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Introduction: Large enterprises, which have more than one business unit, usually have more than one data platform environment. In fact, companies trying to be agile to make quick decisions, all have business unit-specific data platforms. With the computational advancements and rapid growth in data analytics and science, data integration tasks for data platforms are increasing exponentially as they need to support transactional as well as batch processing jobs. Here, the challenge is choosing the orchestration tool, as data integrations have to work in conjunction with different platforms. Though there are several tools in the market, in the article, we will cover how Apache airflow became the most trusted tool for the orchestration industry and its advantages. ETL: Traditional ETL (Extract, Transform, and loading) tools like Informatica, Ab Initio, and Talend all come with their own orchestration process. But as they are proprietary tools, it's expensive as well. Cloud service providers Azure, AWS (Amazon Web Services), and Google Cloud are all trying cash in the orchestration space so Azure has Synapse and AWS has Glue, Step function, etc. Basics: Orchestration is a simple or complex series of workflows defining a process. Workflows depend on one another to perform a business function or a series of business func‐tions calling other services. Then what is the ? process A process is a business function or set of business functions achieved by executing one or more tasks. Then what is the ? task A task is a step in a process. A task may call a service to perform a particular data-related function. Then what is the ? Service A service is software like Azure Synapse Analytics, Python data frame, or AWS DynamoDB that performs tasks. Common Problems in the Legacy Tools: The most common problem in the orchestration tools is their ability to successfully implement and maintain processes across the organization. Engineers and managers want to choose a tool that addresses the below issues: Dependency management: Lack of dependency management results in maintaining process-to-process and task-to-tasks dependency checks within the codebase along with business logic. This increases development time based on the number of processes or tasks involved in a workflow. Task modification to existing processes: Workflow is defined as each step within the code. Embedding the workflow in the code requires manual effort to fully review the code to make changes to one or more tasks resulting in additional time involved in project enhancements and testing. Troubleshooting: Troubleshooting a particular process should not require the need to understand the dependencies, as it results in additional support time. Restart: It should be fairly easy to restart the failed task or trigger an ad-hoc task. Orchestration should not warrant the need to rerun the entire process if a particular task within the process failed. User-friendly interface to track the progress: Orchestration tools should provide best in class user interface with the details to visualize the tasks involved within the processes in one place. Engineers investigating the issue should have a UI to understand the flow of a particular process. UI should display the current process in execution, the time it's taking, etc. Apache Airflow Airflow is a platform to programmatically author, schedule, and monitor workflows geared at data pipelining. Airflow is used to author workflows as DAGs of tasks. The Airflow scheduler executes tasks on an array of workers while following the specified dependencies. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed. Code Development: Authoring a data pipeline using existing tasks involves writing a Python module and deploying it to the Airflow installation. Adding a new kind of task to an airflow installation involves writing and deploying Python code in packages called plugins. This aligns with the primary coding skill set of Data Engineers and scientists, which is Python. Benefits At a high level, process dependency management is easy to develop and maintain via user interface (UI) and support in Airflow, which helps immensely to save time for developers and supporting resources. DAG creation is all in Python scripting language which is the primary coding skillset for Data Engineers. Changes within Airflow can be captured using the GIT repository enabling version controls and change management practices. Airflow has good documentation and an active internal community for answering questions. What are the skillsets required to develop and maintain Airflow? Python is a major skill required to use Airflow. Initial learnings are required to set up Airflow and understand the architecture so it's evident for future maintenance. Can Engineers visualize the Jobs? Yes, Airflow has a Web interface to start and stop processes and also to monitor the current status of the processes. Does Airflow support any integration with Data Quality checkers? DataDog can be integrated with Airflow. How do we make ensure Airflow’s availability? Airflow workers and Web servers can be load balanced. Airflow Scheduler is a single point of failure. How does Airflow scale? Airflow Web server can be scaled based on the number of requests. Airflow workers can be scaled up and down based on requirements.

Apache Airflow And Its Contribution to Enterprise Data Integration

Letters. Personal Records. Dated Notes.

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The Idea of a University Defined and Illustrated: Lecture IV - Elementary Studies

Everything we do as humans requires effort. The complexity of the task at hand determines how much effort - mental or physical - we would need to exert to get the job done. Our brains are wired to construct a flow around how we complete tasks. This is may be consciously done or not and the flow may be sequential or not. The point is we would have to go through these mapped out steps in order to achieve our desired end goal. Processes are a series of actions or steps taken in order to achieve a particular end. They give stability and help organize complex ideas or projects and because . We tend to want to “processify” every task we are faced with and expending the least amount of effort. It's inborn to devise procedures for achieving even the most menial of tasks, including how we get ready for work or school, how we eat, where and how we shop for groceries, how we study, how we cook, and so on. Basically, there’s the process for everything within the scope of “normal human behavior”. humans are creatures of habit On the business side of things, a company’s objectives would typically prioritize giving better returns for shareholders, increasing value for customers, increasing satisfaction levels among employees amongst other critical factors that measure success. The role of process improvement is to make sure that value, profit, or satisfaction on any level is constantly being maximized by optimizing business processes as the company’s strategic goals and consumer demand evolves. To improve or design process flows without focusing on or involving the humans who will use them, is like designing a stunning custom-made piece of clothing for a client with the finest fabric, using state-of-the-art machines and gorgeous finishing without taking the body measurement of the client who will wear the piece. Processes are made for People When the users who will primarily drive a process aren’t involved in that process Ab Initio, there are good reasons to believe that process will flop. It’s easy to get swayed by the advancements in technology and the advent of highly optimized tools into thinking otherwise. Businesses are often consumed with the zeal to implement tools or adopt enhanced business practices and often forget the people who should be at the center of the project. Oftentimes, it’s the acceptance level of the users that will determine if a process design or improvement will succeed in the real world. In designing processes or remodeling an existing one, process managers should know there are 3 things to consider: What should work = Best Practices What can work = Expectation What ACTUALLY works = Reality “ ” involves what process principles or models to use, how implementation should be executed and phased. “ ” is the human view of things - organizational structures, human biases, existing system nuances, and how they affect the design process. What should work What can work “ ” is the conundrum process managers have to contend with. Striking a delicate balance between best practices and expectations. What works While we need the principles to guide us in process designs, we must never forget it has to live up to the expectation of the people who will use it else your reality will just become a bunch of routine tasks that achieve well… nothing. When people don’t know, they don’t care In analyzing, the , 4 out of 5 points were centered around people: reasons why processes fail People forget Low adoption by people People lack vision People don’t understand their roles in the process. When people can’t see the big picture or how a process directly or indirectly affects them, they tune out. A lack of communal vision is a clear indication that the project is probably dead-on-arrival. When a process immediately fails, it’s probably because the people never . understood the assignment Participants need to be involved. To constantly be in the know of the process changes and updates made, and more importantly, have a proper understanding of the value a process improvement brings to the table. They need to understand the importance of evaluating processes to ensure their objectives align with corporate goals When processes are being designed, users should be the focus but when processes fail or succeed, processes should be examined When the outcome of a process is negative and deviates from its intended plan, there’s a tendency to hurriedly identify and blame the fall guy but it’s that should be blamed. Name-calling and finger-pointing distract managers from what they need to give precedence to – . the process, not people the process People change Processes are made to help the people who will use these processes execute their jobs in the most efficient, seamless, and transparent way possible to meet end goals. Repeated processes form the core of a business which eventually helps to boost profits, reduce costs, increase productivity, foster internal collaborations and improve the customer’s experience. Experts say business processes should be at least once a year. This basically points to the fact that processes don’t last forever, meaning at some point they will have to change. The are extremely crucial. benefits of business process management audited, optimized, and updated As consumer needs change, business climates have to adjust to these changes. Accompanying processes should accommodate these changes in the most efficient way possible. Process users must also be aware of how these changes affect their activities. As consumer needs change, business climates have to adjust to these changes The truth is sometimes, process managers cannot always forestall failures from occurring because no matter how hard you try, it's almost impossible to anticipate every fail point in your process design. So, when processes fail, the trick to having a people-focused mindset is to identify and examine the failure points in the current process and find solutions bearing in mind that processes will have to be optimized regularly to suit the dynamic nature of users.

The Human Factor : Why Your Processes Are Bound to Fail

How We Localized Our Website in 9 Languages in Under a Week — And So Can You

Procesando Datos Para Deep Learning: Datasets, visualizaciones y DataLoaders en PyTorch

