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Lift and Shift:
Moving features
with ArcGIS Data
Interoperability
Magnolia River
Who are we?
Magnolia River is a provider of
engineering, inspection, and GIS
solutions for utility & pipeline
infrastructure & operations.
We are located in North Alabama with
offices in Florida, Texas, Missouri,
Louisiana, North Carolina and Michigan.
The Challenge
We needed to move over 2 million features
from 26 databases into a standardized
database model.
There were more than 25 different state
projections that needed to be converted into
Web Mercator
Our Process
● Created a new schema to
incorporate core features and
attribution across all
databases.
● Divided the FME work into 3
groups based on existing
datasets (Water, Wastewater,
and Staging)
● Created a base script for each
database to be converted and
adjusted as needed for specific
requirements of each
database.
The Results
● Crafted 339 individual data interoperability scripts and 36,452 transformers
within the scripts
● Performed complex mapping between source to target features and
attribution values
● Migrated 20,201,956 features from existing SDE databases and SAP to new
SDE databases
● Migrated from 26 different databases to 14 standard databases
● Reduced the overall database size from 80GB to 22GB
● Reduced the number of feature classes from 4,810 to 1,314
● Reduced the number of feature datasets within each database from 9 to 4
Lessons
Learned
● Parameterize as much as
possible
● Incorporate Python where
needed
● Run pre processing scripts
where needed
● Test, Test, Test
THANK YOU!
www.MagnoliaRiver.com

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Lift and Shift 20 Million Features with ArcGIS Data Interoperability

  • 1. Lift and Shift: Moving features with ArcGIS Data Interoperability Magnolia River
  • 2. Who are we? Magnolia River is a provider of engineering, inspection, and GIS solutions for utility & pipeline infrastructure & operations. We are located in North Alabama with offices in Florida, Texas, Missouri, Louisiana, North Carolina and Michigan.
  • 3. The Challenge We needed to move over 2 million features from 26 databases into a standardized database model. There were more than 25 different state projections that needed to be converted into Web Mercator
  • 4. Our Process ● Created a new schema to incorporate core features and attribution across all databases. ● Divided the FME work into 3 groups based on existing datasets (Water, Wastewater, and Staging) ● Created a base script for each database to be converted and adjusted as needed for specific requirements of each database.
  • 5. The Results ● Crafted 339 individual data interoperability scripts and 36,452 transformers within the scripts ● Performed complex mapping between source to target features and attribution values ● Migrated 20,201,956 features from existing SDE databases and SAP to new SDE databases ● Migrated from 26 different databases to 14 standard databases ● Reduced the overall database size from 80GB to 22GB ● Reduced the number of feature classes from 4,810 to 1,314 ● Reduced the number of feature datasets within each database from 9 to 4
  • 6. Lessons Learned ● Parameterize as much as possible ● Incorporate Python where needed ● Run pre processing scripts where needed ● Test, Test, Test