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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>DuckDB Masterclass — The Analytical Engine</title>
<meta property="og:title" content="DuckDB Masterclass — The Analytical Engine">
<meta property="og:description" content="Deep dive into DuckDB — the in-process analytical database for fast SQL analytics on embedded and local data.">
<meta property="og:image" content="../mathsGraph.jpg">
<meta property="og:type" content="article">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link href="https://fonts.googleapis.com/css2?family=Bebas+Neue&family=IBM+Plex+Mono:ital,wght@0,400;0,500;0,700;1,400&family=IBM+Plex+Sans:wght@300;400;500;600&display=swap" rel="stylesheet">
<style>
:root {
--bg: #05060a;
--bg2: #0c0e16;
--bg3: #13162100;
--bg3s: #131621;
--bg4: #1c2030;
--border: #1f2438;
--border2: #2a3050;
--duck: #ffd000;
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--duck-dim: #3d2f00;
--lime: #b8ff3c;
--lime-dim: #243300;
--teal: #00e5c8;
--teal-dim: #003830;
--coral: #ff5f5f;
--coral-dim:#3d0f0f;
--sky: #5dbaff;
--sky-dim: #0c2840;
--violet: #c084fc;
--violet-dim:#2a1050;
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--mono: 'IBM Plex Mono', monospace;
--sans: 'IBM Plex Sans', sans-serif;
--display: 'Bebas Neue', sans-serif;
}
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
html { scroll-behavior: smooth; }
body {
background: var(--bg);
color: var(--text);
font-family: var(--sans);
font-size: 14.5px;
line-height: 1.7;
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/* ── HEX GRID BACKGROUND ── */
body::before {
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pointer-events: none;
z-index: 0;
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/* ── SCANLINES ── */
body::after {
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background: repeating-linear-gradient(
0deg,
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/* ── HEADER ── */
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}
/* ── LAYOUT ── */
.wrapper {
max-width: 1440px;
margin: 0 auto;
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z-index: 1;
}
section { display: none; animation: fadeIn 0.25s ease; }
section.active { display: block; }
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from { opacity: 0; transform: translateY(8px); }
to { opacity: 1; transform: translateY(0); }
}
/* ── SECTION TITLE ── */
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/* ── GRID ── */
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@media (max-width: 960px) { .g2, .g3, .g12, .g21 { grid-template-columns: 1fr; } }
/* ── CARDS ── */
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}
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background: #030407;
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border-radius: 4px;
overflow: hidden;
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}
.code-header {
display: flex;
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background: var(--bg4);
border-bottom: 1px solid var(--border);
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text-transform: uppercase;
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}
pre::-webkit-scrollbar { height: 4px; }
pre::-webkit-scrollbar-thumb { background: var(--bg4); }
/* ── SYNTAX TOKENS ── */
.kw { color: #ff79c6; } /* SQL keywords */
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border: 1px solid var(--border);
border-radius: 4px;
overflow: hidden;
margin: 1.5rem 0;
}
.vector-card {
background: var(--bg2);
padding: 1rem;
cursor: pointer;
transition: background 0.15s;
border: none;
text-align: left;
}
.vector-card:hover { background: var(--bg4); }
.vector-name {
font-family: var(--mono);