Apresentarei aqui algumas abordagens que ajudam( ou podem ajudar) no desempenho e qualidade de seu Front End. Estas ideias foram fundamentadas em minha experiência profissional e no curso de Arquitetura de Software. Serei mais sucinto possível. Qualquer dúvida entrem contato comigo. Antes de entrar nas dicas É importante antes de iniciar ter em mente o funcionamento da Web e da comunicação do HTTP. Pesquisem rapidamente sobre Three Way Handshake, renderização de conteúdo nos browser web. Responsabilidade do HTML, CSS e JS. Vamos as Dicas 1 — Mantenha seu HTML, CSS e JS em arquivos separados; 1.1 — Facilitamos o processo de manutenção de nosso Front End; 1.2 — Conseguimos manter o princípio da responsabilidade única; 1.3 — Facilidade em adotar ferramentar de transpilação de CSS (SASS e LESS) e Bundles (Webpack). Outros formas de utilizar os recurso no HTML: Inline e Embedded. 2 — CSS no início e JS no fim do HTML. 2.1 — Coloque o CSS no início para que a página não carregar inicialmente sem estilo e coloque o JS no final para que a página não se torne lenta em sua primeira resposta; O fonte fica assim: Browser Diet Estilo no início e JS no fim < > html < > head < = > meta charset "UTF-8" < > title </ > title  < = = = > link rel "stylesheet" href "/css/siteCSS.css" media "all" </ > head < > body < > p </ > p   < = > script src "/js/siteJS.js" </ > script </ > body </ > html Indo mais além: uma outra abordagem é utilizar a requisição assíncrona para o JS. Isso faz com que a página não espera o retorno do arquivo para continuar a renderização. (<script async src=”siteJS.js”></script>) 3 — Sempre minifique para produção Ele faz com que um texto legível para nós se torne uma versão menos verbosa e com isso realizar a diminuição dos arquivos( remoção de comentários, redução de nomes de variáveis, remoção de quebra de linhas, etc). 3.1 — Transporte de arquivos menores; 3.2 — Maior velocidade no carregamento pelo Browser; 3.3 — Menor carga de dados na rede e no servidor de aplicação; Dica: pesquise por ferramentas de minificação. 4 —Sempre bundling para produção O processo se resume em criar um (ou alguns) arquivos a partir de vários outros. 4.1 — Redução da quantidade de requisições HTTP ao servidor de aplicação; 4.2 — Melhoria desempenho da página web; Ferramenta: (querido) Webpack. 5 —Imagem Sprites “Cria uma imagem com todas dentro”.Observe o quadro abaixo, ele será a melhor explicação dessa técnica: 5.1 — Redução de requisições HTTP; 5.2 — Redução no tempo de carregamento do site; Ferramenta: vale testar o processo com estas ferramentas https://css.spritegen.com/ , https://www.toptal.com/developers/css/sprite-generator/ . 6 — Remoção de metadados de imagens As imagens possuem metadados que não influenciam na funcionalidade delas para os websites( modelo da câmera, data, local). 6.1 — Diminuição do tráfego na rede; 6.2 — Carregamento mais rápido das imagens; Exemplo de ferramentas: https://www.verexif.com/en/ , https://www.exifremove.com/ . E por fim Existem muitos outras técnicas para melhorar suas páginas web, porém comece com estas pois elas resultarão em grande mudanças na organização e no desempenho do seu site. Não existe bala de prata e além disso sempre vale analisar se para o projeto em questão o aumento da complexidade justifica os ganhos. Referências: http://lesscss.org/ https://sass-lang.com/ https://browserdiet.com/ https://webpack.js.org/

Dicas para ter um “bom” Front End

, una frase popular de la que se habla, sigue siendo un misterio para muchos profesionales. Como proveedor de servicios de web scraping, decidimos reunir algunas de las preguntas y respuestas comunes de web scraping para ayudar a desentrañar el misterio. Web scraping 1. ¿Qué es el web scraping? El web scraping, también conocido como web harvesting y extracción de datos, se refiere básicamente a la obtención de datos disponibles en la World Wide Web a través del Protocolo de transferencia de hipertexto (HTTP) o mediante navegadores web. Leer más: Web Scraping: Cómo Comenzó y Qué Sucederá en El Futuro 2. ¿El Web Scraping Es Legal en Algunos Países? El web scraping en sí no es ilegal, ya que es solo una herramienta para recopilar datos más fácilmente. Sin embargo, hacerlo puede violar la ley cuando robas información no pública, o el sitio de destino prohíbe estrictamente el scraping sin permiso previo o sin mencionar ciertos derechos de autor legales relacionados con el uso de sus datos. Se recomienda encarecidamente que lea detenidamente los Términos y condiciones (ToS) del sitio web antes de scraping. Leer más: ¿El Web Scraping Es Legal en Algunos Países? 3. ¿Cuál es la mejor herramienta de web scraping? La elección de una herramienta de scraping depende de la naturaleza del sitio web y su complejidad. Siempre que la herramienta pueda ayudarlo a obtener los datos de manera rápida y sin problemas con un costo aceptable o cero, puede elegir la herramienta que desee. Leer más: Las mejores Herramientas de Data Scraping para 2020 4. ¿Puedo scrape LinkedIn o Facebook? Desafortunadamente, ambos sitios web a través de robots.txt bloquean el web crawling automatizado.Las disputas legales entre LinkedIn y las empresas que han robado datos han sido un tema candente. Sin embargo, si solo obtiene datos públicos y listas de dos sitios web, es legal extraer datos de ambos sitios web. ; Leer más: Scrape la publicación de LinkedIn 5 Cosas que Debes Saber Antes de Scraping de Facebook 5. What is web scraping used for? Web scraping is aimed at collecting data so it can be applied in any industry that needs the data. It is used largely in market research, , human capital optimization, , and many other fields. price monitoring lead generation Read more: Data Insight: 54 Industries Using Web Scraping 6. ¿Puedo extraer datos de toda la web? Muchas personas creen que el web scraping se puede utilizar para scrape datos de toda la World Wide Web o al menos cientos de miles de sitios web. Esto no es factible en la práctica. Dado que los sitios web no siguen una estructura de página universal, sería difícil para un web scraper interactuar con todas las páginas. 7. ¿Web scraping es data mining ? El web scraping y data mining son dos conceptos diferentes. El web scraping consiste en recopilar datos sin procesar, pero data mining es el proceso de descubrir patrones en grandes conjuntos de datos. Leer más: Data Mining (Wiki) Data Mining Explicada con 10 Historias Interesantes 8. ¿Cómo evitar ser bloqueado al scraping un sitio web? Muchos sitios web lo bloquearían si los scrape demasiado. Para evitar ser denegado, debe hacer que el proceso de scraping se parezca más a un humano que navega por un sitio web. Por ejemplo, agregar un retraso entre dos solicitudes, usar proxies o aplicar diferentes patrones de scraping puede ayudarlo a no ser bloqueado. Leer más: ¿Cómo Scrape Websites sin ser bloqueado? 9. ¿Se puede resolver CAPTCHA durante el web scraping? CAPTCHA solía ser una pesadilla para el web scraping, pero ahora se puede resolver fácilmente. Muchas herramientas de web scraping tienen la característica de resolver CAPTCHA automáticamente durante el proceso de extracción. Y hay muchos solucionadores CAPTCHA que pueden integrarse con sistemas de scraping. : Leer más 5 Cosas que Debes Saber al Evitar CAPTCHA para El Web Scraping 10. ¿Puedo volver a publicar el contenido extraído a través del web crawling? La republicación de contenido debe contar con el consentimiento del propietario. Aunque puede scrape el contenido de texto de sitios web que permiten bots, aún necesita usar estos datos de una manera que no infrinja los derechos de autor del editor. 11. ¿Cuál es la diferencia entre el web scraping y el web crawling ? El Web scraping y el web crawling son dos conceptos relacionados. El web scraping como mencionamos anteriormente es un proceso de obtención de datos de sitios web; el web crawling es navegar sistemáticamente por la World Wide Web, generalmente con el fin de indexar la web. Leer Más: Data crawler 12. ¿Qué es un robots.txt file? Robots.txt es un archivo de texto que le dice a los crawlers, bots o spiders si un website podría scrape. Es fundamental comprender el archivo robots.txt para evitar que se bloquee durante el web scraping. 13. ¿Puedo scrape datos detrás de una página de inicio de sesión? Sí, puede scrape los datos detrás de una página de inicio de sesión fácilmente si tiene una cuenta funcional en el sitio web. El proceso de scraping después del inicio de sesión sería similar al de un scraping normal. Leer más: Extraer datos detrás de un inicio de sesión 14. ¿Cómo extraigo el contenido de las páginas web dinámicas? Un sitio web dinámico actualizaría los datos con frecuencia. Por ejemplo, siempre hay nuevas publicaciones en Twitter. Para scrape desde dicho sitio web, es el mismo proceso que scrape otros sitios web, pero permitiría que el scraper acceda al sitio web con cierta frecuencia para obtener los datos actualizados continuamente. Leer más: Ejecutar/Programar tareas en Cloud 15. ¿Puede una herramienta de web scraping descargar archivos de un sitio web directamente? Sí, hay muchas herramientas de scraping que pueden descargar archivos directamente del sitio web y guardarlos en Dropbox u otros servidores al scraping información de texto.

