Skip to content

DE Mastery GitHub

Start

Curriculum
Roadmap
How to study with this course

Tracks

FND Foundations of distributed data

Overview
01 Why distributed?
02 Partitioning & shuffles
03 Replication & consensus
04 Time
05 Delivery semantics
06 Storage formats
07 Back-of-envelope estimation

SPK Apache Spark

Overview
01 Spark architecture
02 Lazy evaluation and the DAG
03 The DataFrame API
04 Spark SQL and data sources
05 Catalyst and the physical plan
06 Joins and aggregations at scale
07 Partitioning, bucketing, and the small-file problem
08 Memory, caching, and Adaptive Query Execution
09 UDFs and the Pandas API
10 Structured Streaming I — the micro-batch engine and exactly-once
11 Structured Streaming II — watermarks, state, and streaming SCD2
12 Testing and production Spark
BYO Build your own mini columnar query engine

LKH Lakehouse & open table formats

Overview
01 The Iceberg metadata tree
02 Commits & concurrency
03 Catalogs, branching & write-audit-publish
04 Row-level changes — SCD2, CoW & MoR
05 Table layout & maintenance
06 Delta Lake
07 Hudi & the format landscape
08 Engines on the lake
BYO Build your own mini table format

JVM Java for data engineers

Overview
01 Java for Python developers
02 Collections and streams
03 Build and dependencies
04 Serialization
05 Concurrency and the JVM
06 Testing and debugging

KFK Apache Kafka

Overview
01 The log — events, state, retention and replay
02 Topics, partitions, replication — leaders, ISR, acks, durability
03 Producers — batching, idempotence, ordering under retries
04 Consumers & groups — manual commits, at-least-once, no loss across crashes
05 Transactions & exactly-once — read-process-write, read_committed, fencing
06 Schema Registry & contracts
07 Kafka Connect & CDC
08 Kafka Streams
09 Event & topic design
10 Testing & observability (apps)
11 Cluster configuration & partition reassignment
12 Security — SASL/SCRAM authentication & ACL authorization
13 Deployment architecture — multi-region DR
14 Operating & troubleshooting a cluster
BYO Build your own mini Kafka

FLK Apache Flink

Overview
01 Philosophy & architecture
02 DataStream API
03 Time & windows
04 Flink SQL & dynamic tables
05 Joins — regular, interval, temporal (point-in-time) & lookup
06 State, checkpoints & exactly-once — surviving a TaskManager crash
07 Process functions — timers, keyed state & broadcast rules
08 Table API & PyFlink — one pipeline, two languages
09 Streaming patterns — dedup, top-N, split/merge, MATCH_RECOGNIZE, OVER
10 CDC & lake sinks — Debezium changelog to Iceberg, commit on checkpoint, schema evolution
11 Performance & state at scale — backpressure, parallelism, redistribution, skew
12 Deploy & operate — upgrade a stateful job via savepoint without losing state
BYO Build your own mini stream processor

STH Streamhouse

Overview
01 Architecture history — Lambda → Kappa → lakehouse → streamhouse
02 Apache Paimon — primary-key tables, merge engines, partial-update
03 Apache Fluss — PK tables, lake tiering, streaming vs batch consistency
04 Iceberg as streaming storage — commit interval vs small files
05 Streaming dimensional modeling — SCD2 dim + fct with temporal (as-of) join
06 Streaming data quality — in-flight DQ, quarantine / DLQ, completeness
07 Serving layer — OLAP on the streamhouse
08 Latency tiers & cost — seconds, minutes, hours

Foundations of distributed data · FND-04

Time

3 h
requires FND-01

From your world. Late-arriving facts and dimensions in Kimball; how long to hold a window open before you trust the number

Lesson Exercises 1 Quiz Interview drills Explain it back

Practice quiz with instant feedback. For a scored run that counts toward your local progress, use uv run de quiz FND-04.

1A watermark of value W asserts what? AThe system believes it has seen all events with event time <= W BThe current wall-clock time is W CExactly W events have been processed DAll events will arrive before W

A watermark is a completeness claim on event time: all events with event time <= W are assumed to have arrived.

Source: https://nightlies.apache.org/flink/flink-docs-stable/docs/concepts/time/

2A late event with a small timestamp arrives. What must a correct watermark do? AStay the same or move forward — never go backwards BDecrease to the late event's timestamp CReset to zero DJump to wall-clock time

Watermarks are monotonic and non-decreasing. A late event with a small timestamp must not pull the watermark back. If it did, windows that are already closed would re-open.

Source: https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/event-time/generating_watermarks/

3Bounded-out-of-orderness watermark with allowedLateness=5s. The max event time seen is 100s. The current watermark is: A95s B100s C105s Dwall-clock time

W = maxEventTime - allowedLateness = 100 - 5 = 95. The watermark lags the newest event by the grace period.

Source: https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/datastream/event-time/generating_watermarks/

4An event with time t arrives beyond the watermark's grace period. The safe handling for an audit is to: ARoute it to a side output / dead-letter and reconcile, not silently drop it BSilently discard it CRewind the watermark DCount it in the next unrelated window

For auditable totals (for example SOX), you must capture late events for reconciliation. If you drop them silently, completeness breaks.

Source: https://nightlies.apache.org/flink/flink-docs-stable/docs/concepts/time/

5A dbt incremental model that reprocesses 'the last 3 days' is the batch equivalent of: AallowedLateness = 3 days Ba watermark that never advances Cprocessing-time windows Dexactly-once delivery

Reprocessing a trailing window is exactly a coarse watermark with a 3-day lateness grace. The day you stop restating a period is the watermark advancing past it.

Source: https://research.google/pubs/pub43864/

6Why not set allowedLateness to a very large value 'to be safe'? (select all) (select all that apply) AEvery open window holds state, so a large grace period means large or unbounded state BResults are delayed until the watermark passes the window end CIt makes the watermark move backwards DIt changes event time into processing time

A large lateness makes the state bigger and delays results. It does not reverse the watermark (the watermark is still monotonic). It also does not change which clock you use.

Source: https://www.oreilly.com/radar/the-world-beyond-batch-streaming-101/

PreviousFND-03 · Replication & consensus NextFND-05 · Delivery semantics

DE Mastery · source on GitHub · Found an error? Report it on GitHub