font-size: 0.78rem;
font-weight: 700;
color: var(--duck);
margin-bottom: 0.2rem;
letter-spacing: 0.04em;
}
.vector-desc {
font-family: var(--mono);
font-size: 0.65rem;
color: var(--text3);
line-height: 1.4;
}
/* ── EXTENSION CARDS ── */
.ext-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(240px, 1fr));
gap: 1rem;
margin: 1rem 0;
}
.ext-card {
background: var(--bg2);
border: 1px solid var(--border);
border-radius: 4px;
padding: 1rem 1.25rem;
transition: border-color 0.15s, transform 0.15s;
}
.ext-card:hover { border-color: var(--duck); transform: translateY(-2px); }
.ext-name {
font-family: var(--mono);
font-size: 0.9rem;
font-weight: 700;
color: var(--duck);
margin-bottom: 0.25rem;
}
.ext-install {
font-family: var(--mono);
font-size: 0.7rem;
color: var(--text3);
background: var(--bg4);
padding: 3px 8px;
border-radius: 2px;
margin-bottom: 0.5rem;
display: inline-block;
}
.ext-desc {
font-size: 0.82rem;
color: var(--text2);
line-height: 1.5;
}
.ext-bundled { color: var(--lime); font-size: 0.6rem; font-family: var(--mono); letter-spacing: 0.06em; }
/* ── PERF METER ── */
.perf-row {
display: grid;
grid-template-columns: 160px 1fr auto;
gap: 1rem;
align-items: center;
padding: 0.6rem 0;
border-bottom: 1px solid var(--border);
}
.perf-label { font-family: var(--mono); font-size: 0.75rem; color: var(--text2); }
.perf-bar-track { height: 6px; background: var(--bg4); border-radius: 1px; overflow: hidden; }
.perf-bar-fill { height: 100%; border-radius: 1px; transition: width 1.2s cubic-bezier(0.4,0,0.2,1); }
.perf-val { font-family: var(--mono); font-size: 0.7rem; color: var(--text3); min-width: 60px; text-align: right; }
/* ── VS TABLE ── */
.vs-grid {
display: grid;
grid-template-columns: 180px repeat(5, 1fr);
gap: 1px;
background: var(--border);
border: 1px solid var(--border);
border-radius: 4px;
overflow: hidden;
margin: 1rem 0;
font-size: 0.78rem;
}
.vs-cell {
background: var(--bg2);
padding: 0.7rem 0.85rem;
font-family: var(--mono);
font-size: 0.72rem;
}
.vs-head { background: var(--bg4); color: var(--duck); font-weight: 700; letter-spacing: 0.06em; }
.vs-label { color: var(--text3); letter-spacing: 0.06em; text-transform: uppercase; font-size: 0.65rem; }
.vs-yes { color: var(--lime); }
.vs-no { color: var(--coral); }
.vs-part { color: var(--duck); }
.vs-duck-col { background: rgba(255,208,0,0.04) !important; border-left: 2px solid var(--duck); }
/* ── SEPARATOR ── */
.sep { border: none; border-top: 1px solid var(--border); margin: 2rem 0; }
/* ── MISC ── */
p { margin-bottom: 0.9rem; color: var(--text2); font-weight: 300; }
p:last-child { margin-bottom: 0; }
h3 {
font-family: var(--display);
font-size: 1.4rem;
letter-spacing: 0.05em;
color: var(--text);
margin: 1.75rem 0 0.6rem;
}
h3:first-child { margin-top: 0; }
h4 {
font-family: var(--mono);
font-size: 0.75rem;
color: var(--text3);
letter-spacing: 0.1em;
text-transform: uppercase;
margin: 1.2rem 0 0.5rem;
}
ul, ol { padding-left: 1.5rem; color: var(--text2); font-weight: 300; }
ul li, ol li { margin-bottom: 0.3rem; }
strong { color: var(--text); font-weight: 500; }
::-webkit-scrollbar { width: 5px; height: 5px; }
::-webkit-scrollbar-track { background: transparent; }
::-webkit-scrollbar-thumb { background: var(--bg4); border-radius: 2px; }
/* ── ARCHITECTURE DIAGRAM ── */
.arch-diagram {
background: #030407;
border: 1px solid var(--border);
border-radius: 4px;
padding: 1.5rem;
font-family: var(--mono);
font-size: 0.72rem;
line-height: 2;
overflow-x: auto;
}
.arch-layer {
display: flex;
align-items: center;
gap: 8px;
margin-bottom: 4px;
}
.arch-box {
padding: 4px 12px;
border-radius: 2px;
font-size: 0.7rem;
white-space: nowrap;
}
.arch-arrow { color: var(--text3); }
