DATA INTEGRATION_
Build a data sync layer — not point-to-point spaghetti
Parallaxis data integration maps endpoints and weaves reliable sync paths: discover → map → connect → sync → govern. Systems stay connected with owned mappings, durable auth, and failure playbooks — so CRM, ERP, and warehouse stop drifting apart.
Every engagement runs a data sync layer: Discover → Map → Connect → Sync → Govern. Tap the stages on the right.
Data sync layer_
Endpoints become an owned layer ops can trust. Tap a stage — discover through govern.
Discover_
Stage 01 / 05
Inventory systems and owners
List sources, targets, and who gets paged when a sync stalls — before any adapter ships.
- Systems
- Owners
Full fabric_
- 01 Discover
- 02 Map
- 03 Connect
- 04 Sync
- 05 Govern
Map → connect → sync — not point-to-point spaghetti
- 01Discover systemsOwners, paths, SLAs
- 02Map entitiesShared field contracts
- 03Connect adaptersAuth and retry that last
- 04Sync & governCadence, alerts, handoff
02 · SPAGHETTI VS SYNC LAYER_
Same systems. Two very different weaves.
Toggle Point-to-point spaghetti vs data sync layer — watch where trust dies or completes.
Same constraint_
“CRM, ERP, and the warehouse all need the same customer — and every team built their own export.”
Toggle path — layer morphs_
Path nodes_
Custom export per pair
Connected_
Hard-coded field names
Connected_
Shared entity map
Never reached_
Owned sync cadence
Never reached_
Governed handoff
Breaks here_
Outcome_
Silent breakage
One-off scripts, brittle auth, and no shared map. When CRM changes a field, three pipelines die quietly.
03 · SYNC PRESSURE BOARD_
Reliability dies when sync is hoped
Toggle sync pressures — reliability score and outcome update live.
Sync pressures_· Toggle what frays the fabric
Reliability score_
56/100
2 pressures active_
Outcome_
Fragile sync layer
Some paths hold, but silent fails or drift can still cut critical syncs.
04 · MAPPING INSPECTOR_
See the mismatch before you connect
Tap an entity — inspect source and target fields plus mismatch risk.
Entity mappings_· Tap to inspect
Customer_
CRM account to ERP party record.
Source fields_
Salesforce
- Account.Id
- Account.Name
- BillingCountry
Target fields_
ERP
- Party.Code
- Party.Name
- CountryISO
Mismatch risk_
Country codes disagree (US vs USA) — invoices route to the wrong tax path.
05 · CONNECT CHECKLIST_
What a data integration engagement leaves behind
Open a chapter — discover through govern. Vocabulary the owning team runs next.
Checklist chapters_
5 chapters- System inventory and owners
- Existing point-to-point paths
- Critical sync SLAs
In scope_
- Data sync layer: discover → map → connect → sync → govern
- System-to-system connectivity with owned mappings
- Ops handoff so the sync layer stays reliable
Out of scope_
- Warehouse transforms and freshness pipelines (see Data Engineering)
- Dashboard polish without connected systems (see Data Analysis & BI)
- Product squads without an integration constraint (see Product Engineering)
FIT_
Is Data Integration the right next step?
Self-qualify — we work best when systems must stay in sync with clear owners.
Good fit_
- You have clear systems to connect and people who own each end
- Point-to-point scripts or spreadsheet relays are already breaking silently
- You need owned mappings and sync cadence — not a warehouse remodel first
Not yet_
- You only need trusted warehouse transforms and freshness (see Data Engineering)
- You are still choosing metrics with no systems of record (start with Landscape Sketch)
- You only want dashboards on data that is already connected (see Data Analysis & BI)
NEXT STEP_
Ready to put a data sync layer on your systems?
Map your systems — or talk through the sync that keeps breaking. We will confirm endpoints, owners, mappings, and whether data engineering or BI should land next.
Common questions
We run a consistent path for data integration: discover → map → connect → sync → govern. We connect system-to-system paths with owned mappings, durable auth, and failure playbooks — not one-off export scripts.