diff --git a/.gitignore b/.gitignore
index 4934ad1..67a47a4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -193,3 +193,5 @@ pyrightconfig.json
 .ionide
 
 # End of https://www.toptal.com/developers/gitignore/api/python,visualstudiocode
+
+.claude/
diff --git a/README.md b/README.md
index cddef56..4a55f2a 100644
--- a/README.md
+++ b/README.md
@@ -54,6 +54,7 @@ Deploy Memgraph using methods that suit your environment, whether it's container
 
 ### Import
 - [PySpark integration (CSV -> PySpark -> Memgraph)](./pyspark_integration/)
+- [Importing data from Apache Iceberg (PyIceberg)](./import/iceberg/)
 - [Importing data from Arrow Flight](./import/migrate/arrow-flight/)
 - [Importing data with DuckDB](./import/migrate/duckdb/)
 - [Importing data from Amazon Aurora/MySQL](./import/migrate/amazon_aurora/)
diff --git a/import/iceberg/.gitignore b/import/iceberg/.gitignore
new file mode 100644
index 0000000..a243d3c
--- /dev/null
+++ b/import/iceberg/.gitignore
@@ -0,0 +1,2 @@
+warehouse/
+.venv/
diff --git a/import/iceberg/README.md b/import/iceberg/README.md
new file mode 100644
index 0000000..a8be527
--- /dev/null
+++ b/import/iceberg/README.md
@@ -0,0 +1,162 @@
+# Loading Apache Iceberg into Memgraph
+
+Ingest data from [Apache Iceberg](https://iceberg.apache.org/) tables into Memgraph as a property graph.
+
+- **Edition**: Community
+- **Tested on**: Memgraph **v3.9**
+- **Source backends**: PyIceberg, DuckDB (more coming: LOAD CSV/Parquet)
+
+## What this example does
+
+1. [`generate_iceberg.py`](./generate_iceberg.py) generates **1,000,000 users** and **5,000,000 transactions** with numpy and writes them to a local Iceberg warehouse via a PyIceberg `SqlCatalog` (SQLite metadata, local-filesystem warehouse). Zero infra. Sizes are configurable via `--users` / `--transactions`.
+2. [`iceberg_to_memgraph.py`](./iceberg_to_memgraph.py) reads those Iceberg tables and writes them to Memgraph as `(:User)-[:SENT]->(:User)` using batched `UNWIND` queries via [gqlalchemy](https://github.com/memgraph/gqlalchemy) (Memgraph's Python client, built on `pymgclient`).
+
+The reported elapsed time covers only the source → Memgraph ingestion (schema setup and graph reset are excluded), so the number reflects actual data movement.
+
+## Why a local Iceberg catalog?
+
+In production, Iceberg tables live in a data lake (S3 + AWS Glue / REST / Hive catalog). The local SQL catalog used here is a reproducible stand-in so the example runs end-to-end on a fresh clone — only the catalog config block in [`loaders/pyiceberg_loader.py`](./loaders/pyiceberg_loader.py) changes when you point at a real lake.
+
+## Why gqlalchemy and not the official `neo4j` driver?
+
+**Use [gqlalchemy](https://github.com/memgraph/gqlalchemy) when writing to Memgraph.** In our tests on this workload (1M users + 5M transactions, batched UNWIND), gqlalchemy delivered **at least a 2× ingestion throughput improvement** over the `neo4j` Python driver at the same `--workers` and `--batch-size`.
+
+Why: gqlalchemy talks to Memgraph through [`pymgclient`](https://github.com/memgraph/pymgclient), a native C client built specifically for Memgraph's Bolt implementation. The `neo4j` driver is generic Bolt and adds per-call overhead (transactional bookmarking, server-routing checks, result-buffering) that bulk loads pay for on every batch but don't benefit from. For write-heavy ingestion via `UNWIND`, that overhead dominates.
+
+Rule of thumb for this repo:
+- **Writing to Memgraph** (ingest, ETL, bulk load): use gqlalchemy.
+- **Read-mostly app code** where you want OGM ergonomics (Python classes ↔ nodes, query builder): also gqlalchemy.
+- **Cross-vendor code that must run against Neo4j too**: use the `neo4j` driver.
+
+## Run
+
+### 1. Start Memgraph
+
+```bash
+docker compose up -d
+```
+
+Starts Memgraph 3.9 (Community) on `bolt://localhost:7687`. To inspect results visually, also run [Memgraph Lab](https://memgraph.com/docs/data-visualization).
