International Big Data Professional Certificate Certification (BDPC)

International Big Data Professional Certificate Certification (BDPC)

Módulos

Modulo I: Integridad de Big Data

  • Objetivos de Aprendizaje
  • Integridad
  • Contexto de Big Data
  • Dos Niveles de Big Data 
  • CASELET: IBM Watson
  • Alcance de Big Data
  • Las 4 V?s de Big Data
  • Volumen, Velocidad, Variedad y Veracidad
  • Aplicaciones de Big Data 
  • Gestión de Big Data 
  • Ecosistema de Big Data
  • Analizando Big Data 
  • Tablero en Tiempo Real 
  • Resumen de Desafíos y Soluciones
  • Comparación de Tradicionales y Big Data
  • Preguntas de Revisión 
  • Ejercicio Práctico de Liberty Stores: Paso B1

  • Objetivos de Aprendizaje
  • Ecosistema/Arquitectura de Big Data
  • Aplicación de Google Flu
  • Fuentes de Big Data Sources
  • Comunicaciones Entre Personas
  • Comunicaciones Entre Personas y Máquinas
  • Comunicaciones Máquina a Máquina
  • Aplicaciones de Big Data
  • Monitoreo de Sensibilidad del Consumidor
  • Aplicaciones de Big Data
  • Aplicación de Vigilancia Predictiva 
  • Aplicaciones de Big Data 
  • Seguro de Automóvil Flexible - Preguntas de Revisión 
  • Ejercicio Práctico de Liberty Stores: Paso B2

  • Objetivos de Aprendizaje
  • Arquitectura de Google Query
  • Ecosistema / Arquitectura de Big Data
  • Capas en Arquitectura de Big Data 
  • Arquitectura IBM Watson 
  • Arquitectura de Netflix
  • Arquitectura de VMWare 
  • Arquitectura de una Compañía Meteorológica 
  • Arquitectura de Ticketmaster
  • Arquitectura de LinkedIn
  • Arquitectura de PayPal
  • Ecosistema de Hadoop 
  • Ejercicio práctico de Liberty Stores: Paso B3

  • Objetivos de Aprendizaje
  • Ecosistema / Arquitectura de Big Data
  • Hadoop y MapReduce Definidos
  • ¿Por qué la computación en Clúster?
  • Arquitectura de Hadoop: Fragmentación de Datos 
  • Arquitectura Maestro-Esclavo 
  • Arquitectura de Lectura y Escritura del Sistema de Archivos Distribuidos Hadoop (HDFS)
  • Características de HDFS
  • Instalando HDFS
  • Yet Another Resource Negotiator (YARN)
  • Preguntas de Revisión

  • Objetivos de Aprendizaje
  • Arquitectura de Big Data 
  • Arquitectura de MapReduce
  • Arquitectura Maestro-Esclavo en MapReduce
  • Papel de MapReduce 2004
  • Secuencia de MapReduce
  • MR Funciona como una Secuencia de UNIX
  • Contador de Palabras Usando MapReduce
  • Conteo de Palabras Usando MapReduce ? Ejemplo 2
  • Seudo Código MapR para Contador de Palabras
  • Ejemplo de Contador de Palabras (Inglés): Myfile.txt 
  • Resultados de Cada Segmento
  • Resultados Agrupados de Map Operations 
  • Resultados Luego de la Fase de Reducción
  • Pig vs Hive
  • Lenguaje de Hive
  • Arquitectura de Lenguaje Pig

  • Objetivos de Aprendizaje
  • Arquitectura de Big Data
  • Bases de Datos NoSQL
  • NoSQL vs RDBMS
  • Teorema CAP 
  • Arquitectura NoSQL 
  • Tipos de Bases de Datos NoSQL
  • Arquitecturas NoSQL Populares
  • Procesos de Cassandra
  • Lenguajes de Acceso NoSQL
  • Hive - Lenguajes de Acceso NoSQL
  • Pig
  • Preguntas de Revisión

  • Objetivos de Aprendizaje
  • Arquitectura de Big Data
  • Computación Definida en Stream
  • Conceptos de Streaming
  • Aplicaciones de Streaming
  • Características del Algoritmo de Streaming
  • Filtro Bloom - Apache Spark para Computación Streaming 
  • Ecosistemas de Código Abierto - Arquitectura de Apache Spark 
  • Spark vs Hadoop 
  • Conjuntos de Datos Distribuidos Resilientes de Spark (RDD) 
  • Mecanismo de Procesamiento Spark 
  • Código de Spark para Pagerank

  • Objetivos de Aprendizaje
  • Arquitectura de Big Data
  • Sistema de Ingestión de Datos
  • Sistemas de Mensajería
  • Arquitectura de Apache Kafka
  • Componentes de Kafka
  • Mecanismo de Tópicos de Kafka
  • Atributos Clave de Kafka

