Duration
35 hours (usually 5 days including breaks)
Overview
Day 1 – provides a high-level overview of essential Big Data topic areas. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise.
Day 2 – explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead 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.
Day 3 – provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used.
Day 4 – builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security.
Day 5 – covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4.
Course Outline
Day 1 – Fundamental Big Data
- Understanding Big Data
- Fundamental Terminology & Concepts
- Big Data Business & Technology Drivers
- Traditional Enterprise Technologies Related to Big Data
- Characteristics of Data in Big Data Environments
- Dataset Types in Big Data Environments
- Fundamental Analysis and Analytics
- Machine Learning Types
- Business Intelligence & Big Data
- Data Visualization & Big Data
- Big Data Adoption & Planning Considerations
Day 2 – Big Data Analysis & Technology Concepts
- Big Data Analysis Lifecycle (from business case evaluation to data analysis and visualization)
- A/B Testing, Correlation
- Regression, Heat Maps
- Time Series Analysis
- Network Analysis
- Spatial Data Analysis
- Classification, Clustering
- Outlier Detection
- Filtering (including collaborative filtering & content-based filtering)
- Natural Language Processing
- Sentiment Analysis, Text Analytics
- File Systems & Distributed File Systems, NoSQL
- Distributed & Parallel Data Processing,
- Processing Workloads, Clusters
- Cloud Computing & Big Data
- Foundational Big Data Technology Mechanisms
Day 3 – Fundamental Big Data Architecture
- New Big Data Mechanisms, including …
- Security Engine
- Cluster Manager
- Data Governance Manager
- Visualization Engine
- Productivity Portal
- Data Processing Architectural Models, including …
- Shared-Everything and Shared-Nothing Architectures
- Enterprise Data Warehouse and Big Data Integration Approaches, including …
- Series
- Parallel
- Big Data Appliance
- Data Virtualization
- Architectural Big Data Environments, including …
- ETL
- Analytics Engine
- Application Enrichment
- Cloud Computing & Big Data Architectural Considerations, including …
- how Cloud Delivery and Deployment Models can be used to host and process Big Data Solutions
Day 4 – Advanced Big Data Architecture
- Big Data Solution Architectural Layers including …
- Data Sources,
- Data Ingress and Storage,
- Event Stream Processing and Complex Event Processing,
- Egress,
- Visualization and Utilization,
- Big Data Architecture and Security,
- Maintenance and Governance
- Big Data Solution Design Patterns, including …
- Patterns pertaining to Data Ingress,
- Data Wrangling,
- Data Storage,
- Data Processing,
- Data Analysis,
- Data Egress,
- Data Visualization
- Big Data Architectural Compound Patterns
Day 5 – Big Data Architecture Lab
- Incorporates a set of detailed exercises that require delegates to solve various inter-related problems, with the goal of fostering a comprehensive understanding of how different data architecture technologies, mechanisms and techniques can be applied to solve problems in Big Data environments.