Data Warehouse - The Ultimate Guide

Explore data warehousing principles and concepts through practical demonstrations and optional hands-on assignments, reinforcing theory with tool-agnostic examples and quick, accessible setup.

See how data marts sit atop the core data warehouse as an additional layer, modeling data dimensionally with facts and dimensions for specific use cases.

Relational databases store data in tables and use SQL to query them, using primary and foreign keys and joins. This enables OLAP and data warehouses with star schemas.

In-memory databases deliver high query performance for analytical use cases and data marts by storing data in memory and using columnar storage and parallel query plans.

Explore olap cubes, a multidimensional molap approach that pre-calculates values for fast analytics in data marts, using dimensions, hierarchies, and mdx to drill, slice, and dice data.

Explore operational data storage and distinguish the ODS from a data warehouse, emphasizing current state data, near real-time updates, and operational decision making.

Dimensional modeling aims for fast data retrieval by structuring data into dimension and fact tables, reducing duplication and boosting performance and usability for OLAP reporting.

Explore dimensions in data warehousing, learning how they provide context to facts in a star schema through slicing and dicing, with dimension tables, keys, and snowflake concepts.

Explore the star schema as the core data warehouse model, linking a fact table to dimension tables via primary and foreign keys to enable one-to-many relationships and efficient queries.

This lecture explains year-to-date calculations in data warehouses and why storing them breaks the defined grain; compute YTD in Power BI and Tableau using daily values.

Identify the business process and four key decisions for designing fact tables. Define a fine, atomic grain and identify relevant dimensions and facts for flexible analysis.

Understand the difference between natural and surrogate keys and how surrogate keys are created in ETL, then apply them to fact and dimension tables for better storage and joins.

Follow a case study to design fact tables in a data warehouse, analyzing shopping cart checkout, orders, and fulfillment for an e-commerce company to optimize profits.

Define the grain of the fact table using the atomic, transactional level where one row represents a single item order line, preserving dimensionality and analytical value.

Identify additive facts that comply with the grain, derive absolute discounts and profit, and design the final fact table with website, customer, product, and data dimensions.

Explore dimension tables in data warehousing, using surrogate keys, lookup tables, and left joins to link fact and product dimensions, enabling slicing and grouping by attributes.

Explore how to set up a date dimension in a data warehouse using SQL in PostgreSQL, including creating a table, primary key, index, and populating data for reuse across warehouses.

Explains degenerate dimensions as dimension keys in the fact table with no associated dimension and no foreign key, enabling analysis and grouping by payment and order ids with dd suffix.

Explore junk dimensions in dimensional modeling, transforming scattered flags and indicators from a fact table into a low-cardinality dimension to improve query performance and usability.

Explore the ETL process: extract from data sources, transform and load into a data warehouse, using staging, core, and data mart layers and scheduled workflows.

Extract data from sources into the transient staging layer, then apply initial and delta loads to transform and move data into the core layer via SQL tables.

Explore the load workflow in a data warehouse, from staging to core, using delta detection and insert or update operations to append data while preserving history with a current/deleted flag.

Install the Pentaho community edition, download and unzip the package, and run the Windows batch file to launch Spoon, ensuring Java is installed for PostgresQL in the next lecture.

Transform data to create a consolidated view for analysis by integrating data from multiple systems, standardizing data types and column names, reshaping for analytical needs, and loading into core layer.

Implement the ETL plan by extracting from the product table, applying delta logic, and cleaning staging data. Load transformed data into the core schema after adding a surrogate key.

Set up the core schema and staging table in PG admin and configure a Spoon ETL workflow to load dim_product into staging before moving to the core layer.

Evaluate your data integration needs to choose the right ETL tool by defining requirements, considering connectors and cost, reviewing tools, and using a weighted scoring matrix before testing.

Set up the staging sales fact table, the core sales table, and the payment dimension in pgAdmin, with a surrogate-key sequence, and prepare the ETL workflow for the data warehouse.

Connect the SetLastLoad and GetLastLoad transformations in the staging job, configure sales variables, and fix data type mismatches to ensure the staging table loads numeric cost data.