Las 15 preguntas más frecuentes sobre Web Scraping

Con el advenimiento de los grandes datos, las personas comienzan a obtener datos de Internet para el con la ayuda de . Hay varias formas de hacer su propio rastreador: extensiones en los navegadores, codificación de python con o Scrapy, y también como . análisis de datos rastreadores web Beautiful Soup herramientas de extracción de datos Octoparse Sin embargo, siempre hay una guerra de codificación entre las arañas y los anti-bots. Los desarrolladores web aplican diferentes tipos de técnicas anti-scraping para evitar que sus sitios web sean raspados. En este artículo, he enumerado las cinco técnicas anti-scraping más comunes y cómo se pueden evitar. 1. IP Una de las formas más fáciles para que un sitio web detecte actividades de web scraping es a través del seguimiento de IP. El sitio web podría identificar si la IP es un robot en función de sus comportamientos. Cuando un sitio web descubre que se ha enviado una de solicitudes desde IP o en , existe una buena posibilidad de que la IP se bloquee porque se sospecha que es un bot. En este caso, lo que realmente importa para construir un crawler anti-scraping es el y la de visitas por unidad de tiempo. Aquí hay algunos escenarios que puede encontrar. cantidad abrumadora una sola dirección periódicamente un corto período de tiempo número frecuencia : Hacer múltiples visitas en segundos. No hay forma de que un humano real pueda navegar tan rápido. Entonces, si su crawler envía solicitudes frecuentes a un sitio web, el sitio web definitivamente bloquearía la IP para identificarlo como un robot. Escenario 1 Disminuya la velocidad de scraping. Configurar un tiempo de retraso (por ejemplo, la función "dormir") antes de ejecutar o aumentar el tiempo de espera entre dos pasos siempre funcionaría. Solución: : Visitar un sitio web exactamente al mismo ritmo. El humano real no repite los mismos patrones de comportamiento una y otra vez. Algunos sitios web monitorean la frecuencia de las solicitudes y si las solicitudes se envían periódicamente con el mismo patrón exacto, como una vez por segundo, es muy probable que se active el mecanismo anti-scraping. Escenario 2 : Establezca un tiempo de retraso aleatorio para cada paso de su rastreador. Con una velocidad de scrapubg aleatoria, el rastreador se comportaría más como los humanos navegan por un sitio web. Solución Algunas técnicas anti-scraping de alto nivel incorporarían algoritmos complejos para rastrear las solicitudes de diferentes IP y analizar sus solicitudes promedio. Si la solicitud de una IP es inusual, como enviar la misma cantidad de solicitudes o visitar el mismo sitio web a la misma hora todos los días, se bloquearía. Escenario 3: Cambie su IP periódicamente. La mayoría de los servicios VPN, y servicios proxy podrían proporcionar IP rotadas. Al través una solicitud Rotación de IP, el rastreador no se comporta como un bot, lo que reduce el riesgo de ser bloqueado. Solución: cloud servers 2. Captcha ¿Alguna vez has visto este tipo de imagen al navegar por un sitio web? 1.Necesita un clic 2.Necesita seleccionar imágenes específicas Estas imágenes se llaman Captcha. Captcha significa prueba de Turing pública completamente automatizada para diferenciar a computadoras y seres humanos. Es un programa público automático para determinar si el usuario es un humano o un robot. Este programa proporcionaría varios desafíos, como imagen degradada, rellenar espacios en blanco o incluso ecuaciones, que se dice que son resueltas solo por un humano. Esta prueba ha evolucionado durante mucho tiempo y actualmente muchos sitios web aplican Captcha como técnicas anti-scraping. Alguna vez fue muy difícil pasar Captcha directamente. Pero hoy en día, muchas ahora se pueden aplicar para resolver problemas de Captcha, aunque pueden requerir habilidades de programación más avanzadas. Algunas personas incluso crean sus propias bibliotecas de características y crean técnicas de reconocimiento de imágenes con aprendizaje automático o habilidades de aprendizaje profundo para pasar esta verificación. herramientas de código abierto Es más fácil no activarlo que resolverlo Para la mayoría de las personas, la forma más fácil es ralentizar o aleatorizar el proceso de extracción para no activar la prueba Captcha. Ajustar el tiempo de retraso o usar IP rotados puede reducir efectivamente la probabilidad de activar la prueba. 3. Iniciar Sesión Muchos sitios web, especialmente las plataformas de redes sociales como Twitter y Facebook, solo le muestran información después de iniciar sesión en el sitio web. Para rastrear sitios como estos, los rastreadores también necesitarían simular los pasos de registro. Después de iniciar sesión en el sitio web, el rastreador debe guardar las cookies. Una cookie es un pequeño dato que almacena los datos de navegación para los usuarios. Sin las cookies, el sitio web olvidaría que ya ha iniciado sesión y le pedirá que vuelva a iniciar sesión. Además, algunos sitios web con mecanismos de raspado estrictos solo pueden permitir el acceso parcial a los datos, como 1000 líneas de datos todos los días, incluso después de iniciar sesión. Tu bot necesita saber cómo iniciar sesión 1) Simular operaciones de teclado y mouse. El rastreador debe simular el proceso de inicio de sesión, que incluye pasos como hacer clic en el cuadro de texto y los botones "iniciar sesión" con el mouse, o escribir información de cuenta y contraseña con el teclado. 2) Inicie sesión primero y luego guarde las cookies. Para los sitios web que permiten cookies, recordarían a los usuarios guardando sus . Con estas cookies, no es necesario volver a iniciar sesión en el sitio web a corto plazo. Gracias a este mecanismo, su rastreador podría evitar tediosos pasos de inicio de sesión y raspar la información que necesita. cookies 3) Si, desafortunadamente, encuentra los mecanismos de escalado estrictos anteriores, puede programar su rastreador para monitorear el sitio web a una frecuencia fija, como una vez al día. Programe el rastreador para que raspe las 1000 líneas de datos más recientes en períodos y acumule los datos más nuevos. 4. UA UA significa , que es un encabezado del sitio web para identificar cómo visita el usuario. Contiene información como el sistema operativo y su versión, tipo de CPU, navegador y su versión, idioma del navegador, un complemento del navegador, etc. User-Agent Un ejemplo de UA: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0) AppleWebKit/535.11 (KHTML, como Gecko) Chrome/17.0.963.56 Safari/535.11 Al scrape un sitio web, si su rastreador no contiene encabezados, solo se identificaría como un script (por ejemplo, si usa python para construir el rastreador, se declararía como un script de python). Los sitios web definitivamente bloquearían la solicitud de un script. En este caso, el buscador debe pretender ser un navegador con un encabezado UA para que el sitio web pueda proporcionarle acceso. A veces, el sitio web muestra diferentes páginas o información a diferentes navegadores o diferentes versiones, incluso si ingresa al sitio con la misma URL. Lo más probable es que la información sea compatible con un navegador mientras que otros navegadores están bloqueados. Por lo tanto, para asegurarse de que puede ingresar a la página correcta, se requerirán múltiples navegadores y versiones. Cambie entre diferentes UA para evitar ser bloqueado Cambie la información de UA hasta que encuentre la correcta. Algunos sitios web sensibles que aplican técnicas complejas de anti-scraping pueden incluso bloquear el acceso si se usa el mismo UA durante mucho tiempo. En este caso, necesitaría cambiar la información de UA periódicamente. 5. AJAX Hoy en día, se desarrollan más sitios web con AJAX en lugar de las técnicas tradicionales de desarrollo web. AJAX significa JavaScript asíncrono y XML, que es una técnica para actualizar el sitio web de forma asíncrona. En pocas palabras, no es necesario volver a cargar todo el sitio web cuando solo se producen pequeños cambios dentro de la página. Entonces, ¿cómo podría saber si un sitio web aplica AJAX? Toda la página se actualizará incluso si solo realiza un pequeño cambio en el sitio web. Por lo general, aparece un signo de carga y la URL cambia. Para estos sitios web, podríamos aprovechar el mecanismo e intentar encontrar el patrón de cómo cambiarían las URL. Luego, podría generar URL en lotes y extraer información directamente a través de estas URL en lugar de enseñarle a su crawler cómo navegar por sitios web como los humanos. Un sitio web sin AJAX: Solo se cambiará el lugar donde hace clic y no aparecerá ningún signo de carga. Por lo general, la web URL no cambiaría, por lo que el crawler tiene que lidiar con ella de una manera directa. Un sitio web con AJAX: Para algunos sitios web complejos desarrollados por AJAX, se necesitarían técnicas especiales para descubrir formas cifradas únicas en esos sitios web y extraer los datos cifrados. Resolver este problema puede llevar mucho tiempo porque las formas cifradas varían en las diferentes páginas. Si pudiera encontrar un , podría descifrar automáticamente el sitio web y extraer datos. navegador con operaciones JS incorporadas Las técnicas de web scraping y anti-scraping están progresando todos los días. Quizás estas técnicas estarían desactualizadas cuando lea este artículo. Sin embargo, siempre puede obtener ayuda de . Aquí en Octoparse, nuestra misión es hacer que los datos sean accesibles para cualquier persona, en particular, aquellos sin antecedentes técnicos. Como herramienta de web scraping, podemos proporcionarle soluciones listas para implementar para estas cinco técnicas anti scraping. ¡No dude en cuando necesite una poderosa herramienta de web scraping para su negocio o proyecto! Octoparse contactarnos Referencia Megan Mary Jane. 2019. Cómo evitar las técnicas anti-scraping en el web scraping. Recuperado de: https://bigdata-madesimple.com/how-to-bypass-anti-scraping-techniques-in-web-scraping/ Previously published at http://www.octoparse.es/blog/5-tecnicas-anti-scraping