.aql { background: var(--duck-dim); color: var(--duck); border: 1px solid var(--duck-dim); }
.apr { background: var(--lime-dim); color: var(--lime); border: 1px solid var(--lime-dim); }
.aex { background: var(--teal-dim); color: var(--teal); border: 1px solid var(--teal-dim); }
.ast { background: var(--sky-dim); color: var(--sky); border: 1px solid var(--sky-dim); }
.aio { background: var(--violet-dim); color: var(--violet); border: 1px solid var(--violet-dim); }
.acm { background: var(--coral-dim); color: var(--coral); border: 1px solid var(--coral-dim); }
</style>
</head>
<body>
<nav style="font-family:'DM Mono',monospace;font-size:0.7rem;padding:8px 16px;background:#1c1a16;color:#b8b0a4;border-bottom:1px solid #333;letter-spacing:0.03em;position:sticky;top:0;z-index:9999;">
<a href="../index.html" style="color:#cc4400;text-decoration:none;">KeGG</a>
<span style="color:#555;margin:0 6px;">/</span>
<span style="color:#b8b0a4;">Masterclasses</span>
<span style="color:#555;margin:0 6px;">/</span>
<span style="color:#f2ece0;">DuckDB Masterclass — The Analytical Engine</span>
</nav>
<header>
<div class="header-inner">
<div class="logo">
<span class="logo-duck">🦆</span>
<span class="logo-text">DUCK<span>DB</span></span>
</div>
<nav>
<button class="nav-btn active" onclick="show('quack',this)">QUACK</button>
<button class="nav-btn" onclick="show('ingress',this)">INGRESS</button>
<button class="nav-btn" onclick="show('sqlplus',this)">SQL+</button>
<button class="nav-btn" onclick="show('extensions',this)">EXTENSIONS</button>
<button class="nav-btn" onclick="show('python',this)">PYTHON</button>
<button class="nav-btn" onclick="show('perf',this)">PERFORMANCE</button>
<button class="nav-btn" onclick="show('patterns',this)">PATTERNS</button>
<button class="nav-btn" onclick="show('vs',this)">VS FIELD</button>
</nav>
<div class="version-pill">v1.2.x STABLE</div>
</div>
</header>
<div class="wrapper">
<!-- ══════════════════════════════════════════════════
SECTION: QUACK — ARCHITECTURE + ORIENTATION
══════════════════════════════════════════════════ -->
<section id="quack" class="active">
<div class="section-header">
<div>
<div class="section-title">THE <em>DUCK</em> MANIFESTO</div>
<div class="section-sub">DuckDB is an in-process OLAP engine. No server. No daemon. No network round-trips. It embeds into your Python, Node, Java, or Rust process and tears through analytical queries at memory bandwidth speed. Think SQLite philosophy — but column-store, vectorized, and parallelized.</div>
</div>
<div style="text-align:right">
<div style="font-family:var(--mono);font-size:0.65rem;color:var(--text3);letter-spacing:0.08em">BORN</div>
<div style="font-family:var(--display);font-size:2rem;color:var(--duck)">2018</div>
<div style="font-family:var(--mono);font-size:0.65rem;color:var(--text3)">CWI Amsterdam</div>
</div>
</div>
<div class="stat-grid">
<div class="stat"><div class="stat-num" style="color:var(--duck)">0</div><div class="stat-label">External Deps</div></div>
<div class="stat"><div class="stat-num" style="color:var(--lime)">∞</div><div class="stat-label">File Formats</div></div>
<div class="stat"><div class="stat-num" style="color:var(--teal)">~50MB</div><div class="stat-label">Binary Size</div></div>
<div class="stat"><div class="stat-num" style="color:var(--sky)">1024</div><div class="stat-label">Rows / Vector</div></div>
<div class="stat"><div class="stat-num" style="color:var(--coral)">100%</div><div class="stat-label">ANSI SQL</div></div>
<div class="stat"><div class="stat-num" style="color:var(--violet)">MVCC</div><div class="stat-label">Transactions</div></div>
</div>
<div class="g2">
<div>
<div class="card">
<div class="card-title">Execution Architecture</div>
<div class="arch-diagram">
