CertificationTraining Badge from Acclaim/Credly. A Certified Big Data Engineer has demonstrated...

®

Big Data Engineer

BIG DATA ENGINEERCertification

Big DataEngineer

C E R T I F I E D

®

Big DataEngineering

T R A I N I N G

®

The Big Data Science Certified Professional (BDSCP) program from Arcitura provides formal education and accreditation programs dedicated to fields of practice associated with Big Data, including analytics and analysis, data science, architecture and engineering.

TABLE OF CONTENTS

Module 9: Big Data Engineering Lab

Module 8: Advanced Big Data Engineering

Module 2: Big Data Analysis &

Technology Concepts

Exam(s)

Module 7: Fundamental Big Data Engineering

Module 1: Fundamental Big Data

Training & Certification

Arcitura Certification Programs

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TRAINING & CERTIFICATIONThe Big Data Engineer track is comprised of BDSCP Modules 1, 2, 7, 8 and 9, the outlines for which are provided in the upcoming pages. The final course module consists of a series of lab exercises that require participants to apply their knowledge of the preceding courses in order to fulfill project requirements and solve real world problems. Completion of these courses as part of a virtual or on-site workshop results in each participant receiving an official digital Certificate of Completion, as well as a digital Training Badge from Acclaim/Credly.

A Certified Big Data Engineer has demonstrated proficiency in designing and utilizing Big Data solutions (using Hadoop, MapReduce and other tools), with an emphasis on Big Data mechanisms used to enable data processing, data storage and the establishment of Big Data pipelines. Depending on the exam format chosen, attaining the Big Data Engineer Certification can require passing a single exam or multiple exams. Those who achieve this certification receive an official digital Certificate of Excellence, as well as a digital Certification Badge from Acclaim/Credly with an account that supports the online verification of certification status.

For more information, visit: www.arcitura.com/bdscp/engineer.

Big Data Engineer

Big DataEngineer

C E R T I F I E D

®

Big DataEngineering

T R A I N I N G

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5Copyright © Arcitura Education Inc. www.arcitura.com

EXAM(S)You can take exams anywhere in the world via Pearson VUE testing centers, Pearson VUE online proctoring and Arcitura on-site exam proctoring at your location.

You are provided with three flexible exam format options:

• Complete Exam B90.BDE, a single combined exam for the entire Big Data Engineercertification track. Recommended for those who want to only take a single exam thatencompasses all course modules within this track.

• Complete the partial version of Exam B90.BDE. Recommended for those who havealready obtained a BDSCP certification and would like to achieve the Big DataEngineer Certification without having to be retested on BDSCP Modules 1 and 2.

• Complete one module-specific exam for each course module in Big Data EngineerCertification track. This is recommended for those who want to progress graduallythrough the track and who would like to be assessed after each course module beforeproceeding to the next.

Visit www.arcitura.com/bdscp/exams for more information. (Note that not all exam formats may be available via all exam delivery options.)

It is recommended that you prepare for the exam(s) by acquiring the Big Data Engineer Certification eLearning kit bundle or the printed Big Data Engineer Certification study kit bundle or by attending an instructor-led workshop that includes BDSCP Modules 1, 2, 7, 8 and 9. The current public workshop calendar can be viewed at www.arcitura.com/workshops. To learn more about having a private workshop delivered at your location, visit www.arcitura.com/private.

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Fundamental Big Data

MORE INFOFor curriculum information, visit www.arcitura.com/bdscp.

MODULE

01 This foundational course provides a high-level overview of essential Big Data topic areas. A basic understanding of Big Data from business and technology perspectives is provided, along with an overview of common benefits, challenges, and adoption issues.

The course content is divided into a series of modular sections, each of which is accompanied by one or more hands-on exercises.

