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Taming Big Data con MapReduce y Hadoop – Hands On!

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Taming Big Data con MapReduce y Hadoop – Hands On!. MapReduce aprender rápido mediante la construcción de más de 10 ejemplos reales, usando Python, MRJob, y el Servicio de MapReduce elástico de Amazon.

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No te pierdas este fabuloso curso online llamado Taming Big Data con MapReduce y Hadoop – Hands On!. Es 100% online y comenzarás justo en el momento de matricularte. Tú serás el que marques tu propio ritmo de aprendizaje.

Breve descripción del curso llamado Taming Big Data con MapReduce y Hadoop – Hands On!

MapReduce aprender rápido mediante la construcción de más de 10 ejemplos reales, usando Python, MRJob, y el Servicio de MapReduce elástico de Amazon.

El profesor de este fabuloso curso 100% online es Frank Kane, un auténtico experto en la materia, y con el que aprenderás todo lo necesario para ser más competitivo. El curso se ofrece en Inglés.

Descripción completa del curso llamado Taming Big Data con MapReduce y Hadoop – Hands On!

Course Description “Big data” analysis is a hot and highly valuable skill – and this course will teach you two technologies fundamental to big data quickly: MapReduce and Hadoop. Ever wonder how Google manages to analyze the entire Internet on a continual basis? You’ll learn those same techniques, using your own Windows system right at home. Learn and master the art of framing data analysis problems as MapReduce problems through over 10 hands-on examples, and then scale them up to run on cloud computing services in this course. You’ll be learning from an ex-engineer and senior manager from Amazon and IMDb. Learn the concepts of MapReduce Run MapReduce jobs quickly using Python and MRJob Translate complex analysis problems into multi-stage MapReduce jobs Scale up to larger data sets using Amazon’s Elastic MapReduce service Understand how Hadoop distributes MapReduce across computing clusters Learn about other Hadoop technologies, like Hive, Pig, and Spark By the end of this course, you’ll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. We’ll have some fun along the way. You’ll get warmed up with some simple examples of using MapReduce to analyze movie ratings data and text in a book. Once you’ve got the basics under your belt, we’ll move to some more complex and interesting tasks. We’ll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We’ll analyze a social graph of superheroes, and learn who the most “popular” superhero is – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You’ll find the answer. This course is very hands-on; you’ll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. Over 5 hours of video content is included, with over 10 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Hadoop-based technologies, including Hive, Pig, and the very hot Spark framework – complete with a working example in Spark. Don’t take my word for it – check out some of our unsolicited reviews from real students: “I have gone through many courses on map reduce; this is undoubtedly the best, way at the top.” “This is one of the best courses I have ever seen since 4 years passed I am using Udemy for courses.” “The best hands on course on MapReduce and Python. I really like the run it yourself approach in this course. Everything is well organized, and the lecturer is top notch.”

Información adicional

Profesor

Frank Kane

Lecciones

52

Duración

5

Nivel

Todos

Idioma

Inglés

Incluye

Acceso de por vida <br/> Devolución a los 30 días garantizada <br/> Disponible en iOS y Android <br/> Certificado de finalización

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