+
+### 2. Install dependencies
+
+This example uses [uv](https://docs.astral.sh/uv/). Install it once if you don't have it (`curl -LsSf https://astral.sh/uv/install.sh | sh`), then sync the project env:
+
+```bash
+uv sync
+```
+
+### 3. Generate the Iceberg warehouse (once)
+
+```bash
+uv run python generate_iceberg.py
+```
+
+Expected output:
+
+```
+Generating 1,000,000 users...
+Generating 5,000,000 transactions...
+Writing Iceberg tables...
+
+users:        1,000,000 rows
+transactions: 5,000,000 rows
+warehouse:    .../import/iceberg/warehouse
+```
+
+Generation takes ~10–20s and produces a few hundred MB of Parquet under `warehouse/`. Re-running drops and recreates the tables.
+
+For a quick smoke test, scale down: `uv run python generate_iceberg.py --users 10000 --transactions 50000`.
+
+### 4. Load Iceberg into Memgraph
+
+Pick a source backend; optionally tune parallelism and batch size:
+
+```bash
+uv run python iceberg_to_memgraph.py --source pyiceberg                          # defaults
+uv run python iceberg_to_memgraph.py --source duckdb --workers 8                 # 8 parallel writers
+uv run python iceberg_to_memgraph.py --source pyiceberg --workers 8 --batch-size 50000
+```
+
+Expected output:
+
+```
+[pyiceberg, workers=1,  batch=10000, user-write=create] Ingested into Memgraph in 142.37s
+[duckdb,    workers=8,  batch=10000, user-write=create] Ingested into Memgraph in  31.04s
+[pyiceberg, workers=20, batch=25000, user-write=create] Ingested into Memgraph in  24.34s
+```
+
+Flags:
+
+- `--workers N` — N parallel Bolt sessions writing to Memgraph. A single reader thread fills a bounded queue; N writer threads drain it. Memgraph is in analytical mode for the duration (set by `prepare_graph`), so concurrent writes don't conflict.
+- `--batch-size N` — rows per UNWIND batch / Bolt round trip. Default 10000. Bigger batches amortize round-trip overhead but raise per-call memory; smaller batches expose more concurrency to workers but spend more time in network framing.
+
+Tuning order: get `--batch-size` into the right ballpark first (1k–50k is typical), then crank `--workers` up to roughly the CPU count of the server. Comparing `pyiceberg` vs `duckdb` at equal `--workers` and `--batch-size` isolates the scan engine difference; the writer side is identical.
+
+Exact time depends on hardware. The default sizes (1M nodes + 5M edges) typically ingest in 2–5 minutes single-threaded and noticeably faster with `--workers 4..16`.
+
+## Verify
+
+Connect via Memgraph Lab or `mgconsole` and run:
+
+```cypher
+MATCH (u:User)-[t:SENT]->(v:User)
+RETURN u.name AS sender, v.name AS receiver, t.amount, t.ts
+ORDER BY t.amount DESC
+LIMIT 10;
+```
+
+You should see the top 10 transactions with sender/receiver names. To verify counts:
+
+```cypher
+MATCH (u:User) RETURN count(u);          // 1,000,000
+MATCH ()-[t:SENT]->() RETURN count(t);   // 5,000,000
+```
+
+## Architecture
+
+```
+generate_iceberg.py  ──► warehouse/  (local Iceberg, 1M users + 5M txs)
+                              │
+                              ▼
+              loaders/{pyiceberg,duckdb}_loader.py
+                              │
+                              ▼
+                    iceberg_to_memgraph.py ──► Memgraph (Bolt)
+```
+
+[`iceberg_to_memgraph.py`](./iceberg_to_memgraph.py) is backend-agnostic — it talks to the `Loader` interface defined in [`loaders/base.py`](./loaders/base.py).
+
+### Adding a new source backend
+
+1. Implement `loaders/<your>.py` with `users()` and `transactions()` returning iterators of dict batches.
+2. Register it in [`loaders/__init__.py`](./loaders/__init__.py) under `LOADERS`.
+3. Run `python iceberg_to_memgraph.py --source <your>`.
+
+### Pointing at a real Iceberg lake
+
+Replace the `SqlCatalog(...)` block in [`loaders/pyiceberg_loader.py`](./loaders/pyiceberg_loader.py) with a configured catalog, e.g.:
+
+```python
+from pyiceberg.catalog import load_catalog
+
+self.catalog = load_catalog("prod", **{
+    "type": "rest",
+    "uri":  "https://catalog.example.com",
+    # ...credentials, warehouse, etc.
+})
+```
+
+The rest of the loader and the entire writer stay the same.
