The data ecosystem is huge. This is how we put the pieces in order, and why.
Data stack components
Where data is generated: databases, SaaS applications, ERP systems.
How data moves and is normalized from the source to analytical storage.
Where data from every source is aggregated, usually a data warehouse or a lakehouse.
Cleaning, structuring and packaging sources into easy-to-use analytical models.
Exploring, visualizing and finding strategic insight in the data.
Known as reverse ETL: moving data from the warehouse into operational systems (CRM, marketing) to act on it.
Benefits
Modularity
Independent functions that combine: building a data stack is much simpler.
Speed
Autonomous components that are faster to set up and iterate.
Cost
Cloud technology with more predictable costs than an on-premise install.
Build process
- 01Choose the data lakehouse
- 02Select the ingestion tool
- 03Define the modeling process
- 04Build analytics that adds value
- 05Select the reverse ETL solution