The analytics that only know today

Embedded analytics is real-time because it reads the live tables, and it forgets for the same reason. SAP’s own guidance says it is for operational reporting, not a data warehouse. Most teams hear the first half and discover the second at trend time.

RuleUse embedded analytics for operational questions about now. History is a warehouse feature, and the warehouse should be one you own.

Neverplan a management trend on views over live tables. What archiving removes, and what updates overwrite, is gone from every view at once.

◆ What the vendor itself saysoperational reporting only, and archived data disappears.

This page needs fewer sources than most, because SAP concedes the point. The published guidance describes embedded analytics as operational reporting on current data, and states outright that it is not intended for building a data warehouse. Community guidance from SAP architects adds that archived data disappears from embedded analytics, along with everything else that reads the live tables.

The architecture explains it in one sentence: the analytical views are queries over the transactional tables, computed at request time, with no store of the past underneath. Real-time and memoryless are the same property seen from two sides.

◆ The three doors SAP offerseach one paid, each one still the vendor’s.

Door	What it adds	What it costs you
The warehouse product	A separate warehousing system with snapshots and history, the classic answer.	A second landscape to license, staff, and migrate, inside the vendor’s stack.
The cloud data platform	Managed warehousing with capacity-unit pricing. Covered on the cost page.	Consumption fees that are hard to decode, and the data still lives on rented ground.
The analytics cloud	Dashboards and stories on top. Covered on the limits page.	A front end inheriting the source’s limits, with its own scheduling walls.

◆ The owned answersnapshot facts in your warehouse, extraction you control.

The structural fix is the same on every platform this catalog covers: dated snapshot tables in a warehouse you own, fed by the extraction pattern.

Open items by day for aging, stock by day for valuation and aging, order status by day for backlog. Each is one growing table, and every question about the past becomes a filter instead of a reconstruction. Different vendor, same sentence as Oracle and Workday: the system only knows now.

Use case

Problem

Embedded analytics reads live tables, so it holds current data only, loses archived data, and cannot produce multi-year trends.

What we build

Dated snapshot tables in a warehouse you own, fed by the extraction pattern, one per state worth remembering.

What you get

Trends and as-of answers that survive archiving, restructurings, and vendor product decisions, because the history is yours.

How many of your trends survive archiving?

We build the snapshot layer that remembers, in your cloud, in 10 days.

Talk to us

Terms on this page

SAP: The vendor. Systems, Applications, and Products in data processing.
HANA: SAP’s in-memory database, the platform S/4 runs on.
ERP: Enterprise resource planning, the system of record for operations and finance.
embedded analytics: Delivered analytical views computed live over the transactional tables.
analytical view: A query definition, not a store. Nothing behind it persists results.
archiving: Moving old documents out of the live database, and out of every live view.
capacity unit: The consumption metric SAP’s cloud data platform bills by.
snapshot fact: A table storing dated copies of a state, one row per thing per date.