+
+## Files
+
+| File | Purpose |
+| --- | --- |
+| [`generate_iceberg.py`](./generate_iceberg.py) | One-time: generate 1M users + 5M txs into local Iceberg tables |
+| [`loaders/base.py`](./loaders/base.py) | Abstract `Loader` interface |
+| [`loaders/pyiceberg_loader.py`](./loaders/pyiceberg_loader.py) | PyIceberg implementation |
+| [`loaders/duckdb_loader.py`](./loaders/duckdb_loader.py) | DuckDB `iceberg_scan` implementation |
+| [`iceberg_to_memgraph.py`](./iceberg_to_memgraph.py) | Backend-agnostic CLI loader |
+| [`docker-compose.yml`](./docker-compose.yml) | Memgraph 3.9 (Community) |
+| `warehouse/` | Generated by `generate_iceberg.py`, gitignored |
diff --git a/import/iceberg/docker-compose.yml b/import/iceberg/docker-compose.yml
new file mode 100644
index 0000000..45ccbdd
--- /dev/null
+++ b/import/iceberg/docker-compose.yml
@@ -0,0 +1,13 @@
+services:
+  memgraph:
+    image: memgraph/memgraph-mage:3.9.0
+    container_name: iceberg-memgraph
+    ports:
+      - "7687:7687"
+      - "7444:7444"
+    command:
+      - "--log-level=TRACE"
+      - "--also-log-to-stderr=true"
+      - "--query-execution-timeout-sec=0"
+      - "--storage-snapshot-interval-sec=0"
+      - "--storage-wal-enabled=false"
diff --git a/import/iceberg/generate_iceberg.py b/import/iceberg/generate_iceberg.py
new file mode 100644
index 0000000..6671c17
--- /dev/null
+++ b/import/iceberg/generate_iceberg.py
@@ -0,0 +1,181 @@
+"""Generate users + transactions and write them to a local Iceberg warehouse.
+
+Defaults: 1,000,000 users and 5,000,000 transactions.
+
+Generation is vectorized with numpy, so producing 1M+5M rows takes only a
+few seconds. Random seeds are fixed, so runs are deterministic.
+
+In production your Iceberg tables already live in your data lake (S3 + Glue
+or REST catalog). This script just creates a reproducible stand-in so the
+example runs end-to-end on a fresh clone — only the catalog config block
+below changes when you point at a real lake.
+"""
+import argparse
+from pathlib import Path
+
+import numpy as np
+import pyarrow as pa
+from pyiceberg.catalog.sql import SqlCatalog
+from pyiceberg.exceptions import NoSuchNamespaceError, NoSuchTableError
+from pyiceberg.io.pyarrow import schema_to_pyarrow
+from pyiceberg.schema import Schema
+from pyiceberg.types import (
+    DoubleType,
+    LongType,
+    NestedField,
+    StringType,
+    TimestampType,
+)
+
+ROOT = Path(__file__).resolve().parent
+WAREHOUSE = ROOT / "warehouse"
+NAMESPACE = "default"
+
+DEFAULT_USERS = 1_000_000
+DEFAULT_TXS = 5_000_000
+
+COUNTRIES = ["US", "UK", "DE", "FR", "JP", "ES", "IT", "BR", "IN", "CA"]
+
+USERS_SCHEMA = Schema(
+    NestedField(1, "user_id", LongType(), required=True),
+    NestedField(2, "name", StringType()),
+    NestedField(3, "email", StringType()),
+    NestedField(4, "country", StringType()),
+)
+
+TX_SCHEMA = Schema(
+    NestedField(1, "tx_id", LongType(), required=True),
+    NestedField(2, "from_user", LongType(), required=True),
+    NestedField(3, "to_user", LongType(), required=True),
+    NestedField(4, "amount", DoubleType()),
+    NestedField(5, "timestamp", TimestampType()),
+)
+
+
+def generate_users(n: int, seed: int = 42) -> pa.Table:
+    rng = np.random.default_rng(seed)
+    user_ids = np.arange(1, n + 1, dtype=np.int64)
+    countries = np.array(COUNTRIES)[rng.integers(0, len(COUNTRIES), size=n)]
+
+    # Synthetic but unique per user. List comp on 1M rows takes ~1s.
+    names = [f"User_{i:07d}" for i in user_ids]
+    emails = [f"user{i}@example.com" for i in user_ids]
+
+    # Derive the PyArrow schema from the Iceberg schema so column nullability
+    # matches `required=True` declarations — otherwise iceberg_table.append()
+    # rejects the table with a "required vs optional" mismatch.