5 Técnicas Anti-Scraping que Puedes Encontrar

Human Minds in Relation to Non-Human Minds The contemporary discussion of artificial intelligence is not the first time human beings have wrestled with the possibility of other minds that are . Scholastic philosophers invested a considerable effort into understanding angels, with angels understood to be higher than human beings on the great chain of being. And the links of the represent fundamentally different kinds of things that jointly constitute the world, and not a gradual ladder of progress, which is how it is often interpreted today. not human minds great chain of being A significant chunk of Saint Thomas’ is devoted to angelology: Questions 50 through 64 is the “ ,” which goes into a luxury of detail in relation to the nature of angels. In the , angels interact with human beings, but human beings are not the same kind of beings as angels, so this poses certain problems. Aquinas (and other Scholastics) takes the bull by the horns and wrestles directly with these problems posed by angels. Since angels do not need to breathe or drink or eat, which human beings must, there are questions that must be answered about, for example, what was going on when Lot invited two angels to his house in Sodom and he prepared them a meal. Summa Theologiae Treatise on Angels Bible While these questions are interesting, much more interesting are the questions about the minds of angels — how they think, what they know, and how they know. Aquinas argues ( , , Article: 5), citing Averroes to underline his argument, that angels are intellect and will (and, by implication, that the angelic mind has no component of sensation) because they have no bodies naturally joined to them: First Part Question: 54 only “…In our soul there are certain powers whose operations are exercised by corporeal organs; such powers are acts of sundry parts of the body, as sight of the eye, and hearing of the ear. There are some other powers of the soul whose operations are not performed through bodily organs, as intellect and will: these are not acts of any parts of the body. Now the angels have no bodies naturally joined to them, as is manifest from what has been said already ( Question [51] , Article [1] ). Hence of the soul’s powers only intellect and will can belong to them.”  “The Commentator (Metaph. xii) says the same thing, namely, that the separated substances are divided into intellect and will. And it is in keeping with the order of the universe for the highest intellectual creature to be entirely intelligent; and not in part, as is our soul. For this reason the angels are called ‘intellects’ and ‘minds,’ as was said above ( Article [3] , ad 1).” For the contemporary reader coming from the mainstream of naturalistic Anglo-American analytical philosophy (meaning more-or-less my own philosophical perspective), the Thomist is a thought experiment that takes as its premise, “If there are beings such as this, what will the properties of these beings be?” When we today contemplate the possibility of strong AI (or, what I consider more interesting, machine consciousness), we are similarly asking, “If there are beings such as this, i.e., minds attributable to machines, what will these minds be like? How will they think? What will be their motives?” Treatise on Angels Would we say that machine minds have no corporeal bodies naturally joined to them, or would we say that the machine itself would be the corporeal body of some artificial intelligence? One of the persistent ideas about artificial intelligence is that this would be very different from human minds because of the lack of embodiment, and Aquinas seems to point to angels being very different from human beings not merely because they lack corporeal bodies, but because their lack of corporeal bodies means that the angelic mind is distinct from the human mind. One question that vexes artificial intelligence researchers, and especially those who speculate on superintelligence, is the (the problems that arise from the orthogonality thesis, viz. that final goals and intelligence levels are independent). This problem does not exist for angelology, as we can be assured, , that the motivations of human beings and angels, along with all the rest of creation, are aligned with divine purpose, so that there cannot be a radical departure from the directionality to history imposed by an omniscient, omnipresent, and omnipotent deity. orthogonality problem ab initio Now, it is true that rebellious human wills defied divine purpose, and indeed Satan defied divine purpose, but the universe is still unfolding according to the divine plan, despite these misbehaviors. To pursue this would take us deep into the theological permutation of the freewill/determinism debate (which, significantly, is not how the problem is posed or debated today), so we must be content with the idea that the Lord works in mysterious ways and leave it at that. In place of the theological framework within which our ancestors grappled with the problems of , we have a naturalistic and scientific framework within which we think about the minds of machines, the minds of ETI, and the minds of our own species in the distant past and in the far future. In this naturalistic framework there are forms of directionality (though not that of a divine will) with which all existents must align, and by this I mean that arrows of time as they have been variously defined and distinguished. non-human minds Different lists of arrows of time have been produced, all of which I have seen include the thermodynamic arrow of time, which thus points to some consensus. I consider the increasing metallicity of the visible universe to be as robust as the thermodynamic arrow of time, though I have not seen it any of these lists of arrows of time. However you choose to parse the irreversibility of the cosmos, if you recognize arrows of time that introduce irreversibility into the world, then this is a minimal naturalistic alignment that will hold for all minds, whether human, animal, ETI, machine, or otherwise. In other words, any or all of these minds would be minimally subject to the irreversibility of the arrow(s) of time. As I said, this is a naturalistic alignment. I strongly suspect that an analysis of consciousness would reveal structures of consciousness that any consciousness would have in common with any other consciousness, and that would mean that there would be some structures of consciousness in common, as well as some structures only shared by a smaller subset of conscious agents. However, as I noted in and , if a philosophy of mind is not also a philosophy of consciousness, we may have to treat the problems of mind and consciousness separately. minimal Must a Philosophy of Mind be a Philosophy of Consciousness? Extended Cognition and Naturalism If we do so, identifying a mind-body problem distinct from a consciousness-body problem, this distinction gives us a minimal framework for understanding machine intelligence that is not also machine consciousness (and, I suppose, vice versa). And there are some potential advantages to making this distinction. While the idea of extended cognition as applied to human beings or other biologically implemented minds seems, at times, a bit of a stretch, I would not hesitate nearly so much in attributing extended cognition to a machine mind. Every component that we plugged into a machine mind, or which we networked with a machine mind, would constitute a kind of extended cognition. Here we might get into philosophical problems unique to a machine mind that do no (at least, do not ) apply to biologically implemented minds. For example, where would we draw the distinction between a machine mind proper and its extended cognition? If we plug a new module into a machine mind, is this simply a larger machine mind, or is it a machine mind with extended cognition? And there would be problems of a sorites paradox involved in making a machine mind larger or smaller: how many components can we take away from a machine mind and have it still be a mind? yet

Angelology and Artificial Intelligence

Industry 4.0 technologies are rapidly changing the landscape of retail, transportation, and logistics. Technologies like big data analytics, artificial intelligence (AI), autonomous robots, internet of things (IoT), cloud computing, virtual/augmented reality and others are fueling these transformations. As a result, grocery shopping is at the threshold of incredible metamorphosis.  In 2017, when the online retail giant for $13.7 billion, everyone in the grocery industry realized that a new wave of e-grocery has emerged and is going to transform the entire grocery business. Even though e-grocery was not new as existing established players like Walmart and startups like Instacart were already venturing into the online grocery space, the Whole Foods purchase created a new level of awareness of things that are going to come. Today the global grocery industry understands that online retail is the future. Businesses are trying to find their path into this newly discovered land. They are seriously examining solutions on how to engage customers online, optimize inventory and deliver accurately and quickly. Amazon purchased one of the prominent grocery stores Whole Foods Challenges of e-grocery inventory management The success of an e-grocery business is fundamentally dependent on inventory management; these grocers walk the fine line between over-stocking and under-stocking. Over-stocking uses up costly warehouse space and locks up capital which may otherwise be available for other moving lines. Products decay over time and in perishables the decay is very quick, often by the end of the day. Every wastage affects the bottom line and pushes up prices which turn makes the business unproductive. On the other hand, understocking stunts the business. Nothing can be worse than turning the customer away by not being able to supply an item. In traditional e-commerce businesses, customers can pre-order products that are not in stock. But customers need grocery now! One cannot be listing cereals, dairy, fruits, vegetables, baby food, oil, detergents, personal care items as out of stock. The demand for them is instant. Traditionally Grocery businesses are high-volume, low-value in nature, and profit margins are low. Items are stocked at wholesale prices and sold in retail over a period. If a business must address a shortfall of stock by buying at retail prices to fulfill customer demand, it is ab initio unviable. Optimizing inventory is crucial for the survival of grocery industry. No wonder, the e-grocery companies are beelining for using Industry 4.0 technologies for their inventory management processes. Emerging trends in the e-grocery space Big Data Analytics, AI, and Machine Learning Today e-grocery businesses generate a significant amount of data of what was sold that can be useful to predict future trends. However, this big data requires a heavy investment of time and labor to examine and make sense out of. As a result, data analytics and machine learning are now increasingly resorted to by the businesses to extract relevant insights and make strategic business decisions. For instance, we at , use graphics processing units (GPUs) on Google Cloud to train our machine learning models. The models help us predict the daily and hourly demands. The algorithms consider the variations in parameters like the day of the week, the day of the month, the weather, holidays, traffic conditions, and other significant local events. These variances are used along with other forecasting models that account for the growth and seasonality of the business. In the same way, AI-based predictive models are being used to predict operational failures and improve warehouse management. As machine learning models get smarter with time, the systems get more efficient. There is no doubt that this trend of incorporating data analytics and machine learning is going to help the e-grocery industry immensely. Chaldal Robotics and Automation Besides maintaining inventory, physical movement of the inventory is another vexing problem for the industry as it requires a lot of human labor. Industry 4.0 technologies are coming to help the businesses, as robotic systems are taking over manual tasks. These robots are automating operations in response to customer orders, and are helping businesses build space-saving compact warehouses. Automated warehouses don’t need to waste areas as they can reach spaces that are not easy for humans to reach. There are rails between aisles for the robots to move around and fetch products or to stock them. Robots are more versatile than industrial automated assembly lines, because, they can be easily re-programmed by the designers instead of updating the entire production line. Robotics and Automation, thus go directly towards reducing the size of real estate investments. Ride-sharing and Self-driving logistics Besides warehousing, the other frontier of the challenge is getting the products delivered to customers. Groceries differ from regular e-commerce products such as books in two ways: the quick turn-around time that is expected by customers and the perishable nature of products. As an e-grocery business’s order volume grows, it needs to be able to scale its logistics system along with it, while maintaining a cold chain for the delivery of perishables such as milk, fish, and meat. This considerably increases the delivery cost that further affects the bottom line of the business. At Chaldal, we started a ride-sharing service Chalao, which uses scooters and mopeds to cater to the peak demand for grocery and perform passenger rides during off-peak hours to offset the costs. As transport technology improves, we can expect these rides to be undertaken by self-driving automatons that will further reduce the cost and improve reliability. The future of e-grocery As online grocery businesses are using the latest technology to solve supply chain, logistics and inventory management challenges, even small operators are able to scale their businesses through the use of predictive analytics and automation, thanks to the advances in Industry 4.0 technologies. Grocery has always been the problem child of E-commerce — the high demand on operational excellence and the low-margin of the products are a hard sell for many businesses, that even the giants Walmart and Amazon haven’t fully invested in these systems yet. But that’s changing with Industry 4.0 technologies which help the system function with positive unit economics, and this is apparent from the number of e-grocery ventures that have emerged around the world.

