A Guide to Modern Data Stack - Building Efficient Data Infrastructure

Overview

Discover how to build and optimize a modern data stack with expert guidance on essential components, technologies, and best practices for efficient data management and analytics infrastructure.

Syllabus

• Introduction to Modern Data Stack
Definition and Importance
Key Components and Benefits
• Data Ingestion and Integration
ETL vs. ELT Processes
Tools for Efficient Data Ingestion (e.g., Apache Kafka, Segment)
Best Practices for Data Integration
• Data Storage Solutions
Data Warehouses vs. Data Lakes vs. Lakehouses
Overview of Leading Technologies (e.g., Snowflake, AWS Redshift, Google BigQuery, Databricks)
Storage Optimization Techniques
• Data Transformation and Cleaning
Data Modeling Approaches (e.g., Star Schema, Snowflake Schema)
Tools and Technologies (e.g., dbt, Apache Spark)
Ensuring Data Quality and Consistency
• Data Governance and Security
Establishing Data Governance Frameworks
Privacy and Compliance Considerations
Security Best Practices (e.g., data encryption, access controls)
• Data Analytics and Business Intelligence
Choosing the Right BI Tools (e.g., Looker, Tableau, Power BI)
Key Metrics and Dashboard Design
Promoting Data-Driven Decision Making
• Infrastructure Management and Monitoring
Automation and Orchestration Tools (e.g., Apache Airflow)
Monitoring and Logging (e.g., Prometheus, Grafana)
Cost Management Strategies
• Emerging Trends and Future Directions
Machine Learning and AI in Data Infrastructure
The Role of Real-time Analytics
Innovations in Data Processing and Storage Technologies
• Case Studies and Practical Applications
Analyzing Successful Implementations
Lessons Learned from Real-world Examples
• Course Summary and Best Practices
Recap of Key Learnings
Building a Roadmap for Implementation
Resources for Continued Learning and Development
• Final Project and Assessment
Designing a Modern Data Stack for a Given Use Case
Peer Review and Feedback

Subjects

Data Science