+    return pa.table(
+        {
+            "user_id": user_ids,
+            "name": names,
+            "email": emails,
+            "country": countries,
+        },
+        schema=schema_to_pyarrow(USERS_SCHEMA),
+    )
+
+
+def generate_transactions(n_txs: int, n_users: int, seed: int = 43) -> pa.Table:
+    rng = np.random.default_rng(seed)
+    tx_ids = np.arange(1, n_txs + 1, dtype=np.int64)
+    from_user = rng.integers(1, n_users + 1, size=n_txs, dtype=np.int64)
+    to_user = rng.integers(1, n_users + 1, size=n_txs, dtype=np.int64)
+
+    # Avoid self-loops: bump any equal target by 1 (wrapping at n_users).
+    eq = from_user == to_user
+    to_user[eq] = (to_user[eq] % n_users) + 1
+
+    amount = rng.uniform(1.0, 10000.0, size=n_txs).round(2)
+
+    # Spread timestamps uniformly across one year (microsecond precision).
+    base = np.datetime64("2025-01-01T00:00:00", "us")
+    seconds_in_year = 365 * 24 * 60 * 60
+    offsets_us = (
+        rng.integers(0, seconds_in_year, size=n_txs) * 1_000_000
+    ).astype("timedelta64[us]")
+    timestamps = base + offsets_us
+
+    return pa.table(
+        {
+            "tx_id": tx_ids,
+            "from_user": from_user,
+            "to_user": to_user,
+            "amount": amount,
+            "timestamp": timestamps,
+        },
+        schema=schema_to_pyarrow(TX_SCHEMA),
+    )
+
+
+def get_catalog() -> SqlCatalog:
+    WAREHOUSE.mkdir(parents=True, exist_ok=True)
+    return SqlCatalog(
+        "default",
+        **{
+            "uri": f"sqlite:///{WAREHOUSE}/catalog.db",
+            "warehouse": f"file://{WAREHOUSE}",
+        },
+    )
+
+
+def ensure_namespace(catalog: SqlCatalog, namespace: str) -> None:
+    try:
+        catalog.load_namespace_properties(namespace)
+    except NoSuchNamespaceError:
+        catalog.create_namespace(namespace)
+
+
+def reset_table(catalog: SqlCatalog, name: str, schema: Schema):
+    full = f"{NAMESPACE}.{name}"
+    try:
+        catalog.drop_table(full)
+    except NoSuchTableError:
+        pass
+    return catalog.create_table(full, schema=schema)
+
+
+def write_iceberg(
+    catalog: SqlCatalog, name: str, schema: Schema, arrow_table: pa.Table
+):
+    iceberg_table = reset_table(catalog, name, schema)
+    iceberg_table.append(arrow_table)
+    return iceberg_table
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Generate synthetic users + transactions into local Iceberg tables."
+    )
+    parser.add_argument(
+        "--users",
+        type=int,
+        default=DEFAULT_USERS,
+        help=f"Number of users to generate (default: {DEFAULT_USERS:,})",
+    )
+    parser.add_argument(
+        "--transactions",
+        type=int,
+        default=DEFAULT_TXS,
+        help=f"Number of transactions to generate (default: {DEFAULT_TXS:,})",
+    )
+    args = parser.parse_args()
+
+    catalog = get_catalog()
+    ensure_namespace(catalog, NAMESPACE)
+
+    print(f"Generating {args.users:,} users...")
+    users_table = generate_users(args.users)
+    print(f"Generating {args.transactions:,} transactions...")
+    txs_table = generate_transactions(args.transactions, args.users)
+
+    print("Writing Iceberg tables...")
+    write_iceberg(catalog, "users", USERS_SCHEMA, users_table)
+    write_iceberg(catalog, "transactions", TX_SCHEMA, txs_table)
+
+    print(f"\nusers:        {args.users:,} rows")
+    print(f"transactions: {args.transactions:,} rows")
+    print(f"warehouse:    {WAREHOUSE}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/import/iceberg/iceberg_to_memgraph.py b/import/iceberg/iceberg_to_memgraph.py
new file mode 100644
index 0000000..674efcc
--- /dev/null
+++ b/import/iceberg/iceberg_to_memgraph.py
@@ -0,0 +1,223 @@
+"""Load users + transactions into Memgraph from a configurable source.
+
+The source backend (PyIceberg, DuckDB) is swappable via --source. Only the
+read side differs — the writer below is identical for every backend.
+
+Use --workers N to fan out N parallel Bolt writer processes. The single
+reader feeds a multiprocessing queue; each worker process drains it via
+its own gqlalchemy Memgraph connection. Memgraph runs in analytical mode
+(set in prepare_graph), so concurrent writes don't conflict.
+
+The reported elapsed time covers the source -> Memgraph ingestion only:
+schema setup and graph reset are excluded so the number reflects actual
+data movement, not one-time bookkeeping.