Hi-Tech Waves in World of Grocery

tl;dr: Fare lo sviluppatore in Italia è il più delle volte un incubo Fra tutti i report disponibili, nessuno analizza nel dettaglio lo scenario italiano Abbiamo deciso di farlo noi con un questionario che trovi qui: https://goo.gl/eWDECC E’ anonimo, richiede circa 5–8 minuti e i risultati saranno pubblicati entro fine anno L’iniziativa è legata al progetto Jaguar28 : il nostro obiettivo è riscrivere, in meglio, il rapporto fra professionisti del tech ed aziende 😉 Se hai aperto questo articolo poiché indignato dal titolo, inizia dalla parte finale “PRO TIPS / FAQ / DISCLAIMER” e poi leggi il resto Se lavori in ambito tech, quante volte hai già sentito la frase “ ”? E quante volte ti sei sentito d’accordo? Essere uno sviluppatore in Italia fa schifo Il mercato del lavoro per chi si occupa di sviluppo e programmazione in Italia è “famoso” per essere fra i più arretrati in Europa: tutti lo sappiamo e tutti ci rendiamo conto di trovarci di fronte ad un disagio strutturale che può solo peggiorare con il tempo. Il problema non riguarda solo i salari (i più bassi dell’Unione Europea, insieme a Portogallo, Spagna e Grecia) ma più in generale la concezione del ruolo di “developer” all’interno delle aziende. Per ogni storia romanticheggiante di qualche ragazzino californiano che diventa miliardiario grazie ad una app e viene intervistato da tutti i giornali, in Italia ci sono decine di storie molto meno piacevoli su aziende che non conoscono orari, non investono su innovazione e formazione e credono di poter disporre liberamente della vita dei propri collaboratori. L’intramontabile convinzione del “ ” è infatti anche parzialmente accettabile ma perde ogni legittimazione quando il professionista è sbattuto in uno scantinato a fare il refactoring di progetti vecchi di decenni scritti in Fortran (o quasi) e viene considerato come un pericoloso sovverviso quando si prende un permesso per partecipare ad un seminario. i salari in Italia sono più bassi perché la vita è meno cara Per fortuna esistono già alcune startup ed aziende che hanno compreso l’importanza di questi professionisti e che, per quanto possibile, cercano di garantire loro le condizioni più stimolanti: sono ancora casi isolati — in genere in quei contesti con un’elevata competizione per attrarre i migliori talenti — ma da qualche parte dovremmo pur partire. In tutto questo non dobbiamo dimenticare la crescente categoria dei professionisti italiani che vanno a vivere e lavorare all’estero. Troppo spesso vengono liquidati con un “ ”, anche se non se ne conosce l’effettiva situazione. : esistono sviluppatori italiani che vivono all’estero e sono sottopagati anche lì! loro sì che hanno fatto bene ad andarsene, bravi! NOTIZIA SHOCK Ecco quindi spiegato il grande paradosso: oggi sono moltissimi i professionisti che si lamentano della situazione italiana, ma non esiste nessuna vera analisi a tal proposito. Ogni anno sono pubblicate ricerche davvero apprezzabili sul mondo degli sviluppatori (quella di Stack Overflow è in genere la più completa) che però considerano l’Italia un mercato periferico e che non merita di essere valutato nel dettaglio. Ci siamo quindi chiesti: Qual è il salario medio degli sviluppatori italiani? Quali sono le tecnologie che utilizzano, quelle che vorrebbero utilizzare e quelle che invece vorrebbero dimenticare? Quali sono i fattori di soddisfazione ed insoddisfazione in azienda? Per trovare una risposta, abbiamo deciso di lanciare una nostra survey e condividere pubblicamente la fotografia che emergerà. L’obiettivo è creare una coscienza collettiva sull’argomento e provare ad influenzare il cambiamento, mettendo sul tavolo dati concreti ogni volta che un’azienda si chiederà perché non riesce a trovare sviluppatori in gamba. Il questionario è disponibile qui: https://goo.gl/eWDECC Se hai almeno 1 anno di anzianità come developer, sistemista, QA tester, DevOps e ruoli simili, lavori full-time in Italia o sei un italiano all’estero e hai voglia di dedicare 5 minuti del tuo tempo per aiutarci, questo è il questionario che fa per te. Il questionario è totalmente anonimo e non ti chiederemo indirizzi email o informazioni sensibili riconducibili direttamente a te. Entro fine anno pubblicheremo i risultati. Questa iniziativa è legata al progetto : siamo una startup di Torino e il nostro obiettivo è riuscire a riscrivere, in meglio, il rapporto fra professionisti del tech ed aziende. Qualche mese fa abbiamo già pubblicato un per raccontare come vorremmo farlo. Jaguar28 Manifesto Se hai domande, puoi scriverci a hello@jaguar28.com Grazie! MILESTONES: Lancio della survey: 11 luglio 2017, ore 14.00 Risposte inviate nelle prime 24 ore: 254 Visualizzazioni nelle prime 24 ore: 976 🏆_:_ 1000 risposte 14 luglio 2017, ore 14.19 PRO TIPS / FAQ / DISCLAIMER: Se ti va, condividi questo articolo e/o la survey anche con i tuoi contatti! Se vuoi condividerlo nei tuoi gruppi facebook, chiedi prima il permesso agli admin per non farci passare come spam 😅 Il questionario non chiede mai il tuo nome o il tuo indirizzo email, quindi è un database che non potrà mai finire nelle mani di qualche scammer o, peggio, di qualcuno che non conosce la differenza fra Java e Javascript. Sappiamo che, da un punto di vista metodologico, il report è pieno di possibili bias. Non abbiamo tuttavia la presunzione di volerlo presentare all’ONU in seduta plenaria. O forse sì? Stack Overflow ha parlato di alcuni aspetti dello scenario italiano nella Developer Survey del 2016 (% di remote worker, età media, importanza del salario), rispetto ad un campione di circa 1000 profili italiani. Nella Developer Survey 2017, Stack Overflow analizza USA, India, UK, Francia e Germania ma non l’Italia (da cui ha ricevuto circa 900 risposte). Ci è stato detto che l’AICA (Associazione Italiana per l’Informatica e il Calcolo Automatico) distribuisce annualmente un report abbastanza simile. Noi purtroppo non siamo riusciti a reperirlo da nessuna parte. In autunno lanceremo la controparte volta a raccogliere il punto di vista delle aziende tech. Non invieremo i risultati attraverso una newsletter perché sappiamo che la privacy è top concern e non vogliamo lasciare spazio a nessun tipo di insinuazione su come potremmo usare i vostri indirizzi email. I risultati verranno pubblicati con un post su questo profilo e riproposti nei gruppi dove abbiamo già postato questo articolo. Il titolo è volutamente provocatorio (o clickbait, se volete un termine Internet 3.0) per una ragione molto semplice: la maggior parte della dev community italiana si attiva solo quando il flame è alle porte. : il titolo è una provocazione, non una conclusione. RIPETO Commentare l’articolo e/o l’iniziativa solo in base al titolo senza averne letto per intero il contenuto equivale a condividere con toni indignati i link dei siti di fake news. Se il tuo sesto senso ti fa percepire una truffa, una fonte di speculazione o una cospirazione degli Illuminati non sei obbligato a completare il questionario né a condividere pubblicamente le tue paranoie. Se la cosa ti rende più sereno: sì, siamo Rettiliani e tu ci hai scoperto.

Essere uno sviluppatore in Italia fa schifo

Many of you probably know that one of the most crucial steps in running a successful startup company is to define the right minimum viable product, simply known as MVP. Developing an MVP even after conducting initial market tests can be challenging, as the version of your product is essentially an unvalidated hypothesis. It's common for developers to waste time and resources on MVPs that are not well-defined. This guide will help you identify the core features of your product, enabling you to avoid wasting time and money. To define your MVP, you must envision the first instance of your product and find a working method on how to showcase its best possible features in a simple and brief way. Let’s take a look at the 7-step guide on how you can turn your product idea into a successful startup and appeal to early adopters. How to Find Your MVP MVP is not a beta version of your product designed to determine bugs. It is also not an idea written down on a piece of paper or presented in a couple of slides. Your MVP is intended to test market demand assumptions, refine the product vision, and help you choose where to focus future development efforts. MVPs are therefore a very effective strategy for determining your product-market fit. So, what should be your first few moves when turning your idea into a viable product? 1. Your Idea: The Basics Let’s start with the basics. What is the ground idea of your perfect product, and what is your reality at the moment? At this stage, your priority is to analyze the problem that you are trying to solve. Describe the size of your problem, and provide all the necessary statistics and data – how does this problem impact your user? How does it impact your product idea? Are there any existing products that can successfully solve the same type of problem? Collect as much evidence as possible – it can help you massively in achieving success. Do not forget to keep it well-structured. For instance, let’s take a look at Uber. In the past, getting a taxi could be rather annoying when you would have to wait at a curb in an attempt to catch a car. Uber appealed to those who wanted the ability to rapidly call a cab by simply pulling out their smartphone, making a couple of clicks in a user-friendly app, and therefore, solving the initial problem and pain point completely. 2. Understand Your Users During this stage, you need to analyze the current state of the market, identify your target audience, and study your competitors. This will help you understand how your product can stand out among others and what features are important to your product users. Here, your product team must ensure they clearly know who their users are. Identify each class (or type) of the user, their needs, wants, pain points, expectations, experience, and other metrics you find useful in your case. The final step of your research should be to draft the so-called success criteria – the standard to judge whether an objective has been achieved – for each type of product user. Once you have identified your users, it’s time to learn if you can actually understand them. Collect information about their behavior, their profiles, and habits – of course, by complying with the data collection laws in your country. Get as much knowledge and publicly accessible insights into who your users are. You can also carry out a few interviews, and create surveys or focus groups to further deepen your research. 3. What Is Your Big Picture Product? What would your product look like if you had all the possible resources, time, and investment? What is the ideal state of your product? It is recommended that you list down all the valid ideas collected from your team that could help your startup reach its final stage of development. Moreover, you can also collect feedback from your users. To help you think big, you can answer the following questions: What major issue are you aiming to address? What ambitions do you have for your product? Which user requirements are you trying to meet? What challenges faced by users are you aiming to overcome? What tasks require improvement, efficiency, or innovation that your product will accomplish? 4. What Is Your Minimum Product? Now that you have got an idea regarding your ideal product, your next step is to define what would be enough for your product to work with the least amount of resources, time, and investment. Identify your best interactions with the users and gather a list of top-priority features that would be able to provide just enough value. The question you must answer before you move on to the next step is ‘What is the single thing that your product absolutely must do to fulfill its promises?’  For instance, if your goal is to create a trustworthy and efficient communication platform, you might say that your product must deliver seamless and secure communication between users without compromising privacy or data integrity. 5. What Is Your Viable Product? Gather your team’s and users’ feedback – it’s time to generate ideas! It might take a couple of brainstorming sessions, but your end goal is to ensure you have captured a few high-potential thoughts. Here are several questions you might want to answer at this stage: Do you have the necessary skills, expertise, time, resources, and funding? Do you have sufficient knowledge and resources to deliver value solely to early adopters? Have you conducted thorough user research and identified potential early adopters? Do you have mechanisms in place to gather user feedback and collect data on user interactions and contexts? Can you handle product scalability? Can your MVP cope with the potential surge in user activity? Once you have collected all the necessary data, it is time to review and combine your ideas in order to define your MVP draft, which should show the way your product solves the problems you indicated during the first few stages of research and with what features this result is reached. 6. Define Your MVP Put together the results of the above steps and define your minimum viable product. Keep in mind that defining your MVP means that you already have your product vision on a much larger scale. Do not make the mistake of many beginner product managers who do not have a sufficient definition of what their ‘full product’ should be. One way you can define your minimum viable product is by scanning the backlog of your ‘full product’ and highlighting the best features, which are expected to help make the product user happy, satisfied, and keep the user engaged. You can divide these features into sets and highlight the exact function this or that set plays in the product vision. Now that you have your feature sets defined, find the subset that can provide the minimum viability. Carefully analyze each case of user stories – a description of a software system written from the perspective of an end-user or system user – in terms of cost, value, importance, and so on. Rank your user stories from top to least priority. Your team must make their business judgment call and apply their product sense to see where the thin line between top and least priority cases is. Your top priority user story might be the exact MVP you’ve been searching for. There is another important point to make here: until you have at least a couple of paying customers, it is never a good idea to pour money into developing your own unique software. Remember that the market is already full of existing tools you can use to define your MVP, like using a third-party API. 7. Innovate Your MVP Obviously, your work does not stop here. It could take a few tries to define the right MVP, but it is worth the time and the effort. Your MVP must reflect what you build, why you build, for whom you build, and when you build. You have to continue your business journey with systematic feedback, new integrations, innovative features, and so on – while your MVP becomes the base, or the vessel, for your experiments. Conclusion Let’s summarize all the ideas pinpointed in the article so that you can start working on your MVP right away. Define the problem you want to solve. Decide whether it is worth solving and if there are existing products on the market that can already solve this exact problem successfully. Step 1. Analyze your users by applying different tactics like surveys, focus groups, interviews, etc. Step 2. Define what your full product should look like. Collect feedback from your team and first adopters. Step 3. Create a list of top-priority features that your product must have in order to carry out its basic functionalities. Step 4. Generate, combine, and sort ideas on the viability of your product – how it works, what its features are, and how you are planning to scale the product in the future. Create an MVP draft. Step 5. Define an MVP by finding your best user stories. Use existing tools to create an MVP before you start pouring investments into developing your own in-house software. Step 6. Continue to generate feedback, and innovate your MVP. Step 7.

