Data Warehousing & Reporting
Module Overview
Master modern data warehousing architectures and analytics platforms using cloud-native technologies. Build expertise in designing scalable data warehouses, implementing ETL/ELT pipelines, and creating powerful business intelligence dashboards. Learn advanced concepts including big data processing, real-time analytics, and AI-enhanced reporting. Transform your Task Manager application into a comprehensive analytics platform with sophisticated data insights and automated reporting capabilities.
Advanced Concepts
Data Warehouse Architecture & Design
Overview
Master fundamental data warehouse architecture patterns including star schema, snowflake schema, and data vault modeling. Learn to design scalable dimensional models that support complex analytical queries while maintaining performance and data integrity. Develop expertise in modern data lake and lakehouse architectures, understanding when to use each approach for optimal business value and technical efficiency.
Learning Resources
| Course Title | Provider | Description | Level | Mandatory | Action |
|---|---|---|---|---|---|
|
Data Warehousing for Business Intelligence Specialization
|
University of Colorado Boulder
|
Comprehensive specialization covering star/snowflake schemas, OLAP concepts, dimensional modeling, and ETL processes with hands-on projects.
|
Intermediate | Required | Start Learning |
|
Dimensional Modeling Tutorial
|
Bryan Cafferky
|
Practical dimensional modeling techniques covering star schema design, fact tables, dimension tables, and slowly changing dimensions.
|
Beginner | Required | Start Learning |
|
Big Data and Analytics Architecture Guide
|
Google Cloud Architecture Center
|
Comprehensive guide to data platform design patterns covering data lakes vs warehouses, streaming vs batch, and ETL vs ELT patterns.
|
Advanced | Required | Start Learning |
|
Stanford Database Systems Modeling
|
Stanford University
|
Theoretical foundations of database design including relational design theory, normal forms, UML modeling, and dependency theory.
|
Advanced | Required | Start Learning |
|
Data Vault 2.0 Methodology
|
Data Vault Alliance
|
Advanced data modeling methodology for enterprise data warehouses focusing on agility, scalability, and historical data tracking.
|
Advanced | Optional | Start Learning |
|
Lakehouse Architecture Patterns
|
Databricks
|
Modern data architecture combining data lake flexibility with data warehouse performance and reliability features.
|
Advanced | Optional | Start Learning |
Architecture Design Workshop
Task Manager Analytics Data Warehouse Design
- Design comprehensive dimensional model for Task Manager analytics including user behavior, task performance, and system metrics
- Create star schema with fact tables for task events, user sessions, and system performance measurements
- Design dimension tables for users, tasks, projects, time, and geographic data with appropriate hierarchies
- Implement slowly changing dimensions for tracking historical changes in user profiles and project structures
- Develop data governance framework including data lineage, quality rules, and security classifications
- Document architecture decisions and create data dictionary with business glossary
ETL/ELT Pipelines & Data Integration
Overview
Build robust ETL/ELT pipelines using modern cloud data integration platforms including Apache Airflow, Google Cloud Dataflow, and dbt. Master data transformation patterns, error handling, and performance optimization techniques. Learn to implement real-time and batch processing pipelines that scale with business growth while maintaining data quality and reliability through comprehensive monitoring and alerting systems.
Learning Resources
| Course Title | Provider | Description | Level | Mandatory | Action |
|---|---|---|---|---|---|
|
Apache Airflow Complete Course
|
Apache Software Foundation
|
Comprehensive Airflow training covering DAG creation, complex workflows, custom operators, hooks, sensors, and testing strategies.
|
Intermediate | Required | Start Learning |
|
Google Cloud Dataflow & Apache Beam
|
Google Cloud
|
Advanced data processing pipelines using Apache Beam covering PCollections, PTransforms, windowing, triggers, and side inputs.
|
Advanced | Required | Start Learning |
|
Building Production ETL Pipeline
|
GitHub Project
|
End-to-end ETL pipeline from MySQL to BigQuery with Docker containerization, Cloud Run deployment, and Terraform infrastructure.
|
Advanced | Required | Start Learning |
|
dbt (Data Build Tool) Fundamentals
|
dbt Labs
|
Modern ELT tool for data transformation including modeling, testing, documentation, and version control for analytics code.
|
Intermediate | Required | Start Learning |
|
Cloud Composer Documentation
|
Google Cloud
|
Managed Apache Airflow service covering environment setup, DAG creation, GCP service integration, monitoring, and security.
|
Intermediate | Optional | Start Learning |
|
Real-time Streaming with Apache Kafka
|
Confluent
|
Real-time data streaming architecture using Kafka for high-throughput, low-latency data pipelines and event-driven architectures.