+"""
+import argparse
+import multiprocessing as mp
+import time
+from typing import Iterable
+from urllib.parse import urlparse
+
+from gqlalchemy import Memgraph
+
+from loaders import available_sources, get_loader
+from loaders.base import Loader
+
+USER_CREATE_QUERY = """
+UNWIND $rows AS r
+CREATE (u:User {id: r.user_id, name: r.name, email: r.email, country: r.country})
+"""
+
+USER_MERGE_QUERY = """
+UNWIND $rows AS r
+MERGE (u:User {id: r.user_id})
+SET u.name    = r.name,
+    u.email   = r.email,
+    u.country = r.country
+"""
+
+USER_QUERIES = {"create": USER_CREATE_QUERY, "merge": USER_MERGE_QUERY}
+
+TX_BATCH_QUERY = """
+UNWIND $rows AS r
+MATCH (a:User {id: r.from_user})
+MATCH (b:User {id: r.to_user})
+CREATE (a)-[:SENT {tx_id: r.tx_id, amount: r.amount, ts: r.timestamp}]->(b)
+"""
+
+_SHUTDOWN = "__SHUTDOWN__"  # string sentinel survives pickling across processes
+
+
+def _parse_uri(uri: str) -> tuple[str, int]:
+    parsed = urlparse(uri)
+    return parsed.hostname or "localhost", parsed.port or 7687
+
+
+def prepare_graph(db: Memgraph) -> None:
+    """Pre-ingestion setup: switch to analytical mode for fast bulk writes,
+    wipe the graph (DROP GRAPH also clears schema/indexes), then create
+    the label and label-property indexes used by the loader."""
+    db.execute("STORAGE MODE IN_MEMORY_ANALYTICAL")
+    db.execute("DROP GRAPH")
+    db.execute("CREATE INDEX ON :User")
+    db.execute("CREATE INDEX ON :User(id)")
+
+
+def write_serial(
+    db: Memgraph, label: str, query: str, batches: Iterable[list[dict]]
+) -> None:
+    for i, batch in enumerate(batches, start=1):
+        db.execute(query, parameters={"rows": batch})
+        print(f"[{label}] batch {i} ({len(batch)} rows) done")
+
+
+def _writer_process(
+    host: str, port: int, label: str, query: str, q: "mp.Queue"
+) -> None:
+    """Worker entry point. Each process owns its Memgraph connection — Bolt
+    sockets aren't safe to share across forked processes."""
+    name = mp.current_process().name
+    db = Memgraph(host=host, port=port)
+    while True:
+        item = q.get()
+        if item == _SHUTDOWN:
+            return
+        db.execute(query, parameters={"rows": item})
+        print(f"[{label}] {name} finished batch ({len(item)} rows)")
+
+
+def write_parallel(
+    host: str,
+    port: int,
+    label: str,
+    query: str,
+    batches: Iterable[list[dict]],
+    n_workers: int,
+) -> None:
+    """Producer-consumer: the main process reads from the loader and pushes
+    batches onto a multiprocessing queue; n_workers writer processes pull
+    and run UNWIND/MERGE on their own Memgraph connections."""
+    q: "mp.Queue" = mp.Queue(maxsize=n_workers * 4)
+    procs = [
+        mp.Process(
+            target=_writer_process,
+            args=(host, port, label, query, q),
+            name=f"writer-{label}-{i}",
+        )
+        for i in range(n_workers)
+    ]
+    for p in procs:
+        p.start()
+    try:
+        for batch in batches:
+            q.put(batch)
+    finally:
+        for _ in range(n_workers):
+            q.put(_SHUTDOWN)
+    for p in procs:
+        p.join()
+
+
+def ingest(
+    db: Memgraph,
+    host: str,
+    port: int,
+    loader: Loader,
+    n_workers: int,
+    user_query: str,
+) -> float:
+    """Time the source -> Memgraph ingestion (excludes prepare_graph)."""
+    start = time.perf_counter()
+
+    if n_workers <= 1:
+        write_serial(db, "users", user_query, loader.users())
+        write_serial(db, "tx", TX_BATCH_QUERY, loader.transactions())
+    else:
+        # Users must finish before transactions: the tx batch MATCHes them.
+        write_parallel(host, port, "users", user_query, loader.users(), n_workers)
+        write_parallel(host, port, "tx", TX_BATCH_QUERY, loader.transactions(), n_workers)
+
+    return time.perf_counter() - start
+
+
+def dry_run(loader: Loader) -> float:
+    """Iterate the loader without writing — times batch preparation only."""
