One place where all the numbers meet
A warehouse where data from the CRM, ERP, billing and website is linked into one model and keeps its history. The technology is chosen by volume, not fashion.
01Builder
Sketch your data architecture
Pick sources and goals — the diagram appears on the right straight away: sources, pipeline, warehouse, BI and outputs.
Preliminary architectureNodes: 11
- CRM → ETL · ELT
- ERP · 1C → ETL · ELT
- Billing → ETL · ELT
- +2 sources → ETL · ELT
- ETL · ELT → Data quality checks
- Data quality checks → Data Warehouse
- Data Warehouse → Data marts
- Data marts → BI layer · metrics
- BI layer · metrics → Dashboards, Scheduled reports
- Dashboards
- Scheduled reports
SOURCES CRM · ERP · 1C · Billing · Website · events · External API PIPELINE 5 connectors · Scheduled / incremental loads · Deduplication · validation · reference data STORAGE Data Warehouse (PostgreSQL or ClickHouse by volume) · Data marts per department OUTPUTS Dashboards · Scheduled reports
This is a preliminary outline. The architecture may change once we look at your sources.
02Data Warehouse
A model, not a pile of tables
Facts and dimensions, names the business understands, versioned reference data. Department marts sit on top of the shared model.
- PostgreSQL for most workloads
- ClickHouse when events reach millions a day
- Change history (SCD)
04Contact
Let’s look at your data
Five questions and you get a preliminary data architecture and a first step. Or just write to us.
Preliminary data architecture
Step 1 of 6