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How FME drives
dynamic data loads
to Esri's UPDM
Presenter
The
Peak
of
Data
Integration
20
23
Krzysztof
Lemańczyk
S&P Global
Associate Director
(GIS/ETL/Cloud)
The
Peak
of
Data
Integration
20
23
About S&PGlobal Commodity Insights
Part of S&P Global (NYSE: SPGI), the world’s foremost provider of credit ratings, benchmarks,
analytics and workflow solutions in the global capital, commodity and automotive markets.
S&PGlobal Commodity Insights:
● Provides a complete view of global energy and commodities markets that enables our
customers to make decisions with conviction and create long-term, sustainable value.
● Vital to navigating energy transition, coverage includes oil and gas, power, petrochemicals,
metals, agriculture and shipping.
● A trusted connector that brings together thought leaders, market participants, governments,
and regulators.
● Produces 15,000+ daily price assessments across global commodities. 60% of world crude oil
trade estimated to be priced using the Platts Dated Brent® benchmark. Leaders in energy
transition price transparency for hydrogen, low carbon, metals and recycled plastics markets.
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Agenda
1. Introduction
2. Technical walk-through
3. Summary
The
Peak
of
Data
Integration
20
23
Introduction
The
Peak
of
Data
Integration
20
23
Background
Problem Statement:
• Numerous internal pipeline data models and systems collecting data from
various global areas with a diverse range of content and complexity
Objectives:
• Utilize FME as ETL tool and automation driver
• Leverage ESRI’s ArcGIS platform for the data model and editing/QC
• Use AWS to host the platform in the cloud
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Goals
• Single data model for all data sources
• As much automation as possible
• Improved data delivery time
• Improved performance
• OOTB funcionality
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Key components
• UPDM - Utility and Pipeline Data Model
• FME suite
• ArcGIS suite
• Amazon AWS
S&P Global Commodity Insights
https://arcg.is/0XGOe91
The
Peak
of
Data
Integration
20
23
Technical walk-through
The
Peak
of
Data
Integration
20
23
FME Data Pipeline
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Data Sources
Approx. 100 sources:
• Schema Variations
• Format Variations
• Geospatial and Non-geospatial
• Various coordinate systems
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Input data tiers
• RAW – copy of the source
• Daily – source loaded into a database
• Native – current version
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Change Detection
Detecting Data Changes with FME:
• Data with unique identifiers
• Data without unique identifiers
• “Hidden” data updates
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Data load to UPDM
Database-based mappings:
• Asset group
• Asset type
• Attributes
• Domains
FME loads data to UPDM Feature Service
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
eGDB versioning with FME
• FME creates daily versions
• QC by SMEs
• Automated clean up
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Downstream systems
• RestAPI interface
• Publication schema
• Direct load with FME
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
FME Data Pipeline
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
FME Server components
The FME Server project consists of:
• Automations: 6
• Connections: 5
• Repositories: 2
• Resources: 6
• Server Workspace Apps: 2
• Tokens: 3
• Topics: 3
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
FME Data Pipeline configuration
Data Pipeline is driven using configuration tables:
• Attribute corrections
• Mapping tables
• Data sources definitions
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Notifications and Logging
• Real-time Notifications in MS Teams
• Logging of Data Operations
• Tracking Errors and Issues
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Key role of FME Server
• Scheduling and Events
• Process Orchestration
• Notifications
• Performance improvements
• Infrastructure cost reduction
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Summary
The
Peak
of
Data
Integration
20
23
FME Advantages
● Solves challenges related to data formats
● Supports geospatial and non-geospatial data
● Quick and efficient support
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
FME Advantages (cont.)
● Reduces manual effort and errors
● Improves efficiency in data load processes
● Allows advanced data operations
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
FME Advantages (cont.)
● Fully automates data pipeline
● Integrates with other systems and tools
● Enhances data management capabilities
S&P Global Commodity Insights
The
Peak
of
Data
Integration
20
23
Next steps
● Extend list of supported sources
● Support for network topology
● Utilize remaining functionality of UPDM
● AI based format change detection
S&P Global Commodity Insights
ThankYou!

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