|
Advanced | Optional | Start Learning |
Data Pipeline Engineering Project
Task Manager Analytics ETL Pipeline
- Design and implement comprehensive ETL pipeline extracting data from Task Manager operational databases
- Build data transformation layer using dbt to create analytics-ready fact and dimension tables
- Configure Apache Airflow for pipeline orchestration with proper dependency management and error handling
- Implement real-time streaming pipeline for live task updates using Kafka or Google Pub/Sub
- Set up data quality checks, monitoring, and alerting for pipeline failures and data anomalies
- Deploy pipeline to cloud infrastructure with CI/CD automation and infrastructure as code
Cloud Data Warehousing Platforms
Overview
Master leading cloud data warehouse platforms including Google BigQuery, Amazon Redshift, and Snowflake. Learn platform-specific optimization techniques, cost management strategies, and advanced features like machine learning integration. Develop expertise in SQL for analytics, partitioning strategies, clustering optimization, and performance tuning for large-scale analytical workloads.
Learning Resources
| Course Title | Provider | Description | Level | Mandatory | Action |
|---|---|---|---|---|---|
|
BigQuery Complete Documentation
|
Google Cloud
|
Comprehensive BigQuery training covering SQL analytics, partitioning strategies, clustering optimization, cost management, and BigQuery ML workflows.
|
Beginner | Required | Start Learning |
|
Build a Data Warehouse with BigQuery
|
Google Cloud Skills Boost
|
Hands-on BigQuery labs covering joining data, troubleshooting joins, unions, date-partitioned tables, and working with JSON/arrays/structs.
|
Intermediate | Required | Start Learning |
|
BigQuery Colab Notebooks
|
Google Research
|
Hands-on BigQuery analysis with Python integration including BigQuery Python client, pandas integration, and ML workflows.
|
Intermediate | Required | Start Learning |
|
Snowflake Data Warehouse Tutorial
|
Snowflake University
|
Comprehensive Snowflake training covering architecture, virtual warehouses, data sharing, and advanced analytics features.
|
Intermediate | Required | Start Learning |
|
Amazon Redshift Deep Dive
|
AWS Training
|
Advanced Redshift training covering cluster management, performance tuning, data distribution strategies, and Redshift Spectrum.
|
Advanced | Optional | Start Learning |
|
BigQuery Performance Optimization
|
Google Cloud
|
Advanced performance optimization including partitioning, clustering, materialized views, and query optimization techniques.
|
Advanced | Optional | Start Learning |
Cloud Data Warehouse Implementation
Task Manager BigQuery Analytics Platform
- Implement comprehensive BigQuery data warehouse with optimized table partitioning and clustering strategies
- Create analytics SQL queries for complex business intelligence scenarios including time-series analysis
- Implement BigQuery ML models for user behavior prediction and task completion forecasting
- Set up cost optimization strategies including query optimization, slot management, and storage optimization
- Configure BigQuery data governance including row-level security, column-level security, and audit logging
- Build cross-platform comparison by implementing similar functionality on Snowflake or Redshift
Business Intelligence & Data Visualization
Overview
Master modern business intelligence tools including Looker Studio, Tableau, Power BI, and custom visualization frameworks. Learn to design compelling dashboards that drive business decisions through effective data storytelling. Develop expertise in advanced analytics, self-service BI, and embedded analytics while understanding user experience principles for dashboard design and data presentation.
Learning Resources
| Course Title | Provider | Description | Level | Mandatory | Action |
|---|---|---|---|---|---|
|
Looker Studio Complete Guide
|
Google
|
Comprehensive data visualization guide covering data source connections, chart creation, calculated fields, sharing, and collaboration features.
|
Beginner | Required | Start Learning |
|
Looker Studio BI Dashboard Lab
|
Google Cloud Skills Boost
|
Hands-on lab building complete BI dashboard with real-world data including aggregation, scheduled queries, and interactive features.
|
Intermediate | Required | Start Learning |
|
Tableau Public Training
|
Tableau
|
Comprehensive Tableau training covering data connections, advanced visualizations, calculated fields, and dashboard design best practices.
|
Beginner | Required | Start Learning |
|
Power BI Learning Path
|
Microsoft Learn
|
Complete Power BI training covering data modeling, DAX calculations, advanced visualizations, and Power BI Service deployment.
|
Intermediate | Required | Start Learning |
|
D3.js Data Visualization
|
freeCodeCamp
|
Advanced web-based data visualization using D3.js for custom interactive charts and dashboards with JavaScript and SVG.
|
Advanced | Optional | Start Learning |
|
Dashboard Design Best Practices
|
Stephen Few
|
Data visualization design principles focusing on clarity, effectiveness, and user experience for business intelligence dashboards.
|
Intermediate | Optional | Start Learning |
BI Dashboard Development Project
Task Manager Executive Analytics Dashboard
- Design comprehensive executive dashboard using Looker Studio with real-time Task Manager KPIs
- Create advanced analytical visualizations including cohort analysis, funnel analysis, and trend prediction
- Implement interactive filtering, drill-down capabilities, and dynamic date range selection
- Build comparative analysis dashboard using Tableau for advanced statistical visualizations
- Develop mobile-responsive dashboard design with optimized performance for various devices
- Create automated reporting system with scheduled email delivery and alert notifications
Big Data Processing & Modern Data Stack
Overview
Master big data processing technologies including Apache Spark, Hadoop ecosystem, and modern cloud-native data processing services. Learn to design and implement scalable data processing architectures for massive datasets. Develop expertise in stream processing, batch processing, and hybrid architectures while understanding the modern data stack including data lakes, data lakehouses, and real-time analytics platforms.