The following primary topics are covered:

• Understanding Big Data• Fundamental Big Data Terminology and Concepts• Big Data Business Drivers and Technology Drivers• Traditional Enterprise Technologies Related to Big Data• OLTP, OLAP, ETL and Data Warehouses in relation to Big Data• Characteristics of Data in Big Data Environments• Dataset Types in Big Data Environments• Structured, Unstructured and Semi-Structured Data• Metadata and Data Veracity• Fundamental Analysis and Analytics• Quantitative and Qualitative Analysis• Machine Learning Types• Descriptive and Diagnostic Analytics• Predictive and Prescriptive Analytics• Business Intelligence and Big Data• Data Visualization and Big Data• Big Data Adoption and Planning Considerations


CONTENTSThis course is available as part of an Arcitura Study Kit in full-color printed and eLearning formats. In addition to the base course materials used during training workshops, additional materials designed for self-study purposes are also included.

• Workbook • Self-Study Guide• Symbol Legend Poster• Mind Map Posters• Flashcards• Big Data Fundamentals Textbook • Audio Tutor Recordings (usb)

eLEARNINGThis course is available via on-line access as part of an eLearning Study Kit.

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Big Data Analysis & Technology Concepts

MODULE

02


This course explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The course content intentionally keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions, as well as a high-level understanding of the back-end components that enable these functions.


• Big Data Analysis Lifecycle (from Business Case Evaluation to Data Analysis and Visualization)

• A/B Testing and Correlation• Regression and Heat Maps• Time Series Analysis• Network Analysis and Spatial Data Analysis• Classification and Clustering• Filtering, including Collaborative Filtering and Content-based Filtering• Sentiment Analysis and Text Analytics• Clusters and Processing Batch and Transactional Workloads• How Cloud Computing relates to Big Data• Foundational Big Data Technology Mechanisms• Big Data Storage Devices and Processing Engines• Resource Managers, Data Transfer Engines and Query Engines• Analytics Engines, Workflow Engines and Coordinate Engines


• Workbook • Self-Study Guide• Supplement• Mind Map Poster• Flashcards• Audio Tutor Recordings (usb)



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Fundamental Big Data Engineering

MODULE

07


This course covers engineering-related concepts, techniques and technologies for the processing and storage of Big Data datasets. It highlights the unique challenges faced when processing and storing large, volatile and disparate sets of data. NoSQL is covered and the MapReduce data processing engine is explained in detail as a base framework for high-volume batch data processing.


• Big Data Engineering Techniques and Challenges• Big Data Storage, including Sharding, Replication, CAP Theorem,

ACID and BASE• Master-Slave, Peer-to-Peer Replication, Combining Replication

with Sharding• Big Data Storage Requirements, Scalability, Redundancy

and Availability• Fast Access, Long-term Storage, Schema-less Storage and

Inexpensive Storage• On-Disk Storage, including Distributed File System and Databases• Introduction to NoSQL and NewSQL• NoSQL Rationale and Characteristics• NoSQL Database Types, including Key-Value, Document,

Column-Family and Graph Databases• Big Data Processing Engines• Distributed/Parallel Data Processing, Schema-less Data Processing• Multi-Workload Support, Linear Scalability and Fault-Tolerance• Big Data Processing Requirements, including Batch, Cluster and

Realtime Modes• MapReduce for Big Data Processing, including Map, Combine,

Partition, Shuffle and Sort and Reduce• MapReduce Algorithm Design• Task Parallism, Data Parallism


• Workbook • Self-Study Guide• Mind Map Poster• Flashcards• Audio Tutor Recordings (usb)



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Advanced Big Data Engineering

This course builds upon Module 7 by exploring advanced engineering topics pertaining primarily to the storage and processing of Big Data datasets. Specifically, it covers advanced Big Data engineering mechanisms, in-memory data storage and realtime data processing.

The course presents further considerations for building MapReduce algorithms and also introduces the Bulk Synchronous Parallel (BSP) processing engine, along with a discussion of graph data processing. The Big Data mechanisms required for developing Big Data pipelines, its stages and the design process involved in building Big Data processing solutions are also explored.