+    start = time.perf_counter()
+
+    for label, batches in (("users", loader.users()), ("tx", loader.transactions())):
+        for i, batch in enumerate(batches, start=1):
+            print(f"[{label}] batch {i} ({len(batch)} rows) prepared")
+
+    return time.perf_counter() - start
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Load users + transactions into Memgraph from various sources."
+    )
+    parser.add_argument(
+        "--source",
+        choices=available_sources(),
+        default="pyiceberg",
+        help="Data source backend (default: pyiceberg).",
+    )
+    parser.add_argument(
+        "--uri",
+        default="bolt://localhost:7687",
+        help="Memgraph Bolt URI (default: bolt://localhost:7687).",
+    )
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=1,
+        help="Number of parallel Bolt writer processes (default: 1).",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=10_000,
+        help="Rows per UNWIND batch / Bolt round trip (default: 10000).",
+    )
+    parser.add_argument(
+        "--user-write",
+        choices=tuple(USER_QUERIES),
+        default="create",
+        help="How to write user nodes: 'create' (faster, assumes clean graph) "
+             "or 'merge' (idempotent, safe for re-runs). Default: create.",
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Iterate source batches without writing to Memgraph; reports prep time only.",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    loader = get_loader(args.source, batch_size=args.batch_size)
+
+    if args.dry_run:
+        elapsed = dry_run(loader)
+        print(
+            f"[{args.source}, batch={args.batch_size}, dry-run] "
+            f"Prepared source batches in {elapsed:.2f}s"
+        )
+        return
+
+    host, port = _parse_uri(args.uri)
+    db = Memgraph(host=host, port=port)
+    prepare_graph(db)
+    elapsed = ingest(
+        db, host, port, loader, args.workers, USER_QUERIES[args.user_write]
+    )
+
+    print(
+        f"[{args.source}, workers={args.workers}, batch={args.batch_size}, "
+        f"user-write={args.user_write}] "
+        f"Ingested into Memgraph in {elapsed:.2f}s"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/import/iceberg/loaders/__init__.py b/import/iceberg/loaders/__init__.py
new file mode 100644
index 0000000..af4677c
--- /dev/null
+++ b/import/iceberg/loaders/__init__.py
@@ -0,0 +1,29 @@
+"""Source-backend registry. Add new backends here.
+
+To plug in a new source (e.g. DuckDB, raw Parquet, LOAD CSV):
+
+    1. Implement loaders/<name>.py with a Loader subclass.
+    2. Register it in LOADERS below.
+    3. Run: python iceberg_to_memgraph.py --source <name>
+"""
+from .base import Loader
+from .duckdb_loader import DuckDBLoader
+from .pyiceberg_loader import PyIcebergLoader
+
+LOADERS: dict[str, type[Loader]] = {
+    "pyiceberg": PyIcebergLoader,
+    "duckdb": DuckDBLoader,
+    # "parquet":  ParquetLoader,   # TODO
+}
+
+
+def get_loader(source: str, batch_size: int = 10_000) -> Loader:
+    if source not in LOADERS:
+        raise ValueError(
+            f"Unknown source: {source!r}. Available: {sorted(LOADERS)}"
+        )
+    return LOADERS[source](batch_size=batch_size)
+
+
+def available_sources() -> list[str]:
+    return sorted(LOADERS)
diff --git a/import/iceberg/loaders/base.py b/import/iceberg/loaders/base.py
new file mode 100644
index 0000000..4f0ccd6
--- /dev/null
+++ b/import/iceberg/loaders/base.py
@@ -0,0 +1,24 @@
+"""Common interface every source backend must implement.
+
+The point of this abstraction is that iceberg_to_memgraph.py is identical
+regardless of where the data is read from — Iceberg via PyIceberg, Iceberg
+via DuckDB, raw Parquet, CSV, etc. Each backend yields batches of plain
+dicts; the writer turns them into Cypher.
+"""
+from abc import ABC, abstractmethod
+from typing import Iterator
+
+
+class Loader(ABC):
+    """Yield users and transactions as batches of dicts.
+
+    Keep the dict keys aligned with the Cypher in iceberg_to_memgraph.py:
+        users:        user_id, name, email, country
+        transactions: tx_id, from_user, to_user, amount, timestamp
+    """
+
+    @abstractmethod
+    def users(self) -> Iterator[list[dict]]: ...
+
+    @abstractmethod
+    def transactions(self) -> Iterator[list[dict]]: ...
diff --git a/import/iceberg/loaders/duckdb_loader.py b/import/iceberg/loaders/duckdb_loader.py
new file mode 100644
index 0000000..172c690
--- /dev/null
+++ b/import/iceberg/loaders/duckdb_loader.py
@@ -0,0 +1,60 @@
+"""Read Iceberg tables via DuckDB's iceberg extension.