<div class="arch-layer">
<span style="color:var(--text3);min-width:100px">SQL / Jinja</span>
<span class="arch-box aql">Parser + Binder</span>
<span class="arch-arrow">→</span>
<span class="arch-box aql">Logical Planner</span>
<span class="arch-arrow">→</span>
<span class="arch-box aql">Optimizer</span>
</div>
<div style="margin:4px 0 4px 100px;color:var(--text3)">↓ Physical Plan</div>
<div class="arch-layer">
<span style="color:var(--text3);min-width:100px">Execution</span>
<span class="arch-box apr">Pipeline Executor</span>
<span class="arch-arrow">→</span>
<span class="arch-box apr">Vectorized Operators</span>
<span class="arch-arrow">→</span>
<span class="arch-box apr">Morsel Scheduler</span>
</div>
<div style="margin:4px 0 4px 100px;color:var(--text3)">↓↕ Read / Write</div>
<div class="arch-layer">
<span style="color:var(--text3);min-width:100px">Storage</span>
<span class="arch-box aex">Buffer Manager</span>
<span class="arch-arrow">→</span>
<span class="arch-box aex">Column Groups</span>
<span class="arch-arrow">→</span>
<span class="arch-box aex">Compression</span>
</div>
<div style="margin:4px 0 4px 100px;color:var(--text3)">↕ Scan / Push</div>
<div class="arch-layer">
<span style="color:var(--text3);min-width:100px">I/O Layer</span>
<span class="arch-box aio">Local FS</span>
<span class="arch-box aio">S3 / GCS / AZ</span>
<span class="arch-box aio">HTTP</span>
<span class="arch-box aio">Memory</span>
</div>
<div style="margin:4px 0 4px 100px;color:var(--text3)">↕ Scan Pushdown</div>
<div class="arch-layer">
<span style="color:var(--text3);min-width:100px">Formats</span>
<span class="arch-box ast">Parquet</span>
<span class="arch-box ast">CSV</span>
<span class="arch-box ast">JSON</span>
<span class="arch-box ast">Iceberg</span>
<span class="arch-box ast">Delta</span>
<span class="arch-box ast">Arrow</span>
</div>
<div style="margin:4px 0 4px 100px;color:var(--text3)">↑ Extensions</div>
<div class="arch-layer">
<span style="color:var(--text3);min-width:100px">Scanners</span>
<span class="arch-box acm">PostgreSQL</span>
<span class="arch-box acm">MySQL</span>
<span class="arch-box acm">SQLite</span>
<span class="arch-box acm">Spatial</span>
</div>
</div>
</div>
</div>
<div>
<div class="card">
<div class="card-title">The Mental Model Shift</div>
<table>
<thead><tr><th>Old World</th><th>DuckDB World</th></tr></thead>
<tbody>
<tr><td>Launch a server</td><td>Import a library</td></tr>
<tr><td>Connection pool</td><td>In-process call</td></tr>
<tr><td>Row-store pages</td><td>Column groups + zone maps</td></tr>
<tr><td>Interpret row by row</td><td>Vectorized 1024-row batches</td></tr>
<tr><td>Single-threaded scan</td><td>Morsel-driven parallelism</td></tr>
<tr><td>JDBC/ODBC latency</td><td>Zero-copy Arrow handoff</td></tr>
<tr><td>ETL into warehouse</td><td>Query <em>in place</em></td></tr>
<tr><td>Schema-bound tables</td><td>Schema-on-read anything</td></tr>
<tr><td>Fixed compute cluster</td><td>Laptop = analytics engine</td></tr>
</tbody>
</table>
</div>
<div class="card" style="margin-top:1rem">
<div class="card-title">Vectorized Execution — Why It's Fast</div>
<p>Every operator processes <strong>1,024-row chunks</strong> (vectors). This isn't just cache-friendly — it enables SIMD: the CPU executes the <em>same operation</em> across 8–16 values simultaneously using AVX-512 instructions. A filter on a 100M-row Parquet file doesn't touch 99.9M rows that fail zone map checks.</p>
<div class="callout c-duck">
<strong>Zone Maps + Pushdown</strong>
Every row group in Parquet stores min/max metadata. DuckDB's optimizer pushes <code>WHERE event_ts > '2024-01-01'</code> down to the scan: row groups outside the range are never read. No full table scans for selective queries.