• Advanced Big Data Engineering Mechanisms• Serialization and Compression Engines• In-Memory Storage Devices• In-Memory Data Grids and In-Memory Databases• Read-Through, Read-Ahead, Write-Through and Write-Behind

Integration Approaches• Polyglot Persistence• Explanation, Issues and Recommendations• Realtime Big Data Processing• Speed Consistency Volume (SCV)• Event Stream Processing (ESP)• Complex Event Processing (CEP)• The SCV Principle• General Realtime Big Data Processing and MapReduce• Advanced MapReduce Algorithm Designs• Bulk Synchronous Parallel (BSP) Processing Engine• BSP vs. MapReduce• BSP Synchronous Parallel• Graph Data and Graph Data Processing using BSP (Supersteps)• Big Data Pipelines, including Definition and Stages• Big Data with Extract-Load-Transform (ELT)• Big Data Solution Characteristics, Design Considerations and

Design Process

MODULE

08



• Workbook • Self-Study Guide• Mind Map Poster• Flashcards• Audio Tutor Recordings (usb)



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Big Data Engineering Lab

This course module covers a series of exercises and problems designed to test the participant’s ability to apply knowledge of topics covered previously in course modules 7 and 8. Completing this lab will help highlight areas that require further attention, and will further prove hands-on proficiency in Big Data engineering practices as they are applied and combined to solve real-world problems.

As a hands-on lab, this course incorporates a set of detailed exercises that require participants to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data engineering technologies, mechanisms and techniques can be applied to solve problems in Big Data environments.

For instructor-led delivery of this lab course, the Certified Trainer works closely with participants to ensure that all exercises are carried out completely and accurately. Attendees can voluntarily have exercises reviewed and graded as part of the class completion. For individual completion of this course as part of the Module 9 Study Kit, a number of supplements are provided to help participants carry out exercises with guidance and numerous resource references.

MODULE

09



• Lab Exercises Booklet• Self-Study Guide• Mind Map Poster• Flashcards• Audio Tutor Recording (usb)



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To learn more, visit: www.arcitura.com/nextgen

