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The Big Data Engineering course covers essential practices for designing, configuring and utilizing Big Data solutions. It explores Hadoop, MapReduce and various Big Data engineering techniques, including storage models, NoSQL, NewSQL and processing engines. The course further delves into advanced topics related to dataset storage and processing, such as in-memory storage, realtime processing, bulk synchronous parallel processing and graph data processing.

Complete the Big Data Engineering course and, optionally, get accredited as a Certified Big Data Engineer by passing the certification exam. You can purchase the course now and get the exam later, or you can get them together at a discount as part of the Certification Bundle.

Upon completing the course you will receive a digital certificate of completion, as well as a digital training badge from Acclaim/Credly. Because this course encompasses both the Big Data Professional and Big Data Engineer certifications, upon passing the exam you will also receive official Big Data Professional and Big Data Engineer digital accreditation certificates and certification badges from Acclaim/Credly, along with an account that can be used to verify your certification status.

If you already completed the Big Data Professional course modules, you can purchase a partial course (or a partial bundle) with only the modules specific to the Big Data Engineer track here.

The Big Data Engineering course is comprised of the following 5 course modules, each of which has an estimated completion time of 10 hours:

  • Module 1: Fundamental Big Data Science & Analytics
  • Module 2: Big Data Analysis & Technology Concepts
  • Module 11: Fundamental Big Data Engineering
  • Module 12: Advanced Big Data Engineering
  • Module 13: Big Data Engineering Lab

Choose the Certification Bundle to receive the entire course together with the online-proctored certification exam and a set of practice exam questions, all at a bundle discount.

Exam Details

Upon purchasing this course, you will automatically receive access via the Online Interactive eLearning platform. To provide you with the greatest flexibility, you will also have the option to access the course materials via two additional eLearning formats, at no extra cost. All three eLearning formats are briefly described below. A more detailed comparison can be found here.
  1. For everyday learning: An online interactive eLearning platform with individual lessons, as well as interactive and automatically graded exercises and practice questions.
  2. For learning on-the-go: A study kit platform with access to full course documents that support online/offline synching, annotations, comments, custom bookmarks and cross-document searches.
  3. For your reference: A set of printable PDF documents that you can keep (for all course workbooks and posters).
All three forms of access are subject to Arcitura’s *. Upon purchase, access to the online interactive eLearning platform (1) is provided within one business day. Access to the study kits (2) and the PDF documents (3) is provided upon request.

The course is comprised of a set of modules. Each module has a set of lessons and is further supplemented with exercises to help reinforce your understanding of key topics. Shown below are the digital contents and the topic outline for each course module:


Module 1: Fundamental Big Data Science & Analytics

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


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Interactive Exercises
  • Mind Map Poster

  • Symbol Legend Poster
  • Patterns and Mechanisms Poster
  • Practice Exam Questions
  • PDFs of Workbook and Posters (printable)

Topics 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

Module 2: Big Data Analysis & Technology Concepts

This course module explores a range of the most relevant topics that pertain to contemporary analysis practices, technologies and tools for Big Data environments. The module 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.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Interactive Exercises

  • Mind Map Poster
  • Supplement
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • 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

Module 11: Fundamental Big Data Engineering

This course module 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.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Interactive Exercises

  • Mind Map Poster
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • 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 Parallelism, Data Parallelism

Module 12: Advanced Big Data Engineering

This course module builds upon Module 11 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.


Course Module Contents


  • Workbook Lessons (100+ pages)
  • Video Lessons (for all topics)
  • Interactive Exercises

  • Mind Map Poster
  • Practice Exam Questions
  • PDFs of Workbook and Poster (printable)

Topics Covered

  • 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 13: Big Data Engineering Lab

This course module presents participants with a series of exercises and problems that are designed to test their ability to apply their knowledge of topics covered in previous modules. Completing this lab will help highlight areas that require further attention and will help prove proficiency in big data engineering practices as they are applied and combined to solve real-world problems.


Course Module Contents


  • Lab Exercise Booklet
  • Mind Map Poster

  • Practice Exam Questions
  • PDFs of Exercise Booklet and Poster (printable)

Topics Covered

  • Reading Exercise 13.1: CFU Case Study Background
  • Lab Exercise 13.2: Big Data Solution for Achieving Regulatory Compliance
  • Lab Exercise 13.3: Enhancing Risk Analysis Capability
  • Lab Exercise 13.4: Develop Innovative Data Analytics Service
  • Reading Exercise 13.5: TCT Case Study Background

  • Lab Exercise 13.6: Solution for Alleviating Service Delays
  • Lab Exercise 13.7: Solution for Reducing Operational Costs
  • Reading Exercise 13.8: TOB Case Study Background
  • Lab Exercise 13.9: Solution for Handling Increased Website Traffic
  • Lab Exercise 13.10: Analyze Marketing Ad Campaign Data

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Interactive & Graded
Challenges

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  • share a common vocabulary and symbol notation
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