Categories
Search
Time Zone

Big Data Engineer

Options






Workshops not currently scheduled

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 $979.97 $489.98 eLearning Kit only

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 5-module course you’ll learn the patterns, mechanisms and approaches that will enable you to Engineer Big Data solutions.

Get a sample of the contents of this course with this short video lesson that provides a non-technical introduction to Big Data Analysis. This second video lesson is an introduction to Big Data Analytics. Both of these topics are part of Module 1 of the course.

The eLearning kit bundle provides access to a 5 Module course bundle that will be available for 1 year from the date of purchase. The course outline is provided in the Details tab, you can also download a pdf version here. You can also take the on-line exam to get a Digital Badge to show your expertise in this field.


Course contents: 5 Modules

  • Module 1: Fundamental Big Data
  • Module 2: Big Data Analysis & Technology Concepts
  • Module 7: Fundamental Big Data Engineering
  • Module 8: Advanced Big Data Engineering
  • Module 9: Big Data Engioneering Lab

 

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:

  • Fundamental Terminology and Concepts
  • Basic Big Data Analytics
  • Big Data and Traditional Business Intelligence and Data Warehouses
  • Common Analysis and Analytics Techniques
  • Automated Recommendation, Classification, Clustering
  • Machine Language, Natural Language, Semantics
  • Big Data Technology Mechanisms
  • 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 1 Fundamental Big Data

A foundational course that establishes a basic understanding of Big Data from business and technology perspectives, including common benefits, challenges and adoption issues.

The following primary topics are covered: 

  • Fundamental Terminology and Concepts
  • A Brief History of Big Data
  • Business Drivers that Have Led to Big Data Innovations
  • Characteristics of Big Data
  • Benefits of Adopting Big Data
  • Challenges and Limitations of Big Data
  • Basic Big Data Analytics
  • Big Data and Traditional Business Intelligence and Data Warehouses
  • Big Data Visualization
  • Common Adoption Issues
  • Planning for Big Data Initiatives
  • New Roles Introduced by Big Data Projects
  • Emerging Trends

 

Module 2 Big Data Analysis & Technology Concepts

Explores contemporary analysis practices, technologies, and tools for Big Data environments at a conceptual level, focusing on common analysis functions and features of Big Data solutions.

The following primary topics are covered:

  • The Big Data Analysis Lifecycle (from dataset identification to integration, analysis, and visualization)
  • Common Analysis and Analytics Techniques•A/B testing, Regression, Correlation, Text Analytics
  • Sentiment Analysis, Time Series Analysis
  • Network Analysis, Spatial Analysis
  • Automated Recommendation, Classification, Clustering
  • Machine Language, Natural Language, Semantics
  • Data Visualization and Visual Analysis
  • Assessing Hierarchies, Part-to-Whole Relationships
  • Plotting Connections and Relationships, Mapping Geo-Spatial Data
  • Foundational Big Data Technology Mechanisms
  • Big Data Storage (Query Workload, Sharding, Replication, CAP, ACID, BASE)
  • Big Data Processing (Parallel Data Processing, Distributed Data Processing, SharedEverything/Nothing Architecture, SCV)

 

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.



← Go Back
© 2020 Silver Platypus, All Rights Reserved. Web Design Melbourne MeKoo Solutions. Saturday, 05 December 2020
Scroll To Top