data-pipelines

---
name: data-pipelines
description: >
license: MIT
---

When this skill is activated, always start your first response with the 🧢 emoji.

# Data Pipelines

A senior data engineer's decision-making framework for building production data
pipelines. This skill covers the five pillars of data engineering - ingestion
patterns (ETL vs ELT), orchestration (Airflow), transformation (dbt), large-scale
processing (Spark), and architecture choices (streaming vs batch) - with emphasis
on when to use each pattern and the trade-offs involved. Designed for engineers
who need opinionated guidance on building reliable, observable, and maintainable
data infrastructure.

---

## When to use this skill

Trigger this skill when the user:
- Designs an ETL or ELT pipeline from scratch
- Writes or debugs an Airflow DAG
- Creates dbt models, tests, or macros
- Optimizes a Spark job (shuffles, partitioning, memory tuning)
- Decides between streaming and batch processing
- Implements incremental loads or change data capture (CDC)
- Plans a data warehouse or lakehouse architecture
- Needs data quality checks, schema evolution, or pipeline monitoring

Do NOT trigger this skill for:
- BI/analytics dashboard design or visualization (use an analytics skill)
- ML model training or feature engineering (use an ML/data-science skill)

---

## Key principles

1. **Idempotency is non-negotiable** - Every pipeline run with the same input must
   produce the same output. Design for safe re-runs from day one. Use date
   partitions, merge keys, or upsert logic so that retries never corrupt data.

2. **Prefer ELT over ETL in modern stacks** - Load raw data first, transform in
   the warehouse. This preserves the source of truth, enables schema-on-read, and
   lets analysts iterate on transformations without re-ingesting. ETL still wins
   when you need to filter sensitive data before it lands.

3. **Partition and increment, never full-reload** - Full table scans on every run
   do not scale. Use incremental models (dbt), date-partitioned loads, and
   watermarks to process only what changed. Fall back to full reload only for small
   reference tables or disaster recovery.

4. **Orchestrate, don't script** - A cron job calling a Python script is not a
   pipeline. Use a proper orchestrator (Airflow, Dagster, Prefect) for retries,
   dependency management, backfills, and observability. The orchestrator should
   own scheduling and state, not your application code.

5. **Test data like code** - Schema tests, row count checks, uniqueness
   constraints, and freshness SLAs are not optional. dbt tests, Great Expectations,
   or custom assertions should gate every pipeline stage. Bad data downstream is
   more expensive than a failed pipeline.

---

## Core concepts

Data pipelines move data from sources (databases, APIs, event streams) through
transformations to destinations (warehouses, lakes, serving layers). The two
dominant patterns are **ETL** (extract-transform-load) and **ELT**
(extract-load-transform). ETL transforms data in-flight before loading; ELT
loads raw data first and transforms inside the destination.

The pipeline lifecycle has four stages: **ingestion** (getting data in),
**orchestration** (scheduling and dependency management), **transformation**
(cleaning, joining, aggregating), and **serving** (making data available to
consumers). Each stage has specialized tools: Fivetran/Airbyte for ingestion,
Airflow/Dagster for orchestration, dbt for transformation, and the warehouse
itself (BigQuery, Snowflake, Redshift) for serving.

**Streaming vs batch** is an architecture decision, not a tool choice. Batch
processes data in time-windowed chunks (hourly, daily). Streaming processes
events continuously as they arrive. Most organizations need both - batch for
historical aggregations and streaming for real-time dashboards or alerting.
The Lambda architecture runs both in parallel; the Kappa architecture uses a
single streaming layer for everything.

---

## Common tasks

### Design an ETL/ELT pipeline

Decide the pattern based on your constraints:

```
Need to filter PII before landing?       -> ETL (transform before load)
Want analysts to iterate on transforms?   -> ELT (load raw, transform in warehouse)
Source data volume > 1TB per load?        -> ELT with Spark for heavy transforms
Small reference data < 100MB?             -> Direct load, skip the framework
```

**Standard ELT flow:**
1. Extract from source (API, database CDC, file drop)
2. Load raw data to staging layer (preserve original schema)
3. Transform in warehouse using dbt (staging -> intermediate -> mart)
4. Test data quality at each layer boundary
5. Serve from mart layer to downstream consumers

> Always land raw data in an immutable staging layer. Transformations should
> read from staging, never modify it. This gives you a re-playable source of truth.

