertbryantblog]

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