International Big Data Professional Certificate Certification (BDPC)

Big Data Professional Course and Certification (BDPC)

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 …

24 hours
Official Certificate
Expert Instructors
Online Learning
Certificación internacional Big Data Professional Certificate (BDPC)
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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.

Certificación internacional Big Data Professional Certificate (BDPC) Applies
Certificación internacional Big Data Professional Certificate (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

Certificación internacional Big Data Professional Certificate (BDPC) Applies
Certificación internacional Big Data Professional Certificate (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.

Course Modules

Module I: Big Data Integrity

  • Learning Objectives
  • Integrity
  • Big Data Context
  • Two Levels of Big Data 
  • CASELET: IBM Watson
  • Scope of Big Data
  • The 4 V's of Big Data
  • Volume, Velocity, Variety, and Veracity
  • Applications of Big Data 
  • Big Data Management 
  • Big Data Ecosystem
  • Analyzing Big Data 
  • Real-Time Dashboard 
  • Summary of Challenges and Solutions
  • Comparison of Traditional and Big Data
  • Review Questions 
  • Practical Exercise of Liberty Stores: Step B1

  • Learning Objectives
  • Big Data Ecosystem/Architecture
  • Application of Google Flu
  • Big Data Sources
  • Communications Between People
  • Communications Between People and Machines
  • Machine to Machine Communications
  • Applications of Big Data
  • Consumer Sensitivity Monitoring
  • Applications of Big Data
  • Predictive Surveillance Application 
  • Applications of Big Data 
  • Flexible Automobile Insurance - Review Questions 
  • Practical Exercise of Liberty Stores: Step B2

  • Learning Objectives
  • Google Query Architecture
  • Big Data Ecosystem / Architecture
  • Layers in Big Data Architecture 
  • IBM Watson Architecture 
  • Netflix Architecture
  • VMWare Architecture 
  • Architecture of a Meteorological Company 
  • Ticketmaster Architecture
  • LinkedIn Architecture
  • PayPal Architecture
  • Hadoop Ecosystem 
  • Practical Exercise of Liberty Stores: Step B3

  • Learning Objectives
  • Big Data Ecosystem / Architecture
  • Hadoop and MapReduce Defined
  • Why Cluster Computing?
  • Hadoop Architecture: Data Fragmentation 
  • Master-Slave Architecture 
  • Hadoop Distributed File System (HDFS) Read and Write Architecture
  • Features of HDFS
  • Installing HDFS
  • Yet Another Resource Negotiator (YARN)
  • Review Questions

  • Learning Objectives
  • Big Data Architecture 
  • MapReduce Architecture
  • Master-Slave Architecture in MapReduce
  • Role of MapReduce 2004
  • MapReduce Sequence
  • MR Works Like a UNIX Sequence
  • Word Count Using MapReduce
  • Word Count Using MapReduce - Example 2
  • MapR Pseudocode for Word Counter
  • Word Counter Example (English): Myfile.txt 
  • Results of Each Segment
  • Grouped Results of Map Operations 
  • Results After the Reduction Phase
  • Pig vs Hive
  • Hive Language
  • Pig Language Architecture

  • Learning Objectives
  • Big Data Architecture
  • NoSQL Databases
  • NoSQL vs RDBMS
  • CAP Theorem 
  • NoSQL Architecture 
  • Types of NoSQL Databases
  • Popular NoSQL Architectures
  • Cassandra Processes
  • NoSQL Access Languages
  • Hive - NoSQL Access Languages
  • Pig
  • Review Questions

  • Learning Objectives
  • Big Data Architecture
  • Stream Defined Computing
  • Streaming Concepts
  • Streaming Applications
  • Characteristics of the Streaming Algorithm
  • Bloom Filter - Apache Spark for Streaming Computing 
  • Open Source Ecosystems - Apache Spark Architecture 
  • Spark vs Hadoop 
  • Resilient Distributed Datasets (RDD) in Spark 
  • Spark Processing Mechanism 
  • Spark Code for Pagerank

  • Learning Objectives
  • Big Data Architecture
  • Data Ingestion System
  • Messaging Systems
  • Apache Kafka Architecture
  • Kafka Components
  • Kafka Topics Mechanism
  • Key Attributes of Kafka

  • Learning Objectives
  • Big Data Architecture
  • Cloud Computing
  • Cloud Computing Access Model
  • Cloud Computing as a Virtualized Infrastructure
  • Benefits of Cloud Computing
  • Cloud Computing Models by Ownership and Service Range

  • Learning Objectives
  • Web-Analyzer Architecture
  • Technology
  • Application Code