Big Data Engineer Add-on

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We are currently planning the workshops for this course. You may purchase an eLearning kit for immediate access or contact us for further details.



USD $566.29 $283.14 without Exam Voucher

Have you completed any other Big Data course from our selection? If so, this upgrade kit will allow you to extend your knowledge without having to repeat the first two modules.

What is Big Data? It is data that your current systems and techniques are unable to process. Processing this data requires new techniques that also adds to the processing and storage demands of your IT systems. In this 3-module course you’ll learn the patterns, mechanisms and approaches that will enable you to Engineer Big Data solutions.


 

Intended for
Intended for architects and IT Professionals that are responsible for the engineering Big Data solutions as well as Solution Architects, Enterprise Architects, Data Architects, BI Specialists.


Learning Outcomes

A comprehensive understanding of the approaches, design patterns and the mechanisms that are essential for architecting Big Data systems. Some of the topics covered include:

  • Big Data Storage Terminologies (including sharding, replication, CAP theorem, ACID, BASE)
  • Big Data Storage Requirements
  • Introduction to NoSQL – NewSQL
  • NoSQL Database Types (including key-value, document, column-family and graph databases)
  • Big Data Processing Requirements
  • MapReduce Explained (including map, combine, partition, shuffle and sort, and reduce)
  • Advanced Big Data Engineering Mechanisms (including serialization & compression engines)
  • In-Memory Storage Devices, In-Memory Data Grids & In-Memory Databases
  • Read-Through, Read-Ahead, Write-Through & Write-Behind Integration Approaches
  • Polyglot Persistence (including Explanation, Issues & Recommendations)
  • Realtime Big Data Processing Concepts (including Speed Consistency Volume (SCV), Event Stream Processing (ESP) & Complex Event Processing (CEP))
  • General Realtime Big Data Processing & Realtime Big Data Processing & MapReduce
  • Bulk Synchronous Parallel (BSP) Processing Engine & BSP vs. MapReduce
  • Big Data Solutions (including Characteristics, Design Considerations & Design Process)

And more…


 The elearning kit is accessible for one year from date of purchase. In the Details tab you can find more information about this eLearning Kit.

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.

Module 7 Fundamental Big Data Engineering

This course explores introductory topics pertaining to the field of developing data processing solutions–data engineering–in the context of Big Data environments. Specifically it covers concepts, techniques and technologies related to the processing and storage of Big Data datasets including MapReduce and NoSQL. It highlights the unique challenges faced when processing and storing Big Data datasets. The MapReduce data processing engine, which is the de facto framework for batch processing of large amounts of data, is also explained in detail.

The following primary topics are covered:

  • Big Data Engineering – Big Data Engineering Challenges
  • Big Data Storage Terminologies (including sharding, replication, CAP theorem, ACID, BASE)
  • Big Data Storage Requirements
  • On-Disk Storage (including distributed file system – databases)
  • Introduction to NoSQL – NewSQL
  • NoSQL Rationale – Characteristics
  • NoSQL Database Types (including key-value, document, column-family and graph databases)
  • Big Data Processing Requirements
  • Big Data Processing (including batch mode and realtime mode)
  • Introduction to MapReduce for Big Data Processing (batch mode)
  • MapReduce Explained (including map, combine, partition, shuffle and sort, and reduce)

Module 8 Advanced Big Data Engineering

This course module builds upon Module 7 by exploring advanced topics pertaining 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. It presents further considerations for developing 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 developing Big Data processing solutions are also explored.

The following primary topics are covered:
– Advanced Big Data Engineering Mechanisms (including serialization & compression engines)
– In-Memory Storage Devices, In-Memory Data Grids & In-Memory Databases
– Read-Through, Read-Ahead, Write-Through & Write-Behind Integration Approaches
– Polyglot Persistence (including Explanation, Issues & Recommendations)
– Realtime Big Data Processing Concepts (including Speed Consistency Volume (SCV), Event Stream Processing (ESP) & Complex Event Processing (CEP))
– General Realtime Big Data Processing & Realtime Big Data Processing & MapReduce
– Advanced MapReduce Algorithm Design
– Bulk Synchronous Parallel (BSP) Processing Engine & BSP vs. MapReduce
– Graph Data & Graph Data Processing using BSP
– Big Data Pipelines (including Definition and Stages)
– Big Data with Extract-Load-Transform (ELT)
– Big Data Solutions (including Characteristics, Design Considerations & Design Process)

Module 9 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.


 Exams & Certification

  • Those that pass the exam required for the Certified Big Data Engineer designation will receive official certificate for this designation and will have access to the benefits associated with this certification.
  • The exam can be taken from the comfort of your home or office via Pearson VUE Online Proctoring

 

Note: All quoted pricing is excluding GST. For customers in Australia GST will be added during the check-out process.



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