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SAP HANA Schema Design
Analyse column cardinality and write a CREATE COLUMN TABLE DDL that applies dictionary encoding to the right low-cardinality columns and types every field correctly for an in-memory store.
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Real-Time Data Engineering on SAP: Tame a Billion Connected-Car Events
ProoV · Intermediate
About this project
An independent ProoV educational experience using BMW Group as a publicly documented SAP HANA customer (nominative reference). Scenarios and datasets are synthetic, for educational use only — not affiliated with, endorsed by, or sponsored by BMW Group AG or SAP SE. You play a database engineer at Vektor Data GmbH, a fictional consultancy, optimising the SAP HANA cluster behind ConnectedDrive's dealer dashboard: design the schema, rewrite slow SQL, benchmark the gain, and brief the CTO.
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inReal-Time Data Engineering on SAP: Tame a Billion Connected-Car Events
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A graded ProoV project, completed step by step and marked against a published rubric.
❖sap-hana, sql and more skills
What you'll do
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Introduction & The BMW × SAP Brief
Step into the seat of a database engineer at a consultancy optimising the SAP HANA cluster behind BMW ConnectedDrive's dealer dashboard. Meet the team, grasp the scale of enterprise concurrency, and get briefed on the four engineering tasks ahead.
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SAP HANA Theory
Learn what makes SAP HANA fast: in-memory column storage, dictionary compression, and the architecture BMW relies on. Then meet the villain — the slow query that is killing the dealer dashboard's SLA.
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Task 1 — Schema Design (DDL)
Load the ConnectedDrive telematics traces with pandas, analyse column cardinality, then write a CREATE COLUMN TABLE DDL schema that applies DICTIONARY encoding to the low-cardinality columns and types all twelve fields correctly.
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Task 2 — Query Optimisation
Profile the trace log to find the two costliest SQL patterns (FULL_TABLE_SCAN and SELECT *), then rewrite them to target the column store with a SAP HANA parallel hint and quantify the speedup.
What you'll learn
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SQL Query Optimisation & Benchmarking
Profile a trace log to find the costliest patterns, rewrite slow SQL to target the column store with parallel hints, then compute CPU, latency, memory, and QPH deltas and test them against a real SLA.
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Executive Communication
Translate benchmark numbers into a 300–500 word pitch to BMW's CTO that opens with a stability guarantee, cites a specific figure, links to the Dealer SLA, and avoids unexplained jargon.