Simplification du Processus Data : Analyse d’Architecture
Découvrez une analyse des défis de l'architecture data héritée : goulots d'étranglement, performance de production et maintenance complexe des données.
Data Process Simplification
An analysis of the Legacy Architecture & Challenges
Input Sources: The Dual Stream
The old process begins with disparate data ingestion. We manage parallel streams for different operational entities (AF & KL), creating immediate duplication in the workflow structure.
The Bottleneck: 'Coupons Issued'
All operational data stores (ODS) and external Data Warehouse inputs merge into a massive central entity: 'Coupons Issued'. This centralization point becomes a critical dependency.
Hidden Complexity: Multiple Dependencies
DWH PNR (Passenger Name Record)
DWH TRAFFIC Data
DWH KL Specifics
The Old Process: Full Architecture
Sirax AF
Sirax KL
ODS AF
ODS KL
COUPONS ISSUED
SALES TABLES
Sales Universe BO
DWH PNR
DWH TRAFFIC
DWH KL
Issue 1: Production Performance
The architecture suffers from critical production performance issues. The serialization of data processing through a single bottleneck causes latency and data availability delays.
Issue 2: Lack of Clarity
The 'Black Box' effect. There is a significant lack of clarity and modularity in the system. Tracing errors or understanding data lineage is difficult due to the monolithic design.
Issue 3: Maintenance Nightmare
Complex Maintenance & Evolution. Any small change in the 'Coupons Issued' logic risks breaking downstream dependencies (BO). Innovation is stifled by fear of regression.
Why Do We Need Change?
Critical Production Performance Issues
Lack of Clarity & Modularity
Complex Maintenance & Evolution
The Path Forward
Simplification is not just an option; it is a necessity for scalability, speed, and reliability.
- data-architecture
- data-process
- big-data
- business-intelligence
- data-warehouse
- performance-it
- legacy-systems
- management-it