NEXT-GEN IT ACADEMY CERTIFICATIONS

DEVOPSMODULE 01 Fundamental DevOps

DEVOPSMODULE 02 DevOps in Practice

DEVOPSMODULE 03 DevOps Lab

BLOCKCHAINMODULE 01 Fundamental Blockchain

BLOCKCHAINMODULE 02

Blockchain Technology & Architecture

BLOCKCHAINMODULE 03

Blockchain Technology & Architecture Lab

CertifiedDevOps

Specialist

CertifiedBlockchainArchitect

IoT MODULE 01 Fundamental IoT

IoT MODULE 02 IoT Technology & Architecture

IoT MODULE 03 IoT Technology & Architecture Lab

CertifiedIoT

Architect

CONTAINERIZATION

MODULE 01Fundamental Containerization

CONTAINERIZATION

MODULE 02Containerization Technology & Architecture

CONTAINERIZATION

MODULE 03Containerization Technology & Architecture Lab

CertifiedContainerization

Architect

MACHINELEARNING

MODULE 01Fundamental Machine Learning

MACHINELEARNING

MODULE 02Advanced Machine Learning

MACHINELEARNING

MODULE 03Machine Learning Lab

CertifiedMachineLearningSpecialist

AI MODULE 01 Fundamental Artificial Intelligence

AI MODULE 02 Advanced Artificial Intelligence

AI MODULE 03 Artificial Intelligence Lab

CertifiedArtificial

IntelligenceSpecialist


To learn more, visit: www.arcitura.com/ccp

CLOUD CERTIFIED PROFESSIONAL (CCP) CLOUD SCHOOL

MODULE 03 Cloud Technology Lab

CertifiedCloud

TechnologyProfessional

MODULE 04 Fundamental Cloud Architecture

MODULE 05 Advanced Cloud Architecture

MODULE 06 Cloud Architecture Lab

CertifiedCloud

Architect

CertifiedCloud

Professional*

MODULE 07 Fundamental Cloud Security

MODULE 08 Advanced Cloud Security

MODULE 09 Cloud Security Lab

CertifiedCloud

SecuritySpecialist

MODULE 10 Fundamental Cloud Governance

MODULE 11 Advanced Cloud Governance

MODULE 12 Cloud Governance Lab

CertifiedCloud

GovernanceSpecialist

MODULE 13 Fundamental Cloud Storage

MODULE 14 Advanced Cloud Storage

MODULE 15 Cloud Storage Lab

CertifiedCloud

StorageSpecialist

MODULE 01 Fundamental Cloud Computing

MODULE 16 Fundamental Cloud Virtualization

MODULE 17 Advanced Cloud Virtualization

MODULE 18 Cloud Virtualization Lab

CertifiedCloud

VirtualizationSpecialist

MODULE 02 Cloud Technology Concepts

* The Certified Cloud Professional designation is automatically issued when achieving any other CCP certification. It can also be achieved by receiving passing grades on Exams C90.01 + C90.02.

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To learn more, visit: www.arcitura.com/bdscp

BIG DATA SCIENCE CERTIFIED PROFESSIONAL (BDSCP) BIG DATA SCIENCE SCHOOL

MODULE 01 Fundamental Big Data

MODULE 02 Big Data Analysis & Technology Concepts

CertifiedBig DataScience

Professional

MODULE 03 Big Data Analysis & Technology Lab

MODULE 04 Fundamental Big Data Analysis & Science

MODULE 05 Advanced Big Data Analysis & Science

CertifiedBig DataScientist

CertifiedBig Data

Professional*

MODULE 06 Big Data Analysis & Science Lab

CertifiedBig Data

Consultant

MODULE 07 Fundamental Big Data Engineering

MODULE 08 Advanced Big Data Engineering

CertifiedBig DataEngineer

MODULE 09 Big Data Engineering Lab

MODULE 10 Fundamental Big Data Architecture

MODULE 11 Advanced Big Data Architecture

CertifiedBig DataArchitect

MODULE 12 Big Data Architecture Lab

MODULE 13 Fundamental Big Data Governance

MODULE 14 Advanced Big Data Governance

MODULE 15 Big Data Governance Lab

CertifiedBig Data


* The Certified Big Data Professional designation is automatically issued when achieving any other BDSCP certification. It can also be achieved by receiving passing grades on Exams B90.01 + B90.02.


To learn more, visit: www.arcitura.com/soacp

SOA CERTIFIED PROFESSIONAL (SOACP) SOA SCHOOL

CertifiedSOA

Analyst

MODULE 04 Fundamental SOA Analysis & Modeling w/ Services & Microservices

MODULE 05 Advanced SOA Analysis & Modelingw/ Services & Microservices

MODULE 06 SOA Analysis & Modeling Labw/ Services & Microservices

CertifiedSOA

Architect

MODULE 07 Advanced SOA Design & Architecturew/ Services & Microservices

MODULE 08SOA Design & Architecture Labw/ Services & Microservices

CertifiedMicroservice

Architect

MODULE 10Advanced Microservice Architecture & Containerization

MODULE 11Microservice Architecture & Containerization Lab

CertifiedService

APISpecialist

MODULE 13Advanced Service API Design & Management

MODULE 14Service API Design & Management Lab

CertifiedService


MODULE 15Fundamental Service Governance & Project Delivery

MODULE 16Advanced Service Governance & Project Delivery

MODULE 17 Service Governance & Project Delivery Lab

CertifiedService

TechConsultant

MODULE 09Fundamental Microservice Architecture & Containerization

MODULE 12Fundamental Service API Design & Management

CertifiedServiceSecurity

Specialist

MODULE 18Fundamental Security for Services, Microservices & SOA

MODULE 19Advanced Security for Services, Microservices & SOA

CertifiedSOA

Professional*

MODULE 01Fundamental SOA, Services & Microservices

MODULE 02 Service Technology Concepts

MODULE 03 Design & Architecture w/ SOA, Services & Microservices

* The Certified SOA Professional designation is automatically issued when achieving any other SOACP certification. It can also be achieved by receiving passing grades on Exams S90.01B + S90.02B or S90.01B + S90.03B.

MODULE 20Security Lab for Services, Microservices & SOA

CertificationTraining Badge from Acclaim/Credly. A Certified Big Data Engineer has demonstrated...

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