</div>
</div>
</div>
</div>
<hr class="sep">
<h3>Instant Gratification — Zero Config, Maximum Power</h3>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">Getting dangerous in 30 seconds</span><span class="code-lang">sql</span></div>
<pre><span class="cm">-- Install: pip install duckdb (that's it. No server. No config.)</span>
<span class="cm">-- Query a 2GB Parquet file from S3 directly — no download:</span>
<span class="kw">SELECT</span> user_id, <span class="fn">count</span>(<span class="op">*</span>) events, <span class="fn">sum</span>(revenue) total
<span class="kw">FROM</span> <span class="jj">read_parquet</span>(<span class="st">'s3://my-data-lake/events/2024/**/*.parquet'</span>)
<span class="kw">WHERE</span> event_type <span class="op">=</span> <span class="st">'purchase'</span>
<span class="kw">GROUP BY ALL</span>
<span class="kw">ORDER BY</span> total <span class="kw">DESC</span>
<span class="kw">LIMIT</span> <span class="nu">20</span>;
<span class="cm">-- GROUP BY ALL — auto-infers non-aggregated columns. Zero maintenance.</span>
<span class="cm">-- ORDER BY column_alias — yes, you can order by an alias you defined above.</span>
<span class="cm">-- Scan 47 CSV files with schema inference and hive partitioning:</span>
<span class="kw">SELECT</span> <span class="op">*</span>
<span class="kw">FROM</span> <span class="jj">read_csv</span>(<span class="st">'data/year=*/month=*/*.csv'</span>,
hive_partitioning <span class="op">=</span> <span class="kw">true</span>, <span class="cm">-- year, month become columns</span>
auto_detect <span class="op">=</span> <span class="kw">true</span>, <span class="cm">-- infer types, delimiters</span>
parallel <span class="op">=</span> <span class="kw">true</span> <span class="cm">-- all cores</span>
);
<span class="cm">-- Query a pandas DataFrame (zero copy via Arrow):</span>
<span class="kw">SELECT</span> <span class="op">*</span> <span class="kw">FROM</span> df <span class="kw">WHERE</span> amount <span class="op">></span> <span class="nu">100</span>; <span class="cm">-- `df` is a Python variable!</span>
<span class="cm">-- Query a live Postgres database as if it's a local table:</span>
<span class="kw">ATTACH</span> <span class="st">'postgresql://user:pass@host/db'</span> <span class="kw">AS</span> pg (<span class="kw">TYPE</span> postgres, <span class="kw">READ_ONLY</span>);
<span class="kw">SELECT</span> <span class="op">*</span> <span class="kw">FROM</span> pg.public.orders <span class="kw">LIMIT</span> <span class="nu">100</span>;</pre>
</div>
</section>
<!-- ══════════════════════════════════════════════════
SECTION: INGRESS — READING FROM EVERYTHING
══════════════════════════════════════════════════ -->
<section id="ingress">
<div class="section-header">
<div>
<div class="section-title"><em>INGRESS</em> FROM ALL VECTORS</div>
<div class="section-sub">DuckDB's superpower for ingress engineers: it reads <em>everything</em>, everywhere, with predicate pushdown, parallel scans, and zero ETL. Your entire data architecture becomes a SELECT statement.</div>
</div>
</div>
<div class="pills" id="ing-pills">
<button class="pill active" onclick="showTab('ing','parquet',this)">Parquet / Iceberg / Delta</button>
<button class="pill" onclick="showTab('ing','cloud',this)">Cloud Storage</button>
<button class="pill" onclick="showTab('ing','csv',this)">CSV / JSON</button>
<button class="pill" onclick="showTab('ing','db',this)">Live Databases</button>
<button class="pill" onclick="showTab('ing','http',this)">HTTP / Stdin</button>
<button class="pill" onclick="showTab('ing','arrow',this)">Arrow / Python</button>
</div>
<!-- PARQUET -->
<div id="ing-parquet" class="tab-pane active">
<div class="g2">
<div>
<div class="callout c-duck">
<strong>WHY PARQUET IS YOUR BEST FRIEND</strong>
DuckDB reads only the columns and row groups it needs. A 10GB Parquet file with a selective WHERE clause may read <1MB. This is column projection + predicate pushdown working together. Your S3 egress bill thanks you.
