ETL, OLAP, Star schema

Interactive Data Warehousing

Master ETL processes, OLAP operations, dimensional modeling, and data warehouse architecture

Data Warehousing Fundamentals

Understanding the core concepts of modern data warehousing

ETL Processes

Extract, Transform, Load

Learn how to extract data from various sources, transform it according to business rules, and load it into the data warehouse.

  • Data extraction techniques
  • Transformation rules
  • Loading strategies
  • Error handling

OLAP Operations

Online Analytical Processing

Explore multidimensional data analysis with drill-down, roll-up, slice, and dice operations.

  • Multidimensional analysis
  • Drill-down & Roll-up
  • Slice & Dice operations
  • Pivot operations

Star Schema

Dimensional Modeling

Design efficient star schemas with fact tables at the center and dimension tables around them.

  • Fact tables design
  • Dimension tables
  • Primary & Foreign keys
  • Query optimization

Snowflake Schema

Normalized Dimensions

Understand normalized dimensional modeling where dimension tables are further normalized.

  • Normalized dimensions
  • Storage optimization
  • Complex relationships
  • Query performance

Interactive Data Warehousing Workspace

Hands-on experience with ETL, OLAP, and dimensional modeling

ETL Process simulator

Watch data flow through the Extract, Transform, and Load pipeline

Extract

Source Systems

Transform

Business Rules

Load

Data Warehouse

OLAP Cube Analyzer

Perform multidimensional analysis on sample sales data

OLAP Operations:
Analysis Results:

Select a dimension and perform OLAP operations to see results

Dimensional Schema Designer

Design and visualize star and snowflake schemas

Data Warehouse SQL Editor
Query Results
Execution Time: 0ms Rows: 0

Executing query...

Execute a query to see results here

Data Warehouse Performance Dashboard

Monitor key performance indicators and metrics

2.5TB
Data Volume
45ms
Avg Query Time
156
ETL Jobs/Day
99.2%
Data Quality

Data Warehouse Architectures

Compare different approaches to data warehouse design

Architecture Description Advantages Disadvantages Best For
Traditional DW Centralized repository with ETL processes Mature, stable, well-understood Rigid, expensive, slow to change Structured reporting, compliance
Data Lake Store raw data in native format Flexible, cost-effective, handles big data Data governance challenges Big data analytics, ML
Data Lakehouse Combines data lake and warehouse benefits Flexible + structured, ACID transactions Complex implementation Modern analytics, real-time
Cloud DW Cloud-native data warehouse solutions Scalable, managed, pay-as-you-go Vendor lock-in, data transfer costs Scalable analytics, startups