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Master DW

Data and Analytics Tutorial

Data and Analytics Overview
Under Construction

Data and Analytics Success

Data and Analytics Strategy
Project Management
Data Analytics Methodology
Quick Wins
Data Science Methodology

Requirements

BI Requirements Workshop

Architecture and Design

Architecture Patterns
Technical Architecture
Data Attributes
Data Modeling Basics
Dimensional Data Models

Enterprise Information Management

Data Governance
Metadata
Data Quality

Data Stores and Structures

Data Sources
Database Choices
Big Data
Atomic Warehouse
Dimensional Warehouse
Logical Data Warehouse
Data Lake
Operational Datastore (ODS)
Data Vault
Data Science Sandbox
Flat Files Data
Graph Databases
Time Series Data

Data Integration

Data Pipeline
Change Data Capture
Extract Transform Load
ETL Tool Selection
Data Warehoouse Automation
Data Wrangling
Data Science Workflow

BI and Data Visualization

BI - Business Intelligence
Data Viaulization

Data Science

Statistics
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics

Test and Deploy

Testing
Security Architecture
Desaster Recovery
Rollout
Sustaining DW/BI

Logical Data Warehouse / Virtual Data Warehouse

A Logical Data Warehouse (LDW) is a data warehouse that is implemented through Data Virtualization middleware rather than physically copying data. Data Virtualization is a technology that provides a consistent interface to data that is located in multiple, heterogeneous data stores. It provides a level of data abstraction which makes the data appear to users to be a single, large data store. The end-user does not need to know where data is physically stored or what type of database technology contains it.

Data Virtualization is a powerful technology which improves read access to data – such as:

  • Fast Time to Market: enables rapid PoC and prototyping.
  • Virtual Database: creates small virtual databases to support individual microservices.
  • Logical Data Warehouse: brings in data from multiple sources – combining data warehouses and big data.
  • Virtual Data Mart: provides a subset view into existing data warehouses and databases.
  • Analytical Sandbox: supports data exploration.
  • Cloud Source: avoids copying excessive data to or from the cloud or between cloud platforms.
  • ETL Source: makes ETL independent of specific data sources and abstracts data transformation.
  • Transactional Updates: performs updates of underlying data stores.
The Logical Data Warehouse implemented using Data Virtualization is an effective way of delivering data to consumers for Data and Analytics.

Logical Data Warehouse


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