  • Objetivos de Aprendizaje
  • Arquitectura de Big Data
  • Computación en la Nube
  • Modelo de Acceso a la Computación en la Nube
  • Computación en la Nube como una Infraestructura Virtualizada
  • Beneficios de la Computación en la Nube
  • Modelos de Computación en la Nube por Propiedad y Rango de Servicios

  • Objetivos de Aprendizaje
  • Arquitectura de Web-Analyzer
  • Tecnología
  • Código de Aplicación  

The Big Data Professional Certificate (BDPC) course is designed to train participants in the foundations and applications of Big Data, addressing concepts such as 4 V’s (volume, speed, variety and veracity), the Hadoop, Spark, NSQL and cloud computer ecosystem.

This course prepares participants to obtain international certification:

Big Data Professional Certificate (BDPC).

Under the practical Learning Method approach, participants will work on laboratories, practical workshops and/or real projects, ensuring the effective application of knowledge acquired in business environments.

At the end of the course, participants will be able to:

  • Understand the Big Data ecosystem, including its main challenges and commercial benefits.
  • Differentiate Big data from conventional data, understanding the characteristics of the 4 v? S.
  • Apply key architectures and tools, such as Hadoop, Spark and Nosql.
  • Implement parallel data processing, using mapreduce and Apache Spark.
  • Integrate NSOQL databases into Big Data environments, understanding its architecture and applications.
  • Implement cloud computer models for mass processing and storage.

Obtain the Big Data Professional Certificate Certification (BDPC).

To participate in this training, attendees must meet the following requirements:

  • Basic knowledge of computer science.

International Big Data Professional Certificate Certification (BDPC) Applies
International Big Data Professional Certificate Certification (BDPC) 24 hours

Learning Methodology

The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.

The instructor (live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.

The methodology seeks that the student does not memorize, but rather understands the concepts and how they are applied in a work environment.

As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.

Conditions to guarantee successful results:
  • a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
  • b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
  • c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.

Pre-enrollment

You do not need to pay to pre-enroll. By pre-enrolling, you reserve a spot in the group for this course or program. Our team will contact you to complete your enrollment.

Pre-enroll now

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Description

The Big Data Professional Certificate (BDPC) course is designed to train participants in the foundations and applications of Big Data, addressing concepts such as 4 V’s (volume, speed, variety and veracity), the Hadoop, Spark, NSQL and cloud computer ecosystem.

This course prepares participants to obtain international certification:

Big Data Professional Certificate (BDPC).

Under the practical Learning Method approach, participants will work on laboratories, practical workshops and/or real projects, ensuring the effective application of knowledge acquired in business environments.

Objectives

At the end of the course, participants will be able to:

  • Understand the Big Data ecosystem, including its main challenges and commercial benefits.
  • Differentiate Big data from conventional data, understanding the characteristics of the 4 v? S.
  • Apply key architectures and tools, such as Hadoop, Spark and Nosql.
  • Implement parallel data processing, using mapreduce and Apache Spark.
  • Integrate NSOQL databases into Big Data environments, understanding its architecture and applications.
  • Implement cloud computer models for mass processing and storage.

Obtain the Big Data Professional Certificate Certification (BDPC).

To participate in this training, attendees must meet the following requirements:

  • Basic knowledge of computer science.

offers

International Big Data Professional Certificate Certification (BDPC) Applies
International Big Data Professional Certificate Certification (BDPC) 24 hours

Learning Methodology

The learning methodology, regardless of the modality (in-person or remote), is based on the development of workshops or labs that lead to the construction of a project, emulating real activities in a company.

The instructor(live), a professional with extensive experience in work environments related to the topics covered, acts as a workshop leader, guiding students' practice through knowledge transfer processes, applying the concepts of the proposed syllabus to the project.

La metodología persigue que el estudiante "does not memorize", but rather "understands" the concepts and how they are applied in a work environment."

As a result of this work, at the end of the training the student will have gained real experience, will be prepared for work and to pass an interview, a technical test, and/or achieve higher scores on international certification exams.

Conditions to guarantee successful results:
  • a. An institution that requires the application of the model through organization, logistics, and strict control over the activities to be carried out by the participants in each training session.
  • b. An instructor located anywhere in the world, who has the required in-depth knowledge, expertise, experience, and outstanding values, ensuring a very high-level knowledge transfer.
  • c. A committed student, with the space, time, and attention required by the training process, and the willingness to focus on understanding how concepts are applied in a work environment, and not memorizing concepts just to take an exam.

Pre-enrollment

You do not need to pay to pre-enroll. By pre-enrolling, you reserve a spot in the group for this course or program. Our team will contact you to complete your enrollment.

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