Learning Resources
| Course Title | Provider | Description | Level | Mandatory | Action |
|---|---|---|---|---|---|
|
Apache Spark Programming
|
Databricks Academy
|
Comprehensive Spark training covering RDDs, DataFrames, Spark SQL, and Structured Streaming for large-scale data processing.
|
Intermediate | Required | Start Learning |
|
Google Cloud Dataproc & Spark
|
Google Cloud
|
Managed Spark and Hadoop service on Google Cloud including cluster management, job submission, and integration with BigQuery.
|
Intermediate | Required | Start Learning |
|
Real-time Analytics with Apache Kafka
|
Confluent
|
Event streaming platform for real-time data pipelines including Kafka Streams, KSQL, and integration with data warehouses.
|
Advanced | Required | Start Learning |
|
Modern Data Stack Architecture
|
dbt Labs
|
Understanding modern data stack components including ELT tools, data warehouses, transformation layers, and orchestration platforms.
|
Intermediate | Required | Start Learning |
|
Delta Lake Data Lakehouse
|
Databricks
|
Open-source storage layer providing ACID transactions, scalable metadata handling, and unified batch/streaming data processing.
|
Advanced | Optional | Start Learning |
|
Apache Iceberg Table Format
|
Apache Software Foundation
|
Open table format for huge analytic datasets with features like schema evolution, hidden partitioning, and time travel.
|
Advanced | Optional | Start Learning |
Big Data Processing Challenge
Task Manager Big Data Analytics Platform
- Design and implement large-scale data processing pipeline using Apache Spark for Task Manager historical data
- Build real-time streaming analytics using Kafka for live task updates and user behavior tracking
- Implement data lakehouse architecture using Delta Lake or Apache Iceberg for unified batch and streaming processing
- Create advanced analytics including machine learning models for user segmentation and task prediction
- Set up distributed processing cluster on Google Cloud Dataproc with auto-scaling and cost optimization
- Build modern data stack integration connecting big data processing with visualization and business intelligence tools
Data Governance & Advanced Analytics
Overview
Establish comprehensive data governance frameworks for enterprise-scale analytics platforms. Master data quality management, lineage tracking, privacy compliance, and security policies. Learn advanced analytics techniques including machine learning integration, anomaly detection, and predictive analytics. Develop expertise in building AI-enhanced analytics platforms that provide intelligent insights while maintaining data quality, security, and regulatory compliance.
Learning Resources
| Course Title | Provider | Description | Level | Mandatory | Action |
|---|---|---|---|---|---|
|
DAMA-DMBOK Data Governance Framework
|
DAMA International
|
Comprehensive data governance framework covering data quality management, data lifecycle, lineage tracking, and enterprise data strategy.
|
Advanced | Required | Start Learning |
|
BigQuery Security and Governance
|
Google Cloud Security
|
Enterprise data protection including IAM, column-level security, row-level security, audit logging, and encryption strategies.
|
Advanced | Required | Start Learning |
|
BigQuery ML Advanced Analytics
|
Google Cloud
|
Machine learning integration in BigQuery including model training, prediction, and advanced analytics use cases for business intelligence.
|
Advanced | Required | Start Learning |
|
Anomaly Detection with BigQuery ML
|
KDnuggets
|
Real-time anomaly detection using ARIMA_PLUS models, time series analysis, alerting systems, and Looker Studio integration.
|
Advanced | Required | Start Learning |
|
Data Quality Metrics and KPI Frameworks
|
Acceldata
|
Establishing measurement systems including data accuracy, completeness, consistency, timeliness metrics, and data downtime measurement.
|
Advanced | Required | Start Learning |
|
GDPR and Data Privacy in Analytics
|
EU GDPR
|
Data privacy compliance in analytics platforms including consent management, data anonymization, and right to be forgotten implementation.
|
Advanced | Optional | Start Learning |
Enterprise Analytics Capstone
Task Manager Enterprise Data Platform
- Implement comprehensive data governance framework with data lineage tracking and quality monitoring
- Build advanced analytics including machine learning models for user behavior prediction and task optimization
- Create anomaly detection system for identifying unusual patterns in task completion and user engagement
- Implement enterprise security including row-level security, column masking, and audit logging
- Design AI-enhanced analytics platform with automated insights generation and intelligent alerting
- Document complete enterprise analytics architecture with governance policies and operational procedures