Learn Data Warehousing From Scratch- From Solution Architect

Explore data warehousing definitions from Kimball and Inmon, highlighting Kimball's view of warehousing for coding and analysis and Inmon's subject-oriented, integrated, time-variant, and nonvolatile data for decision making.

Understand how the operational data store integrates data from multiple sources with minimal transformation and how the staging area acts as a placeholder before loading to the warehouse.

Compare data warehouses and data marts, highlighting that warehouses handle multiple subject areas while marts focus on a single area, with sources feeding a staging area.

The course provides a quick recap of data warehousing concepts, including etl, staging areas, surrogate keys, fact and dimension tables, denormalization, and slowly changing dimensions.

Explore a typical BI environment from data sources and staging to an enterprise data warehouse, with ETL, data quality, and reporting for analysis.

Explore data warehouse methods by comparing Ralph Kimball's dimensional modeling with William's approach, and learn how a bottom-up design uses sources, staging, and ETL for decision making.

Explore Kimball's bottom-up data warehousing approach, loading data from multiple sources into staging areas and a data warehouse, then building star schemas and data cubes for reporting.

Advocates a top-down approach, building an enterprise data warehouse from integrated subject areas, using staging areas to feed data marts and reporting tools.

Select the business process and decide the data grain to design a dimensional model. Choose dimension tables and define fact tables with metrics and keys for high-performance data access.

Explore how fact tables anchor dimensional models, store numeric additive measurements, and link to dimensions through foreign keys, with sales order data examples.

Understand dimension tables in dimensional modeling, their attribute-driven structure, and their join to fact tables via foreign keys. See examples like product, city, time, and customer dimensions.

Explore slowly changing dimensions, including type 1 overrides, type 2 history with start dates and current flags, and type 3 current versus previous values, plus conformed dimensions.

Explore a data warehousing case study for a multi-store grocery chain, detailing fact and dimension tables and data collection from point of sale and deliveries to optimize restocking and pricing.

Explore how to define the right granularity and dimensions such as date, product, store, and promotion, and build a transaction fact table with surrogate keys for a point-of-sale data warehouse.

The star schema centers a fact table surrounded by dimension tables promotion, store, product, sales rep, and customer, linking measures such as order quantity and extended amount via foreign keys.

Explore the snowflake schema as an extension of the star model, with a fact table and dimensions, and use a calendar table to centralize time attributes.

Explore bus architecture in data warehousing by using conformed dimensions across multiple data marts and shared facts, while avoiding redundant account data.

Extend data warehouse schema with new dimension tables like frequent shopper and time of day, referenced by the fact table via foreign keys. Choose the right grain for future extensibility.

Explore analytics and BI vendors across noSQL options, including key-value, document, and columnar databases, and examine how cloud and on-premise tools shape modern data warehousing.