Labs
Each lab takes under an hour on a laptop and turns a claim from this mechanism into a number you measured.
- 1
Measure your skew distribution and price δ
A million heavy-tailed arrival skews: p50 to p99.99, and for each used as the watermark delay, the share of late records and the extra wait.
MeasurePython with NumPy - 2
Watermark with an idle partition
Three partitions, one goes quiet for 10 minutes and then sends its backlog: window closing delay and late events with and without an idleness timeout.
SimulatePython - 3
Retained-window memory
Tumbling windows of size W kept until the watermark passes their end plus allowed lateness L: windows held per key against 1 + L/W.
SimulatePython - 4
Trigger mode versus sink
A window fires twice (3,100, then 3,260) in accumulating or discarding mode, into an overwriting sink or an additive counter.
SimulatePython - 5
Bitemporal table in SQLite or Postgres
A rate table with valid time and transaction time in SQLite: a correction, two as-of questions, and the same history with one updated_at column.
MeasurePython - 6
Event time versus availability time
Features with available_time = valid_time + 9 h: how many events get a different value when joined on availability, and what that does to a model.
MeasurePython with pandas, scikit-learn - 7
Leakage inflates the score (naive join versus as-of join)
20,000 deliveries with a drifting courier speed feature: a naive join on the latest feature value against an as-of join, scored offline and on a later period.
MeasurePython with pandas, scikit-learn - 8
The daylight-saving key bug
02:30 on 26 October 2025 happens twice in Berlin: two instants, one local text. Then month arithmetic in two orders.
MeasurePython - 9
Replay determinism test
A transformation that reads current_timestamp, run twice and "tomorrow"; then with a run_date parameter. Output hashes compared.
MeasurePython