Step-by-Step Guide on How to Define Your Startup MVP and Save Time and Money

আপনার স্টার্টআপ এমভিপি কীভাবে সংজ্ঞায়িত করবেন এবং সময় এবং অর্থ সাশ্রয় করবেন সে সম্পর্কে ধাপে ধাপে নির্দেশিকা

Schritt-für-Schritt-Anleitung zum Definieren Ihres Startup-MVP und zum Sparen von Zeit und Geld

Guía paso a paso sobre cómo definir el MVP de su startup y ahorrar tiempo y dinero

Guide étape par étape sur la façon de définir votre MVP de démarrage et d'économiser du temps et de l'argent

अपने स्टार्टअप एमवीपी को कैसे परिभाषित करें और समय और पैसा कैसे बचाएं, इस पर चरण-दर-चरण मार्गदर्शिका

スタートアップの MVP を定義して時間とコストを節約する方法のステップバイステップガイド

스타트업 MVP를 정의하고 시간과 비용을 절약하는 방법에 대한 단계별 가이드

Guia passo a passo sobre como definir seu MVP de inicialização e economizar tempo e dinheiro

Пошаговое руководство о том, как определить MVP вашего стартапа и сэкономить время и деньги

Startup MVP'nizi Nasıl Tanımlayacağınız ve Zaman ve Paradan Tasarruf Etme Konusunda Adım Adım Kılavuz

Hướng dẫn từng bước về cách xác định MVP khởi nghiệp của bạn và tiết kiệm thời gian và tiền bạc

如何定义你的创业公司 MVP 并节省时间和金钱的分步指南

The ability to gather information from images has profound business potential. And, well, it can also just be fun. In this article, I’ll outline how we used to rapidly build an image object detection feature for a social media startup. Amazon Rekognition What Is Image Object Detection? Simply put, image object detection is the process of detecting and extracting information about entities in a given image. This involves detecting objects, activities, places, animals, products, etc. Image object detection has a wide array of use cases across a variety of industries. Major sectors such as banks, insurance, social media, dating apps, news agencies, and FinTech use object detection in some form or another. Our Use Case Recently, we were tasked with building an image object detection feature for a social media startup. The use case was simple — users should be able to select some of their favorite photos and submit them to be featured on one of the startup’s social media pages. The social media marketing team needed a way to search through the image submissions for certain themes — such as photos of the ocean, popular landmarks, animals, music concerts, etc. Image Analysis Using Deep Learning Analyzing images and classifying them based on scenery and objects within the image is no simple task. Human sight is nothing short of remarkable, and building an application that’s able to replicate the brain’s ability to detect objects is immensely complex. There is an entire computer vision industry devoted to doing just that. Performing object detection from scratch is typically a multi-step process that involves: Collecting images and labeling the objects within Training the ML models Using the trained models to perform the analysis Performance tuning And, repeat… Our aim for this feature, like all others on the project, was to build it quickly and test its efficacy in a production environment as soon as possible. Furthermore, we didn’t want to devote development resources to building a solution from the ground up when we could leverage existing cloud services. Cue Serverless — the startup’s entire backend is fully Serverless and event-driven. With this architecture, we’re able to have teams of developers that only need to focus on features that the social media app from others. Serverless also enables us to build highly scalable services whilst also only paying for exactly what we use — an important consideration for a scaling startup. differentiate So to achieve this feature, we used Amazon Rekognition — a fully Serverless image and video analysis service. Using Rekognition, we were able to develop this complex and critical workflow in a matter of . Let’s dive into it. hours What Is Amazon Rekognition? Amazon Rekognition is an AWS Serverless offering that uses deep learning to perform image and video analysis. Being fully Serverless means that with Rekognition we don’t need to worry about the complexity of the underlying infrastructure; we pay only for what we use it provides us with pre-written software for image and video analysis tasks. Rekognition offers a range of features, including image label detection, face detection, celebrity detection, content moderation, and text detection. and The best part? Rekognition abstracts away the heavy lifting of building, training, and analyzing deep learning models. Image and video analysis is quick and simple, with minimal set-up necessary. We didn’t need to worry about building and training our own datasets and provisioning server capacity so that our service would scale. All we needed to worry about was integrating. Architecture The architecture is straightforward. Our mobile app uploads images from users’ phones into an S3 bucket. The upload to S3 then triggers a Lambda function which in turn calls the Rekognition API and stores the results in DynamoDB for querying. Serverless image object detection architecture diagram using AWS The Code Writing code is fun, right? Well, writing less code is even more fun. Rekognition exposes a set of APIs that you send image data to which perform the analysis and return the results. For our use case, we used the API. detect-labels A simplified version of our Serverless framework Infrastructure as Code file looks like this: //serverless.yamlfunctions:
  imageLabelDetection:
    handler: image-label-detection.handler
      events:
        - s3:
          bucket: my-image-bucket
          event: s3:ObjectCreated:*
          existing: true
      iamRoleStatements:
        - Effect: Allow
          Action: rekognition:DetectLabels
          Resource: "*"
        - Effect: Allow
          Action: s3:GetObject
          Resource: arn:aws:s3:::my-image-bucket Our Lambda code simply calls the Rekognition API and stores the results in DynamoDB, but you can use whatever makes sense for your use case. We obtain the S3 bucket name and the image’s object name from the S3 event and pass those into the function of the . detectLabels Rekognition SDK We also pass in two optional parameters (MaxLabels and MinConfidence) to specify the confidence level threshold and a maximum number of labels that we want returned. In the example below, we will only get up to 20 labels in the response, and all labels will have a confidence level of more than 80%. //image-label-detecion.jsconst AWS = require("aws-sdk");const rekognition = new AWS.Rekognition();exports.handler = async (event) => {
  const imageDetails = event.Records[0].s3;  const bucketName = imageDetails.bucket.name;
  const objectKey = imageDetails.object.key;  const rekognitionResp = await rekognition
    .detectLabels({
      Image: {
        S3Object: {
          Bucket: bucketName,
          Name: objectKey,
        },
      },
      MaxLabels: 20,
      MinConfidence: 80,
    })
    .promise();  // Send to data store, e.g. DynamoDB
  // ...
}; Who doesn’t love a picture of a dog? Below is a response for an image that we uploaded to our S3 bucket. As you can see, Rekognition correctly determines that it’s an image of a dog on an outdoor, gravel path (and tells us where in the image the dog is!). Rekognition response (left), uploaded image (right). Final Thoughts So, what are my thoughts after using Rekognition in production for a few months? Here is a list of key takeaways: Fully Serverless — Avoid the undifferentiated heavy lifting of managing underlying infrastructure complexity and use pre-built software and pre-trained models. Performance — Rekognition is quick, around 600ms in most cases. Easy to use — The API is simple and easy to integrate with. Continuous improvement — AWS is constantly maintaining and iterating on Rekognition meaning it will become increasingly better over time. Accuracy — Rekognition takes advantage of Amazon’s broad set of customers; resulting in highly accurate image label detection. Has a generous free tier, so you can trial before deciding. Cost — We pay only for what we use. Rekognition allows us to scale up and down based on business needs. Summary TLDR: Rekognition enabled us to rapidly build an image object detection feature that’s accurate, fast, and scalable.