</div>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">Parquet — full arsenal</span><span class="code-lang">sql</span></div>
<pre><span class="cm">-- Single file</span>
<span class="kw">FROM</span> <span class="jj">read_parquet</span>(<span class="st">'data/events.parquet'</span>);
<span class="cm">-- Glob — all files in dir, recursive</span>
<span class="kw">FROM</span> <span class="jj">read_parquet</span>(<span class="st">'data/**/*.parquet'</span>);
<span class="cm">-- List of files (heterogeneous paths)</span>
<span class="kw">FROM</span> <span class="jj">read_parquet</span>([
<span class="st">'data/jan.parquet'</span>,
<span class="st">'s3://bucket/feb.parquet'</span>,
<span class="st">'https://cdn.example.com/mar.parquet'</span>
]);
<span class="cm">-- Hive partitioning — injects year/month as columns</span>
<span class="kw">FROM</span> <span class="jj">read_parquet</span>(
<span class="st">'s3://lake/events/year=*/month=*/*.parquet'</span>,
hive_partitioning <span class="op">=</span> <span class="kw">true</span>
);
<span class="cm">-- Schema inspection before you query</span>
<span class="kw">DESCRIBE</span> <span class="kw">SELECT</span> <span class="op">*</span> <span class="kw">FROM</span> <span class="jj">read_parquet</span>(<span class="st">'events.parquet'</span>);
<span class="cm">-- Parquet metadata (row groups, compression, stats)</span>
<span class="kw">SELECT</span> <span class="op">*</span> <span class="kw">FROM</span> <span class="jj">parquet_metadata</span>(<span class="st">'events.parquet'</span>);
<span class="kw">SELECT</span> <span class="op">*</span> <span class="kw">FROM</span> <span class="jj">parquet_schema</span>(<span class="st">'events.parquet'</span>);
<span class="kw">SELECT</span> <span class="op">*</span> <span class="kw">FROM</span> <span class="jj">parquet_file_metadata</span>(<span class="st">'events.parquet'</span>);
<span class="cm">-- Which row groups were actually scanned? (filter pushdown audit)</span>
<span class="kw">SELECT</span> row_group_id, num_rows
<span class="kw">FROM</span> <span class="jj">parquet_metadata</span>(<span class="st">'events.parquet'</span>)
<span class="kw">WHERE</span> stats_min_value <span class="op"><=</span> <span class="st">'2024-06-01'</span>
<span class="kw">AND</span> stats_max_value <span class="op">>=</span> <span class="st">'2024-06-01'</span>;</pre>
</div>
</div>
<div>
<h3>Apache Iceberg</h3>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">Iceberg — time travel + schema evolution</span><span class="code-lang">sql</span></div>
<pre><span class="cm">-- Load extension once:</span>
<span class="kw">INSTALL</span> iceberg; <span class="kw">LOAD</span> iceberg;
<span class="cm">-- Scan current snapshot (REST catalog):</span>
<span class="kw">FROM</span> iceberg_scan(<span class="st">'s3://lake/catalog/my_table'</span>);
<span class="cm">-- Time travel — snapshot_id or timestamp:</span>
<span class="kw">FROM</span> iceberg_scan(
<span class="st">'s3://lake/catalog/events'</span>,
snapshot_id <span class="op">=</span> <span class="nu">5432198765432198765</span>
);
<span class="cm">-- List all snapshots (audit trail):</span>
