Data Engineering: High Performance Design Patterns
Elevating Data Engineering: Unveiling the Blueprint for High-Performance Design Patterns”
Introduction:
Welcome to the “High Performance Data Engineering Design Patterns” training course! In today’s rapidly evolving technological landscape, the effective management and integration of data play a pivotal role in the success of any organization. This course is designed to equip you with the essential knowledge and skills needed to architect and implement high-performance data engineering systems.
Understanding the Challenge
The world of data engineering is dynamic and complex, and achieving high performance is not a one-size-fits-all endeavor. Whether you’re a student gearing up for a career as a data engineer or a seasoned professional looking to strengthen your knowledge, this training provides valuable insights into the core design patterns that form the foundation of scalable data engineering systems.
The Key to Success: Design Patterns
There is no magic bullet for achieving high performance in data integration; it’s a nuanced interplay of technology, design, and process discipline. As Ananth Raman from Harvard Business School aptly puts it, “Technology is never a substitute for process discipline.” In this course, we’ll delve into crucial design patterns that encompass technology stack selection, data modeling, automation, extraction, loading, transformation, error handling, audit, scheduling, and the broader data engineering development life cycle.
Who Should Take This Course?
Whether you are just starting your journey in data engineering or you are a seasoned professional seeking to enhance your skills, this course is tailored to meet your needs. The content spans from fundamental concepts suitable for beginners to advanced strategies that seasoned practitioners can leverage to optimize their data engineering processes.
What You Will Learn
- Technology Stack: Choose the right tools, databases, and replication methods for efficient data integration.
- Data Modeling: Develop logical and physical data models that enhance scalability and performance.
- Automation: Implement automation for mapping development, workflow execution, and metadata management.
- Extract, Load, Transform (ELT): Optimize your ELT processes for high-performance data processing.
- Error Handling and Audit: Design effective error handling mechanisms and audits for data quality assurance.
- Scheduling: Implement robust scheduling strategies for efficient job execution.
- Data Engineering Development Life Cycle: Understand the architectural and project layers in the development life cycle.
Let’s Get Started
Embark on this learning journey to unravel the intricacies of high-performance data engineering design patterns. By the end of this course, you’ll be equipped with the knowledge and tools to architect data engineering systems that not only meet today’s demands but are also poised for scalability and performance in the years to come.
Let’s dive in!
Chapter 1: Introduction to High Performance Data Engineering Design Patterns
- Overview of the training course
- Importance of design patterns in scalable data engineering systems
- Target audience and learning objectives
Chapter 2: Technology Stack
- Choosing the right data replication tool for high performance data integration
- Considering an end-to-end data integration suite for integrated metadata management and lineage
- Selecting high performance databases like Netezza or Teradata for fast data insertion
- Building an Operational Data Store (ODS) for heterogeneous source data integration
Chapter 3: Data Model Design
- Developing a comprehensive logical and physical data model with metadata
- Utilizing a 3NF data model for MPP databases in high performance data integration
- Removing redundant data through normalization and improving data load processes
- Avoiding dependency by using natural/business keys and minimizing surrogate keys
- Defining foreign keys for metadata without enforcing them at the database level
Chapter 4: Automation
- Automating simple mapping development using tools like Informatica Visio Architect or 4GL programming
- Setting up workflow execution time targets and sending alerts for deviations
- Developing metadata queries/views to review code in a mass manner
- Ensuring efficient execution through automated processes
Chapter 5: Extract
- Utilizing data replication for capturing changed records instead of reading directly from source tables
- Implementing group sourcing concepts to minimize impact on source systems
- Performing incremental extraction for reduced extraction time and downstream ETL efficiency
- Optimizing data hops from source to target and controlling extract range using external data models
Chapter 6: Load
- Employing concurrent/parallel table loads for faster data loading
- Leveraging database-specific loader utilities (e.g., NZLoad for Netezza, TPump for Teradata)
- Setting up data load strategies at the session level for restartability and flexibility
Chapter 7: Transform
- Avoiding Informatica cache lookup and utilizing database joins for better performance
- Leveraging the processing power of RDBMS, particularly for MPP databases
- Sequencing data loading and performing FK data validation for near-real-time availability
- Using common reusable components for transformation rules and sharing across ETL processes
Chapter 8: Error Handling/Audit
- Avoiding reject tables and loading all data in the target table while marking invalid rows
- Designing separate “watcher” workflows for proactive notifications on job statuses
- Creating a common audit component to monitor data quality and set threshold ranges for exception handling
- Enhancing lineage in the data warehouse for reporting and record source identification
Chapter 9: Scheduling
- Implementing a recovery and restart system for job resumption or backing out
- Including dummy restart points in workflows for improved production support
- Leveraging tool scheduler while maintaining job frequency/time in a custom data model
Chapter 10: Data Engineering Development Life Cycle
- Overview of the architecture layer and its components
- Explanation of the project layer and its phases for functional area development
- Highlighting the key steps and deliverables in the data engineering development life cycle
Chapter 11: Methods for Capturing Data Changes
- Overview of five methods for capturing data changes in data engineering processes
- Using source timestamps, DBMS logs, before-and-after image comparisons, snapshots, and database triggers
- Considering the pros and cons of each method in different scenarios
Conclusion
- Recap of the key concepts and design patterns covered in the training
- Final thoughts on achieving high performance in data engineering systems
Note: Please feel free to leave a note on what other topics you want to learn or dive more into.