Using Amazon Rekognition to Build an Image Object Detection Feature for a Social Media Startup

সোশ্যাল মিডিয়া স্টার্টআপের জন্য একটি ইমেজ অবজেক্ট ডিটেকশন ফিচার তৈরি করতে অ্যামাজন রেকগনিশন ব্যবহার করা

Mithilfe von Amazon Rekognition eine Funktion zur Bildobjekterkennung für ein Social-Media-Startup erstellen

Uso de Amazon Rekognition para crear una función de detección de objetos de imagen para una startup de redes sociales

Utilisation d'Amazon Rekognition pour créer une fonctionnalité de détection d'objets image pour une startup de médias sociaux

सोशल मीडिया स्टार्टअप के लिए इमेज ऑब्जेक्ट डिटेक्शन फीचर बनाने के लिए अमेज़न रेकोग्निशन का उपयोग करना

Amazon Rekognition を使用してソーシャルメディアスタートアップ向けの画像オブジェクト検出機能を構築する

Amazon Rekognition을 사용하여 소셜 미디어 스타트업을 위한 이미지 객체 감지 기능 구축

Usando o Amazon Rekognition para criar um recurso de detecção de objetos de imagem para uma startup de mídia social

Использование Amazon Rekognition для создания функции обнаружения объектов изображений для стартапа в социальных сетях

Bir Sosyal Medya Kuruluşu için Görüntü Nesnesi Algılama Özelliği Oluşturmak İçin Amazon Rekognition'ı Kullanma

Sử dụng Amazon Rekognition để xây dựng tính năng phát hiện đối tượng hình ảnh cho một công ty khởi nghiệp trên mạng xã hội

使用 Amazon Rekognition 为社交媒体初创公司构建图像对象检测功能

OKRs stand for . Objectives and Key Results Peter Drucker invented them in 1954 as MBO, . Intel used them first. Then John Doerr, once at Intel, brought them to Google. Larry Page and Sergey Brin, Google's founders, learned from him. Management by Objectives An OKR has two parts: the objective, your goal, and the key results, what success looks like. You should limit key results to five per objective. OKRs aim for an outcome, not just an output. For example, don't just say, "Launch app x." Instead, define why you're launching it. What change do you seek? That's your true objective. Remember this: if you're stuck, ask "Why?" Why are we doing this? The answer often reveals the real goal. Measuring success is crucial. Vague goals like "increase engagement" don't help. Be specific. Say, "boost retention by 5%." That's measurable. OKRs fit everywhere, from big-picture strategies to specific product developments. They link high-level vision with day-to-day tactics. In product-driven companies, they align all levels of operation. Step By Step Plan Let’s say you’re a founder of a startup, and you’ve just hit traction. First of all, congratulations on reaching this pivotal moment in your startup's journey. As you navigate this new phase of expansion and complexity, it's crucial to align your team's efforts and maintain focus on what truly matters for sustained growth. This is where OKRs become vital. What follows is a step-by-step guide designed to help you implement OKRs effectively. This guide will cover setting clear objectives, defining measurable key results, and ensuring these OKRs resonate at every level of your organization. It's tailored to maintain the agility and innovation of your startup while scaling operations efficiently. Let's dive in! Day 1: Identify Strategic Gaps & Formulate Key Hypotheses Morning: Identify strategic gaps. Begin by reviewing current performance data and long-term goals. Engage in discussions with department leads to uncover areas where the company's current trajectory diverges from its objectives. This process is essential to pinpoint the specific challenges and opportunities the company must address. It grounds the OKR process in reality, ensuring that objectives are both ambitious and relevant. : What are the primary gaps between our current performance and our long-term goals? Question Afternoon: Develop key hypotheses. In the afternoon, transition into formulating hypotheses that address the identified gaps. These hypotheses should propose potential reasons for the gaps and suggest ways to bridge them. This step is crucial for directing the focus of your strategy. It provides a basis for the 'how might we' questions and subsequent steps, ensuring that the strategy development is hypothesis-driven and targeted. : What hypotheses can we formulate to address these strategic gaps? Question Day 2: Define 'How Might We' Questions & Brainstorm Strategic Options Morning: Transform hypotheses into 'how might we' questions. Transform the previously developed hypotheses into open-ended 'how might we' questions. This approach promotes creative thinking and opens up a range of potential solutions. These questions should challenge the team to think beyond conventional solutions, fostering innovation and broad strategic thinking. : What 'how might we' questions emerge from our key hypotheses? Question Afternoon: Brainstorm WTP/HTW options. Spend the afternoon brainstorming possible Where-to-Play/How-to-Win (WTP/HTW) choices to address the 'how might we' questions. This session should be expansive, encouraging ideas from all corners of the business. The goal is to explore a wide array of strategic options, ensuring that the final strategy is not only robust but also innovative. : What strategic options can we brainstorm to answer these 'how might we' questions? Question Day 3: Determine Barriers & Refine Hypotheses Morning: Identify barriers for each strategic option. Identify what could hinder the success of each WTP/HTW option. This involves analyzing potential risks, market conditions, and internal capabilities. Understanding these barriers is key to making informed strategic decisions and anticipating challenges that might arise in the implementation of these strategies. : What are the potential barriers to our strategic choices? Question Afternoon: Refine Hypotheses. Instead of rushing into tests, use these two days to further refine your hypotheses based on initial feedback and deeper analysis. This refinement should involve critical examination of the hypotheses in light of available data, industry trends, and internal capabilities. The aim is to ensure that the hypotheses are not only relevant and testable but also practical and aligned with your strategic goals. : How have we refined our hypotheses based on identified barriers and feedback? Question Day 4: Update strategy & Establish KPIs Morning: Update strategy based on analysis. Use the insights gained from the morning's analysis to refine your strategy. This may involve selecting specific WTP/HTW options or revising your initial hypotheses. The refinement process is essential for ensuring that the strategy is aligned with the empirical data and is adaptable to the insights gained. : How have we updated our strategy based on the refined hypotheses and barrier analysis? Question Afternoon: Establish KPIs. Begin defining clear Key Performance. This step is vital for setting measurable and specific targets that align with your objectives. This is the constellation of big and small metrics that you’ll be watching or driving in order for the strategy to work, and that will show that it is working. : What KPIs have we established for the updated strategy? Question Day 5: Develop Strategic Objectives Gather key leadership team members to develop 3-5 high-level, company-wide objectives. These should be ambitious, aligning with the company's long-term vision, and should be a direct outcome of the previous days' work. This step is crucial for setting a clear, strategic direction for the entire company. : What are the 3-5 strategic objectives we've developed? Question Day 6: Communicate Objectives & Tackle Questions Morning: Communicate objectives company-wide. Present the defined objectives to all employees, clearly articulating their significance and how they align with the company's broader vision. Effective communication is key to ensuring buy-in and understanding across the organization, which is essential for successful implementation. Afternoon: Q&A Sessions Hold sessions where employees can ask questions to gain clarity and deeper understanding of the company OKRs. : What are the key concerns addressed in the Q&A sessions? Question Day 7: Departmental Breakdown of Objectives & Develop Key Results Morning: Departmental workshops Each department should hold workshops facilitated by department heads or OKR champions. The aim is to interpret how company OKRs translate into departmental objectives.This step ensures that each department understands its role in achieving the broader objectives, fostering a sense of ownership and clarity of purpose. : How have departments translated company OKRs into departmental objectives? Question Afternoon: Develop key results for each department. Develop 3-5 key results for each departmental objective. These should be quantifiable and challenging, yet attainable. This stage is crucial for creating a clear roadmap for each department, outlining how their efforts contribute to the company's overall objectives. : What key results have departments established? Question Day 8: Establish Tracking Mechanism & Mid-Quarter Check-In Planning Morning: Set up tracking tool and train team members. Select and implement a tool for tracking progress on key results. Provide training to ensure that all team members can effectively use the tool. This step is essential for maintaining visibility on progress and ensuring accountability. : What tracking tool have we implemented and how was the team trained? Question Afternoon: Plan for mid-quarter & end of quarter check-in. Organize the logistics and agenda for a mid-quarter review meeting. This meeting should be designed to review progress, identify any barriers, and make necessary adjustments. Planning for this check-in is important for keeping the team on track and adapting to any unforeseen challenges. Establish channels for feedback on the OKR process at all levels. : What is our approach for the mid-quarter and end-of-quarter check-ins? Question Day 50 (of the quarter): Plan Next Quarter’s OKRs Begin Setting OKRs for Next Quarter Start the process of setting objectives and key results for the next quarter, using learnings and feedback from the current quarter. This forward-planning ensures continuous strategic alignment and improvement. Day 60:  Continuous Improvement Session Conduct a Session to Refine the OKR Process Gather insights on the OKR process implementation and discuss ways to refine and improve for future cycles. This reflection is crucial for continuous improvement, ensuring that the OKR process remains effective and relevant to the company's evolving needs. Conclusion By implementing OKRs, your Startup can not only navigate the challenges of growth more effectively but also instill a sense of purpose and accountability among team members. As you implement OKRs, remember that the journey is as crucial as the destination; adaptability and iteration are key. In doing so, you'll empower your team to reach new heights and sustain long-term success in the dynamic landscape of the business world.

A Step-By-Step Guide For Implementing OKRs In Your Scaling Startup

আপনার স্কেলিং স্টার্টআপে ওকেআর বাস্তবায়নের জন্য একটি ধাপে ধাপে নির্দেশিকা

Eine Schritt-für-Schritt-Anleitung zur Implementierung von OKRs in Ihrem Skalierungs-Startup

Una guía paso a paso para implementar OKR en su startup en expansión

Un guide étape par étape pour la mise en œuvre des OKR dans votre startup évolutive

आपके स्केलिंग स्टार्टअप में ओकेआर लागू करने के लिए चरण-दर-चरण मार्गदर्शिका

スケーリングスタートアップに OKR を実装するためのステップバイステップガイド

스케일링 스타트업에서 OKR을 구현하기 위한 단계별 가이드

Um guia passo a passo para implementar OKRs em sua startup de escalonamento

Пошаговое руководство по внедрению OKR в масштабируемый стартап

Ölçeklendirme Girişiminizde OKR'leri Uygulamak İçin Adım Adım Kılavuz

Hướng dẫn từng bước để triển khai OKR khi khởi nghiệp mở rộng quy mô của bạn

在规模化初创公司中实施 OKR 的分步指南

Beyond Login: Implement Fine-Grained Authorization With ZITADEL

লগইন এর বাইরে: ZITADEL এর সাথে সূক্ষ্ম-দানাযুক্ত অনুমোদন বাস্তবায়ন করুন

Über die Anmeldung hinaus: Implementieren Sie eine differenzierte Autorisierung mit ZITADEL