+
+PyIceberg is used only to resolve the latest metadata.json location from
+the catalog. The actual table scan is performed by DuckDB's vectorized
+engine — typically faster than PyIceberg's pure-Python scan on larger
+tables, and a useful A/B against the pyiceberg backend.
+"""
+from pathlib import Path
+from typing import Iterator
+
+import duckdb
+from pyiceberg.catalog.sql import SqlCatalog
+
+from .base import Loader
+
+DEFAULT_WAREHOUSE = Path(__file__).resolve().parent.parent / "warehouse"
+NAMESPACE = "default"
+
+
+class DuckDBLoader(Loader):
+    def __init__(
+        self,
+        warehouse: Path = DEFAULT_WAREHOUSE,
+        batch_size: int = 10_000,
+    ) -> None:
+        self.batch_size = batch_size
+        self.catalog = SqlCatalog(
+            "default",
+            **{
+                "uri": f"sqlite:///{warehouse}/catalog.db",
+                "warehouse": f"file://{warehouse}",
+            },
+        )
+        # The iceberg extension is community-maintained and not bundled with
+        # DuckDB; INSTALL is idempotent so it's safe on every run.
+        self.conn = duckdb.connect()
+        self.conn.execute("INSTALL iceberg")
+        self.conn.execute("LOAD iceberg")
+
+    def _metadata_path(self, table_name: str) -> str:
+        # PyIceberg returns "file:///..." which DuckDB on the local FS reads
+        # fine without the scheme prefix.
+        loc = self.catalog.load_table(
+            f"{NAMESPACE}.{table_name}"
+        ).metadata_location
+        return loc[len("file://") :] if loc.startswith("file://") else loc
+
+    def _scan(self, table_name: str) -> Iterator[list[dict]]:
+        path = self._metadata_path(table_name)
+        arrow = self.conn.execute(
+            "SELECT * FROM iceberg_scan(?)", [path]
+        ).fetch_arrow_table()
+        for batch in arrow.to_batches(max_chunksize=self.batch_size):
+            yield batch.to_pylist()
+
+    def users(self) -> Iterator[list[dict]]:
+        return self._scan("users")
+
+    def transactions(self) -> Iterator[list[dict]]:
+        return self._scan("transactions")
diff --git a/import/iceberg/loaders/pyiceberg_loader.py b/import/iceberg/loaders/pyiceberg_loader.py
new file mode 100644
index 0000000..11ecec0
--- /dev/null
+++ b/import/iceberg/loaders/pyiceberg_loader.py
@@ -0,0 +1,41 @@
+"""Read Iceberg tables via PyIceberg's local SQL catalog.
+
+Swap the SqlCatalog construction below for `load_catalog("rest", ...)` or
+`load_catalog("glue", ...)` to point at a real lake — the rest of the
+loader (and iceberg_to_memgraph.py) is unchanged.
+"""
+from pathlib import Path
+from typing import Iterator
+
+from pyiceberg.catalog.sql import SqlCatalog
+
+from .base import Loader
+
+DEFAULT_WAREHOUSE = Path(__file__).resolve().parent.parent / "warehouse"
+NAMESPACE = "default"
+
+
+class PyIcebergLoader(Loader):
+    def __init__(
+        self, warehouse: Path = DEFAULT_WAREHOUSE, batch_size: int = 10_000
+    ) -> None:
+        self.batch_size = batch_size
+        self.catalog = SqlCatalog(
+            "default",
+            **{
+                "uri": f"sqlite:///{warehouse}/catalog.db",
+                "warehouse": f"file://{warehouse}",
+            },
+        )
+
+    def _scan(self, table_name: str) -> Iterator[list[dict]]:
+        table = self.catalog.load_table(f"{NAMESPACE}.{table_name}")
+        arrow = table.scan().to_arrow()
+        for batch in arrow.to_batches(max_chunksize=self.batch_size):
+            yield batch.to_pylist()
+
+    def users(self) -> Iterator[list[dict]]:
+        return self._scan("users")
+
+    def transactions(self) -> Iterator[list[dict]]:
+        return self._scan("transactions")
diff --git a/import/iceberg/pyproject.toml b/import/iceberg/pyproject.toml
new file mode 100644
index 0000000..8fa1475
--- /dev/null
+++ b/import/iceberg/pyproject.toml
@@ -0,0 +1,12 @@
+[project]
+name = "iceberg-to-memgraph"
+version = "0.1.0"
+description = "Load Apache Iceberg tables into Memgraph as a property graph."
