Truly Scalable Operational Data Layers for Data Pipelines

Overview

Discover how to build and scale operational data layers effectively, focusing on source-to-destination streaming systems and achieving true scalability for large-scale data processing needs.

Syllabus

• Introduction to Operational Data Layers
Definition and Importance
Key Components and Architecture
Use Cases and Industry Applications
• Basics of Data Pipelines
Understanding ETL and ELT
Real-time vs Batch Processing
Common Data Pipeline Architectures
• Source-to-Destination Streaming Systems
Overview of Streaming Architectures
Data Ingestion Techniques
Managing Data Flow and Latency
• Designing Scalable Data Pipelines
Scalability Principles
Horizontal vs Vertical Scaling
Load Balancing Techniques
• Building a Robust Operational Data Layer
Data Storage Solutions
Data Consistency and Availability
Handling Data Formats and Schemas
• Tools and Technologies for Scaling
Overview of Key Platforms (e.g., Apache Kafka, Apache Flink)
Cloud-Based Solutions and Database Technologies
Evaluating Pros and Cons of Different Tools
• Performance Optimization Strategies
Bottleneck Identification
Resource Allocation and Tuning
Monitoring, Logging, and Anomaly Detection
• Implementing Change Data Capture (CDC)
Introduction to CDC
Techniques and Tools for CDC
Integrating CDC with Data Pipelines
• Ensuring Data Quality and Integrity
Data Validation and Cleansing
Implementing Data Governance
Building Fault-Tolerant Systems
• Security and Compliance in Data Pipelines
Data Encryption and Access Control
Compliance with Data Regulations (e.g., GDPR, CCPA)
Auditing and Monitoring for Security Breaches
• Case Studies and Industry Examples
Real-World Implementations
Lessons Learned from Successful Scalability
• Future Trends in Operational Data Layers
Evolution of Streaming Technologies
Impact of AI and Machine Learning on Data Pipelines
Emerging Technologies and Innovations
• Conclusion and Course Wrap-Up
Recap of Key Learnings
Best Practices for Building Scalable Operational Data Layers
Final Q&A and Discussion

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Subjects

Data Science