### Write an Airflow DAG

A well-structured DAG separates orchestration from business logic:

```python
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.google.cloud.operators.bigquery import BigQueryInsertJobOperator
from datetime import datetime, timedelta

default_args = {
    "owner": "data-team",
    "retries": 2,
    "retry_delay": timedelta(minutes=5),
    "execution_timeout": timedelta(hours=2),
}

with DAG(
    dag_id="daily_orders_pipeline",
    schedule="0 6 * * *",
    start_date=datetime(2024, 1, 1),
    catchup=False,
    default_args=default_args,
    tags=["production", "orders"],
) as dag:

    extract = PythonOperator(
        task_id="extract_orders",
        python_callable=extract_orders_fn,
        op_kwargs={"ds": "{{ ds }}"},
    )

    transform = BigQueryInsertJobOperator(
        task_id="transform_orders",
        configuration={"query": {"query": "{% include 'sql/transform_orders.sql' %}"}},
    )

    test = PythonOperator(
        task_id="test_row_counts",
        python_callable=assert_row_counts,
    )

    extract >> transform >> test
```

> Use `catchup=False` for most production DAGs unless you explicitly need
> backfill behavior. Set `execution_timeout` to prevent zombie tasks.

### Build dbt models

Structure dbt projects in three layers:

```
models/
  staging/          -- 1:1 with source tables, light renaming/casting
    stg_orders.sql
    stg_customers.sql
  intermediate/     -- business logic joins, deduplication
    int_orders_enriched.sql
  marts/            -- final consumer-facing tables
    fct_daily_revenue.sql
    dim_customers.sql
```

Example incremental model:

```sql
-- models/staging/stg_orders.sql
{{
  config(
    materialized='incremental',
    unique_key='order_id',
    on_schema_change='append_new_columns'
  )
}}

select
    order_id,
    customer_id,
    order_total,
    cast(created_at as timestamp) as ordered_at
from {{ source('raw', 'orders') }}

{% if is_incremental() %}
where created_at > (select max(ordered_at) from {{ this }})
{% endif %}
```

> Always define `unique_key` for incremental models. Without it, dbt appends
> instead of merging, causing duplicates on re-runs.

### Optimize a Spark job

The three most common Spark performance killers and their fixes:

| Problem | Symptom | Fix |
|---|---|---|
| Data skew | One task takes 10x longer than others | Salt the join key, or use `broadcast()` for small tables |
| Too many shuffles | High shuffle read/write in Spark UI | Repartition before joins, coalesce after filters |
| Small files | Thousands of tiny output files | Use `repartition(N)` or `coalesce(N)` before write |

```python
from pyspark.sql import SparkSession
from pyspark.sql.functions import broadcast

spark = SparkSession.builder.appName("optimize_example").getOrCreate()

# Broadcast small dimension table to avoid shuffle
orders = spark.read.parquet("s3://data/orders/")
products = spark.read.parquet("s3://data/products/")  # < 100MB

enriched = orders.join(broadcast(products), "product_id", "left")

# Repartition by date before writing to avoid small files
enriched.repartition("order_date").write \
    .partitionBy("order_date") \
    .mode("overwrite") \
    .parquet("s3://data/enriched_orders/")
```

> Check `spark.sql.shuffle.partitions` (default 200). For small datasets,
> lower it. For large datasets with skew, raise it.

### Choose streaming vs batch

```
Latency requirement < 1 minute?        -> Streaming (Kafka + Flink/Spark Streaming)
Latency requirement 1 hour - 1 day?    -> Batch (Airflow + dbt/Spark)
Need both real-time AND historical?     -> Lambda (batch + streaming in parallel)
Want one codebase for both?             -> Kappa (streaming-only, replay from log)
```

**Streaming is NOT always better.** It adds complexity in exactly-once semantics,
state management, late-arriving data, and debugging. Use batch unless you have
a proven real-time requirement.