<span class="kw">FROM</span> iceberg_snapshots(<span class="st">'s3://lake/catalog/events'</span>);
<span class="cm">-- Schema evolution — read old snapshot with new schema mapping:</span>
<span class="kw">FROM</span> iceberg_scan(
<span class="st">'s3://lake/catalog/events'</span>,
allow_moved_paths <span class="op">=</span> <span class="kw">true</span> <span class="cm">-- handles S3 prefix changes</span>
);</pre>
</div>
<h3>Delta Lake</h3>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">Delta Lake — DML log + time travel</span><span class="code-lang">sql</span></div>
<pre><span class="kw">INSTALL</span> delta; <span class="kw">LOAD</span> delta;
<span class="cm">-- Current version:</span>
<span class="kw">FROM</span> delta_scan(<span class="st">'s3://lake/tables/orders'</span>);
<span class="cm">-- Time travel by version:</span>
<span class="kw">FROM</span> delta_scan(
<span class="st">'s3://lake/tables/orders'</span>,
version <span class="op">=</span> <span class="nu">42</span>
);
<span class="cm">-- Inspect transaction log:</span>
<span class="kw">FROM</span> delta_table_info(<span class="st">'s3://lake/tables/orders'</span>);</pre>
</div>
</div>
</div>
</div>
<!-- CLOUD STORAGE -->
<div id="ing-cloud" class="tab-pane">
<div class="g2">
<div>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">AWS S3 — full configuration</span><span class="code-lang">sql</span></div>
<pre><span class="kw">INSTALL</span> httpfs; <span class="kw">LOAD</span> httpfs;
<span class="cm">-- Option 1: env vars (AWS_ACCESS_KEY_ID etc. auto-picked up)</span>
<span class="kw">FROM</span> <span class="jj">read_parquet</span>(<span class="st">'s3://my-bucket/data/*.parquet'</span>);
<span class="cm">-- Option 2: explicit credentials</span>
<span class="kw">CREATE</span> <span class="kw">SECRET</span> aws_creds (
<span class="kw">TYPE</span> s3,
KEY_ID <span class="st">'AKIA...'</span>,
SECRET <span class="st">'secret...'</span>,
REGION <span class="st">'us-east-1'</span>
);
<span class="cm">-- Option 3: assume IAM role</span>
<span class="kw">CREATE</span> <span class="kw">SECRET</span> aws_role (
<span class="kw">TYPE</span> s3,
PROVIDER CREDENTIAL_CHAIN,
ROLE_ARN <span class="st">'arn:aws:iam::123:role/DataRole'</span>
);
<span class="cm">-- S3-compatible (MinIO, Cloudflare R2, Backblaze B2):</span>
<span class="kw">CREATE</span> <span class="kw">SECRET</span> r2 (
<span class="kw">TYPE</span> s3,
KEY_ID <span class="st">'...'</span>,
SECRET <span class="st">'...'</span>,
ENDPOINT <span class="st">'account.r2.cloudflarestorage.com'</span>,
URL_STYLE <span class="st">'path'</span>
);
<span class="cm">-- Write back to S3 — it's bidirectional:</span>
<span class="kw">COPY</span> (
<span class="kw">SELECT</span> <span class="op">*</span> <span class="kw">FROM</span> orders <span class="kw">WHERE</span> year <span class="op">=</span> <span class="nu">2024</span>
) <span class="kw">TO</span> <span class="st">'s3://output/orders_2024.parquet'</span>
(<span class="kw">FORMAT</span> parquet, COMPRESSION zstd, ROW_GROUP_SIZE <span class="nu">122880</span>);</pre>
</div>
</div>
<div>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">GCS + Azure Blob</span><span class="code-lang">sql</span></div>