Más allá del inicio de sesión: implemente una autorización detallada con ZITADEL

Au-delà de la connexion : implémentez une autorisation détaillée avec ZITADEL

लॉगिन से परे: ZITADEL के साथ बढ़िया प्राधिकरण लागू करें

ログインを超えて: ZITADEL を使用したきめ細かい認証の実装

로그인 그 이상: ZITADEL로 세분화된 인증 구현

Além do login: implemente autorização refinada com ZITADEL

Помимо входа в систему: реализация детальной авторизации с помощью ZITADEL

Giriş Yapmanın Ötesinde: ZITADEL ile Ayrıntılı Yetkilendirmeyi Uygulayın

Ngoài đăng nhập: Triển khai ủy quyền chi tiết với ZITADEL

超越登录：使用 ZITADEL 实施细粒度授权

Let’s shed light on some eye-opening analysis that helps you to know how even tech giants like Google, Microsoft, and Amazon have encountered their fair share of failures in the realm of product startups. Join me as we delve into the intriguing stories behind ten such startups, uncovering the lessons they hold for aspiring entrepreneurs and innovation enthusiasts. Google's Product Startups Well, there is no need to introduce Google. Am I right? But did you know that while having endless successful apps, there is a long list of Google failed products? Yes, you read it correctly. The search engine giant has had its fingers in numerous pies, but not all turned out to be the sweet successes they were hoping for. Google Failed Projects List: Google Buzz Google Answer Google Notebook Google Wave Google Buzz One of Google's most notable failures is , a social networking, microblogging, and messaging tool integrated into their web-based email program, Gmail. Google introduced Google Buzz in 2010. Google Buzz Why did Google Buzz Fail? Google Buzz faced intense criticism for its privacy settings, often revealing users' email contacts without permission. This led to lawsuits and public backlash, tarnishing Google's reputation and ultimately resulting in the shutdown of Google Buzz in 2011. Google Answers Google Answers is another name in the failed startups list. Google attempted to create a platform where users could ask and answer questions for a fee. It launched in 2002. Why did Google Answers Fail? Despite having a unique concept that was supposed to prove useful for all parties, Google Answers struggled to attract a consistent user base. The need to pay for answers when free alternatives like Yahoo Answers and Quora were available hindered its adoption, leading to its discontinuation in 2006. Google Notebook was designed to save and organize notes and information from the web. It was launched in 2006 and shut down in 2012. Google Notebook Why did Google Notebook Fail? Google Notebook's failure can be attributed to several factors that set it apart from the success of Evernote. One key reason was Google Notebook's lack of robust platform support, making it inconvenient for users who needed to access their notes across different devices. In contrast, Evernote capitalized on this need by providing seamless synchronization and support for various platforms, like smartphones, tablets, and computers. Additionally, Evernote's user-friendly interface and features like voice recordings made it a more versatile tool, addressing real-user requirements. The failure of Google Notebook is its inability to adapt to evolving user habits, and its lack of consistent updates allowed Evernote to gain a competitive edge. Google Wave , later known as Apache, aimed to revolutionize communication by combining email, messaging, and collaborative editing in one platform. Despite its innovative approach, it couldn't find its footing. It was launched in 2009 and discontinued within just one year. Google Wave Why did Google Wave Fail? Google Wave was ahead of its time, and its complex interface made it difficult for users to grasp its full potential. Additionally, Google's marketing and product positioning didn't effectively convey its value. These factors contributed to its demise. Apart from these three Google failed products, there are many more like Google Glass, Google Reader, Google Nexus, Google+, Google Allo, Google Inbox, Google Hangouts, Google Play Music, Google Video Player, etc. Microsoft's Product Startups Moving on to Microsoft's foray into startups, let's explore some of its products that failed to make the impact they were aiming for. Microsoft Failed Products List: MS Zune Windows Me Microsoft Bob MS Zune When we are talking about Microsoft's failed products, is the name that hits the list. MS Zune The Microsoft Zune was a line of portable media players and digital media services developed by Microsoft as a competitor to Apple's iPod and iTunes ecosystem. The Zune brand included hardware devices and an associated software platform for purchasing and managing digital content. It was launched in 2006 and survived till 2011. What is the Reason Behind Zune's Failure? Microsoft Zune faced failure due to various factors. Its late entry into a market dominated by the iPod and its lack of substantial innovation posed challenges. Zune struggled to establish a compelling ecosystem like iTunes, leading to limited market impact. Compatibility issues, less polished software, and a less recognizable brand perception further hindered its adoption. Microsoft's focus on hardware rather than a comprehensive ecosystem also contributed to its downfall. Windows Me , short for Windows Millennium Edition, was a Windows operating system version. Despite its release in the year 2000, it failed to achieve widespread acceptance and acclaim and ended in 2003, followed by extended support by July 2006. Windows Me Why Didn’t Windows Me Face Success? Windows Me was plagued with stability and compatibility issues. It was perceived as a stopgap solution between Windows 98 and Windows XP, and its lack of significant improvements led to its underwhelming performance. Microsoft Bob is the other name on the list when someone searches for failed products of Microsoft. While it aimed to make the interface of Windows 3.1x, Windows 95, and Windows NT more user-friendly by introducing a cartoon-like interface, this attempt at simplification didn't resonate with users. Its lifespan is just one year i.e., from 1995 to early 1996. Microsoft Bob Why did Microsoft Bob Fail? Undeniably, the idea of making computing more accessible was noble. However, the execution in Microsoft Bob missed the mark. Users found the interface cumbersome and counterintuitive, leading to its quick demise. If you think Microsoft has only these three failed product startups, you are wrong. The list is very long, which includes Kin, Microsoft Portrait, Microsoft Lumia Smartphones, MSN, MSN TV, Microsoft Surface RT, Windows 8, Windows Vista, Microsoft Office Assistant (Clippy), Microsoft Internet Explorer 6, Microsoft Groove Music, TerraServer, Microsoft Band, etc. Amazon's Product Startups Lastly, let's explore the list of Amazon's failed products that face downfall because of several reasons: Amazon Failed Products List: Fire Phone Amazon Auctions Amazon Destinations Fire Phone The was a smartphone developed by Amazon, which aimed to integrate tightly with Amazon's ecosystem of products and services. It was Amazon's first attempt at creating a smartphone and was launched with a lot of anticipation in 2014. However, it did not achieve the level of success that Amazon had hoped for. Amazon Fire Phone This Phone was officially launched on July 25, 2014, and discontinued after a relatively short period of availability. It was reported that Amazon discontinued the Fire Phone in August 2015, just over a year after its initial release. Reason Behind the Failure of Fire Phone The Amazon Fire Phone failed due to a combination of factors. Its high initial pricing put it against established flagship phones. Unique features like the 3D display and Firefly seemed gimmicky rather than useful. The limited app ecosystem, absence of Google Play Store, and reliance on Amazon's Appstore made it less appealing. Consumers' loyalty to existing brands and the late entry into a market dominated by Apple and Samsung hindered adoption. Additionally, the exclusive partnership with AT&T limited its availability, while marketing failed to communicate its value compared to competitors. Amazon Auction Amazon Auctions was an online auction platform launched by Amazon.com in March 1999. It was introduced as a way for individuals and businesses to list and sell items through auction-style listings, similar to platforms like eBay. The service allowed sellers to create their listings and buyers to bid on items, with the highest bidder winning the item. Amazon Auctions was discontinued in September 2000, after a relatively short lifespan of about a year and a half. The service faced challenges and did not gain the same level of popularity as eBay, which was already an established player in the online auction market. Causes of Failure Amazon Auction faced an uphill battle due to eBay's stronghold on the online auction market. The lack of unique features and differentiation hindered its ability to attract users away from eBay's platform. Amazon Destinations Amazon Destinations was a travel booking service launched by Amazon in 2015. It aimed to offer users hotel and travel packages in specific regions, focusing on local getaways and nearby destinations. However, the service was quietly discontinued in October 2015, just a few months after its launch. Why did Amazon Destinations Fail? The main reason behind its failure was likely the intense competition in the online travel industry, dominated by established players like Expedia and Booking.com. Additionally, Amazon faced challenges in establishing itself as a credible and trusted platform in the travel sector, which led to limited adoption and ultimately contributed to the decision to discontinue the service. Well, the list does not end here; there are endless Amazon products that have failed, such as Amazon Restaurants, Amazon Wallet, Dash Buttons, Pets.com, Myhabit.com, LiveBid.com, BuyVIP, and much more. Lessons Learned Well, in my 20+ years of experience, I saw many startups that could not establish themselves as big players in their industries. And if I am talking about Google, Amazon, and Microsoft, they have diversified their portfolios and revenue streams across various sectors and services. This diversity acts as a buffer, allowing them to absorb failures or setbacks in one area without severely affecting their financial health. On the other hand, startups, often focusing on a specific niche or product, have a more concentrated risk. A single failure or misstep can significantly affect their limited resources and market position. Unlike industry giants, startups might not have the financial cushion to recover easily from such setbacks. For startups, it's important to manage risks wisely, diversify revenue streams where possible, foster innovation and adaptability, and make strategic decisions based on a solid understanding of market dynamics. While the road to becoming a big player in an industry is challenging, careful planning and an unwavering commitment to their vision can set startups on a path to long-term success.

10 Failed Startup Product Examples by Google, Microsoft and Amazon

Google, Microsoft এবং Amazon-এর 10টি ব্যর্থ স্টার্টআপ পণ্যের উদাহরণ৷

10 Beispiele für gescheiterte Startup-Produkte von Google, Microsoft und Amazon

10 ejemplos de productos de inicio fallidos de Google, Microsoft y Amazon

10 exemples de produits de démarrage ayant échoué par Google, Microsoft et Amazon

Google, Microsoft और Amazon द्वारा 10 असफल स्टार्टअप उत्पाद उदाहरण

Google、Microsoft、Amazon による 10 の失敗したスタートアップ製品の例

Google, Microsoft, Amazon의 실패한 스타트업 제품 사례 10가지

10 exemplos de produtos de inicialização com falha do Google, Microsoft e Amazon

Google, Microsoft ve Amazon'dan 10 Başarısız Başlangıç Ürünü Örneği

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