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Quick Intro~7 MIN· DIST

Distributed processing, OLAP & query opt

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A scannable trailer of the 8-lesson course. Read top to bottom — no clicks needed.

INTROBLOCK · 01
DIST · 7 MIN PREVIEW

Distributed processing, OLAP & query opt

MapReduce mental model. Spark in 2026. Where DuckDB wins. Tune queries with the planner — not vibes.

CONCEPTBLOCK · 02

Distributed = shuffle is the bottleneck

Every distributed query is dominated by one operation: the shuffle. Map-side and reduce-side ops scale linearly with cores; the shuffle scales with data movement, which is gated by network and disk. Three quarters of query optimisation in 2026 is shuffle reduction: better partitioning, broadcast joins where they fit, predicate pushdown, columnar formats. The other quarter is skew handling — one bad key can stall a 10-machine job behind a single straggler.
TIPWhen in doubt, measure shuffle bytes. It's the single most predictive number for query cost.
WATCH OUTDuckDB beats Spark below ~500 GB on a single node. Don't reach for a cluster for problems a laptop solves.
DIAGRAMBLOCK · 03

Stages, shuffles, and where time goes

rowsby keygroupedstragglersSCANFILTERSHUFFLEAGGSKEW HOT KEY
Filter early. Shuffle less. Salt skewed keys. Three rules carry the field.
CODEBLOCK · 04

Spark broadcast join — the simplest 10x win

PYTHON
1from pyspark.sql import SparkSession
2from pyspark.sql.functions import broadcast
3
4spark = SparkSession.builder.getOrCreate()
5orders = spark.read.parquet("s3://lake/orders/") # 200 GB
6users = spark.read.parquet("s3://lake/users/") # 8 MB
7
8# WRONG: shuffles 200 GB by user_id
9slow = orders.join(users, "user_id")
10
11# RIGHT: broadcasts 8 MB to every executor; zero shuffle
12fast = orders.join(broadcast(users), "user_id")
If one side fits in memory (~10s of MB), broadcast it. Spark has autoBroadcastJoinThreshold but explicit broadcast() removes guesswork.
CHEATSHEETBLOCK · 05

Five things to remember

01Shuffle is the bottleneck. Always measure shuffle bytes.
02Broadcast small tables. Skip the shuffle entirely.
03Salt skewed keys to spread hot partitions.
04Columnar + predicate pushdown beats row-format scans on every metric.
05DuckDB on one big box beats Spark on a small cluster up to ~500 GB.
MINIGAME · RAPIDFIRETFBLOCK · 06

True or false: 6 seconds each

Broadcast joins scale with the size of the broadcast side.
CLAIM 1/5 · READY · scroll into view
LESSON COMPLETEBLOCK · 07

Distributed mental model: locked.

NEXTHello Spark: a 10x win in one DAG
WHAT YOU'LL WALK AWAY WITH

Real skills, real career delta.

Skills you'll gain

09
  • Reason about shuffles and skewWorking

    Outcome from completing the course: reason about shuffles and skew.

  • Pick Trino vs DuckDB vs SparkWorking

    Outcome from completing the course: pick trino vs duckdb vs spark.

  • Tune queries with the planner, not vibesWorking

    Outcome from completing the course: tune queries with the planner, not vibes.

  • MapReduce mental modelWorking

    Covered in lesson sequence — drop-in ready.

  • Spark in 2026Working

    Covered in lesson sequence — drop-in ready.

  • DuckDB vs TrinoWorking

    Covered in lesson sequence — drop-in ready.

  • Query optimisation tacticsWorking

    Covered in lesson sequence — drop-in ready.

  • OLAP fundamentalsWorking

    Covered in lesson sequence — drop-in ready.

  • GPU-accelerated processingWorking

    Covered in lesson sequence — drop-in ready.

Career & income delta

Career moves
  • Lead a Distributed processing, OLAP & query opt initiative on your team — most orgs have it on the roadmap and few have shipped it.
  • Consulting work at $150-300/hr — 'DIST shipped to production' is a sought-after specialty in 2026.
  • Move from generic IC to platform/AI-platform team where Distributed processing, OLAP & query opt expertise is the entry ticket.
Income impact
  • $15-40K bump for senior ICs adding Distributed processing, OLAP & query opt to their resume.
  • Freelance / consulting demand for the same skill: $150-300/hr in 2026.
  • Closing enterprise deals often hinges on demonstrating the production patterns from this course.
Market resilience
  • Distributed processing, OLAP & query opt is a durable skill across model and framework consolidations.
  • Production guardrails (cost caps, observability, audit, evals) carry forward to whatever the 2027 stack is.
  • Core patterns transfer to cloud, on-prem, and hybrid deployments.