+requires-python = ">=3.10"
+dependencies = [
+    "pyiceberg[sql-sqlite,pyarrow]>=0.7.0",
+    "pyarrow>=14.0.0",
+    "numpy>=1.26.0",
+    "gqlalchemy>=1.5.0",
+    "duckdb>=0.10.0",
+]
diff --git a/import/iceberg/uv.lock b/import/iceberg/uv.lock
new file mode 100644
index 0000000..c8f2195
--- /dev/null
+++ b/import/iceberg/uv.lock
@@ -0,0 +1,2355 @@
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diff --git a/skills/README.md b/skills/README.md
new file mode 100644
index 0000000..8392bac
--- /dev/null
+++ b/skills/README.md
@@ -0,0 +1,19 @@
+# Skills
+
+Reusable [Claude Code](https://docs.claude.com/en/docs/claude-code) skills for contributors to this repo.
+
+## Available skills
+
+- [`add-best-practice`](./add-best-practice/SKILL.md) — checklist & workflow for adding a new best practice example.
+
+## Install
+
+Run once from the repo root to symlink every skill into `.claude/skills/` so Claude Code discovers them as slash commands:
+
+```bash
+./skills/install.sh
+```
+
+After install, invoke a skill in Claude Code with its slash command, e.g. `/add-best-practice`.
+
+> `.claude/` is gitignored, so the symlinks live only in your local checkout. Re-run `install.sh` whenever a new skill is added.
diff --git a/skills/add-best-practice/SKILL.md b/skills/add-best-practice/SKILL.md
new file mode 100644
index 0000000..3a96138
--- /dev/null
+++ b/skills/add-best-practice/SKILL.md
@@ -0,0 +1,33 @@
+---
+name: add-best-practice
+description: Use this skill when adding a new best practice example to the Memgraph best-practices repository. Walks through categorization, code documentation, dependencies, and README updates so the contribution matches the repo's conventions.
+---
+
+# Add a Memgraph best practice
+
+Follow this checklist when adding a new best practice to this repo. Work through each item — do not skip steps.
+
+## General checklist
+
+- [ ] **Categorize the best practice** inside one of the existing top-level directories (e.g. `import/`, `querying/`, `graphrag/`, `ha/`, `debugging/`, `k8s/`, `python/`, `java/`, `use_cases/`, etc.). If no directory fits, ask the user before creating a new top-level category.
+- [ ] **Provide the code** for the best practice in its own subdirectory under the chosen category.
+- [ ] **Document the code thoroughly** — inline comments where intent is non-obvious, plus a `README.md` in the example directory explaining what it does and why.
+- [ ] **Add a `requirements.txt`** (or equivalent dependency manifest for non-Python examples) so users can install dependencies with one command.
+- [ ] **Add the example to the main [`README.md`](../../README.md)** under the appropriate section in the "List of best practices".
+
+## Example `README.md` checklist
+
+The `README.md` inside the new best-practice directory must include:
+
+- [ ] **Steps to run Memgraph** — the exact `docker run` / `docker compose` / install command needed for this example. Include any non-default flags (e.g. `--schema-info-enabled`, `--storage-mode`).
+- [ ] **Tested Memgraph version** — pin the version you verified the example against (e.g. "Tested on Memgraph 2.18.1").
+- [ ] **Edition** — state explicitly whether this is a **Community** or **Enterprise** capability. If Enterprise, mention the license requirement.
+
+## Workflow
+
+1. Ask the user which category the best practice belongs to (if not obvious).
+2. Create the example directory and code.
+3. Write the example `README.md` with all required sections above.
+4. Add `requirements.txt` (or equivalent).
+5. Update the root `README.md` "List of best practices" with a link to the new example.
+6. Run through both checklists above and confirm every item before reporting the task complete.
diff --git a/skills/install.sh b/skills/install.sh
new file mode 100755
index 0000000..de681fa
--- /dev/null
+++ b/skills/install.sh
@@ -0,0 +1,19 @@
+#!/usr/bin/env bash
+# Symlink every skill in this directory into .claude/skills/ so Claude Code
+# discovers them as slash commands (e.g. /add-best-practice).
+#
+# Run from the repo root:  ./skills/install.sh
+
+set -euo pipefail
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+SKILLS_SRC="$REPO_ROOT/skills"
+SKILLS_DST="$REPO_ROOT/.claude/skills"
+
+mkdir -p "$SKILLS_DST"
+
+for skill in "$SKILLS_SRC"/*/; do
+  name="$(basename "$skill")"
+  ln -sfn "../../skills/$name" "$SKILLS_DST/$name"
+  echo "linked: .claude/skills/$name -> skills/$name"
+done