**Common streaming stack:** Kafka (ingestion) -> Flink or Spark Structured
Streaming (processing) -> warehouse or serving store (output).

### Implement data quality checks

Gate every pipeline stage with assertions:

```yaml
# dbt schema.yml
models:
  - name: fct_daily_revenue
    columns:
      - name: revenue_date
        tests:
          - not_null
          - unique
      - name: total_revenue
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0
              max_value: 10000000
    tests:
      - dbt_utils.recency:
          datepart: day
          field: revenue_date
          interval: 2
```

> Set freshness SLAs on source tables. If source data is stale, fail the
> pipeline early rather than producing silently wrong results.

---

## Anti-patterns / common mistakes

| Mistake | Why it's wrong | What to do instead |
|---|---|---|
| Full table reload every run | Doesn't scale, wastes compute, risks data loss during failures | Incremental loads with watermarks or CDC |
| Business logic in Airflow operators | Makes testing impossible, couples logic to orchestration | Keep Airflow thin - call dbt/Spark/scripts, don't embed SQL |
| No staging layer (transform in place) | Destroys source of truth, no replay capability | Land raw data in immutable staging, transform into separate layers |
| Ignoring data skew in Spark | One partition processes 90% of data, job takes hours | Salt keys, broadcast small tables, analyze data distribution first |
| Skipping schema tests | Bad data silently propagates, discovered by end users | dbt tests, Great Expectations, or custom assertions at every boundary |
| Over-engineering with streaming | Adds complexity without real-time need | Start with batch, add streaming only for proven sub-minute requirements |
| Hardcoded dates in queries | Breaks idempotency, prevents backfills | Use Airflow template variables (`{{ ds }}`) or dbt `ref()` / `source()` |
| No alerting on pipeline failures | Silent failures lead to stale dashboards | Alert on DAG failures, SLA misses, and data freshness breaches |

---

## Gotchas

1. **dbt incremental model without `unique_key` causes duplicates** - An incremental model without `unique_key` set in the config appends new records on every run instead of merging. A re-run after a failure produces duplicate rows that are extremely hard to detect and clean up downstream. Always define `unique_key` for incremental models.

2. **Airflow `catchup=True` triggering thousands of backfill runs** - If you set `catchup=True` (the default) on a DAG with a `start_date` months in the past, Airflow immediately schedules one run per interval from that start date until now. This can flood your workers. Set `catchup=False` for production DAGs and trigger backfills explicitly via the CLI.

3. **Hardcoded dates break idempotency** - SQL queries with `WHERE created_at >= '2024-01-01'` cannot be safely re-run for different time windows. Use Airflow template variables (`{{ ds }}`) or dbt source freshness definitions so that re-runs and backfills process the correct partition automatically.

4. **Data skew makes one Spark task run 10x longer** - A join key where 80% of rows share one value (e.g., `customer_id = NULL` or a dominant category) causes one partition to process nearly the entire dataset while others finish immediately. Profile key cardinality with `df.groupBy("key").count().orderBy(desc("count")).show(20)` before writing join logic.

5. **Streaming over-engineering for batch-compatible requirements** - Kafka + Flink adds exactly-once semantics complexity, late-data handling, state backend management, and operational overhead. If the business requirement is "data available within 15 minutes," a scheduled Airflow DAG running every 10 minutes satisfies it with a fraction of the complexity. Start with batch; add streaming only for proven sub-minute latency needs.

---

## References

For detailed patterns and implementation guidance on specific domains, read the
relevant file from the `references/` folder:

- `references/airflow-patterns.md` - DAG design patterns, sensors, dynamic DAGs, backfill strategies
- `references/dbt-patterns.md` - model layering, macros, packages, CI/CD for dbt
- `references/spark-tuning.md` - memory config, shuffle optimization, partitioning, caching
- `references/streaming-architecture.md` - Kafka, Flink, exactly-once, late data, windowing

Only load a references file if the current task requires it - they are long and
will consume context.

---

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