<pre><span class="cm">-- Google Cloud Storage</span>
<span class="kw">CREATE</span> <span class="kw">SECRET</span> gcs_key (
<span class="kw">TYPE</span> gcs,
KEY_ID <span class="st">'service-account@proj.iam.gserviceaccount.com'</span>,
SECRET <span class="st">'-----BEGIN PRIVATE KEY-----...'</span>
);
<span class="kw">FROM</span> <span class="jj">read_parquet</span>(<span class="st">'gs://bucket/events/*.parquet'</span>);
<span class="cm">-- Azure Blob Storage</span>
<span class="kw">INSTALL</span> azure; <span class="kw">LOAD</span> azure;
<span class="kw">CREATE</span> <span class="kw">SECRET</span> az (
<span class="kw">TYPE</span> azure,
CONNECTION_STRING <span class="st">'DefaultEndpointsProtocol=https;AccountName=...'</span>
);
<span class="kw">FROM</span> <span class="jj">read_parquet</span>(<span class="st">'az://container/events/*.parquet'</span>);</pre>
</div>
<div class="callout c-lime">
<strong>MULTI-CLOUD FEDERATED QUERY — YES, REALLY</strong>
Create secrets for S3, GCS, and Azure. Then write a single query that JOINs across all three. DuckDB resolves the protocol from the URI prefix and reads in parallel. No data movement required.
</div>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">Cross-cloud JOIN</span><span class="code-lang">sql</span></div>
<pre><span class="kw">SELECT</span> s.order_id, g.customer_name, a.campaign_source
<span class="kw">FROM</span> <span class="jj">read_parquet</span>(<span class="st">'s3://orders/2024/*.parquet'</span>) s
<span class="kw">JOIN</span> <span class="jj">read_parquet</span>(<span class="st">'gs://crm/customers/*.parquet'</span>) g
<span class="kw">USING</span> (customer_id)
<span class="kw">JOIN</span> <span class="jj">read_parquet</span>(<span class="st">'az://marketing/campaigns/*.parquet'</span>) a
<span class="kw">ON</span> s.utm_source <span class="op">=</span> a.source_key
<span class="kw">WHERE</span> s.created_at <span class="op">>=</span> <span class="st">'2024-01-01'</span>;</pre>
</div>
</div>
</div>
</div>
<!-- CSV / JSON -->
<div id="ing-csv" class="tab-pane">
<div class="g2">
<div>
<h3>CSV — The Auto-Detective</h3>
<div class="code-wrap">
<div class="code-header"><span class="code-fname">read_csv — schema inference</span><span class="code-lang">sql</span></div>
<pre><span class="cm">-- auto_detect sniffs delimiter, quoting, types, header</span>
<span class="kw">FROM</span> <span class="jj">read_csv</span>(<span class="st">'data.csv'</span>, auto_detect <span class="op">=</span> <span class="kw">true</span>);
<span class="cm">-- What did DuckDB infer? (super useful for debugging)</span>
<span class="kw">SELECT</span> <span class="op">*</span> <span class="kw">FROM</span> <span class="jj">sniff_csv</span>(<span class="st">'messy.csv'</span>);
<span class="cm">-- returns: delimiter, quoting, types, has_header, etc.</span>
<span class="cm">-- Explicit control when auto-detect fails:</span>
<span class="kw">FROM</span> <span class="jj">read_csv</span>(<span class="st">'pipe_delimited.txt'</span>,
delim <span class="op">=</span> <span class="st">'|'</span>,
header <span class="op">=</span> <span class="kw">true</span>,