databricks-core-workflow-a

What this skill does

# Databricks Core Workflow A: Delta Lake ETL

## Overview

Build production Delta Lake ETL pipelines using the medallion architecture (Bronze > Silver > Gold). Uses Auto Loader (`cloudFiles`) for incremental ingestion, `MERGE INTO` for upserts, and Delta Live Tables for declarative pipelines.

## Prerequisites

- Completed `databricks-install-auth` setup
- Unity Catalog enabled with catalogs/schemas created
- Access to cloud storage for raw data (S3, ADLS, GCS)

## Architecture

```
Raw Sources (S3/ADLS/GCS)
    │  Auto Loader (cloudFiles)
    ▼
Bronze (raw + metadata)
    │  Cleanse, deduplicate, type-cast
    ▼
Silver (conformed)
    │  Aggregate, join, feature engineer
    ▼
Gold (analytics-ready)
```

## Instructions

### Step 1: Bronze Layer — Raw Ingestion with Auto Loader

Auto Loader (`cloudFiles` format) incrementally processes new files as they arrive. It handles schema inference, evolution, and scales to millions of files.

```python
from pyspark.sql import SparkSession
from pyspark.sql.functions import current_timestamp, input_file_name, lit

spark = SparkSession.builder.getOrCreate()

# Streaming ingestion with Auto Loader
bronze_stream = (
    spark.readStream
    .format("cloudFiles")
    .option("cloudFiles.format", "json")
    .option("cloudFiles.schemaLocation", "/checkpoints/bronze/orders/schema")
    .option("cloudFiles.inferColumnTypes", "true")
    .option("cloudFiles.schemaEvolutionMode", "addNewColumns")
    .load("s3://data-lake/raw/orders/")
)

# Add ingestion metadata
bronze_with_meta = (
    bronze_stream
    .withColumn("_ingested_at", current_timestamp())
    .withColumn("_source_file", input_file_name())
    .withColumn("_source_system", lit("orders-api"))
)

# Write to bronze Delta table
(bronze_with_meta.writeStream
    .format("delta")
    .outputMode("append")
    .option("checkpointLocation", "/checkpoints/bronze/orders/data")
    .option("mergeSchema", "true")
    .toTable("prod_catalog.bronze.raw_orders"))
```

### Step 2: Silver Layer — Cleansing and Deduplication

Read from Bronze, apply business logic, and MERGE INTO Silver with upsert semantics.

```python
from pyspark.sql.functions import col, trim, lower, to_timestamp, sha2, concat_ws
from delta.tables import DeltaTable

# Read new records from bronze (batch mode for scheduled jobs)
bronze_df = spark.table("prod_catalog.bronze.raw_orders")

# Apply transformations
silver_df = (
    bronze_df
    .withColumn("order_id", col("order_id").cast("string"))
    .withColumn("customer_email", lower(trim(col("customer_email"))))
    .withColumn("order_date", to_timestamp(col("order_date"), "yyyy-MM-dd'T'HH:mm:ss"))
    .withColumn("amount", col("amount").cast("decimal(12,2)"))
    .withColumn("email_hash", sha2(col("customer_email"), 256))
    .filter(col("order_id").isNotNull())
    .dropDuplicates(["order_id"])
)

# Upsert into silver with MERGE
if spark.catalog.tableExists("prod_catalog.silver.orders"):
    target = DeltaTable.forName(spark, "prod_catalog.silver.orders")
    (target.alias("t")
        .merge(silver_df.alias("s"), "t.order_id = s.order_id")
        .whenMatchedUpdateAll()
        .whenNotMatchedInsertAll()
        .execute())
else:
    silver_df.write.format("delta").saveAsTable("prod_catalog.silver.orders")
```

### Step 3: Gold Layer — Business Aggregations

Aggregate Silver data into analytics-ready tables. Use partition-level overwrites for efficient updates.

```python
from pyspark.sql.functions import sum as _sum, count, avg, date_trunc

# Daily order metrics
gold_metrics = (
    spark.table("prod_catalog.silver.orders")
    .withColumn("order_day", date_trunc("day", col("order_date")))
    .groupBy("order_day", "region")
    .agg(
        count("order_id").alias("total_orders"),
        _sum("amount").alias("total_revenue"),
        avg("amount").alias("avg_order_value"),
    )
)

# Overwrite only changed partitions
(gold_metrics.write
    .format("delta")
    .mode("overwrite")
    .option("replaceWhere", f"order_day >= '{target_date}'")
    .saveAsTable("prod_catalog.gold.daily_order_metrics"))
```

### Step 4: Delta Table Maintenance

```sql
-- Compact small files (bin-packing)
OPTIMIZE prod_catalog.silver.orders;

-- Z-order for query performance on frequently filtered columns
OPTIMIZE prod_catalog.silver.orders ZORDER BY (order_date, region);

-- Or use Liquid Clustering (DBR 13.3+) — replaces partitioning + Z-order
ALTER TABLE prod_catalog.silver.orders CLUSTER BY (order_date, region);
OPTIMIZE prod_catalog.silver.orders;

-- Clean up old file versions (default: 7 days)
VACUUM prod_catalog.silver.orders RETAIN 168 HOURS;

-- Compute statistics for query optimizer
ANALYZE TABLE prod_catalog.silver.orders COMPUTE STATISTICS;
```

### Step 5: Delta Live Tables (Declarative Pipeline)

DLT manages orchestration, data quality, lineage, and error handling automatically.

```python
import dlt
from pyspark.sql.functions import col, current_timestamp

@dlt.table(
    comment="Raw orders from Auto Loader",
    table_properties={"quality": "bronze"},
)
def bronze_orders():
    return (
        spark.readStream.format("cloudFiles")
        .option("cloudFiles.format", "json")
        .option("cloudFiles.inferColumnTypes", "true")
        .load("s3://data-lake/raw/orders/")
        .withColumn("_ingested_at", current_timestamp())
    )

@dlt.table(comment="Cleansed orders")
@dlt.expect_or_drop("valid_order_id", "order_id IS NOT NULL")
@dlt.expect_or_drop("valid_amount", "amount > 0")
def silver_orders():
    return (
        dlt.read_stream("bronze_orders")
        .withColumn("amount", col("amount").cast("decimal(12,2)"))
        .dropDuplicates(["order_id"])
    )

@dlt.table(comment="Daily revenue metrics")
def gold_daily_revenue():
    return (
        dlt.read("silver_orders")
        .groupBy("region", "order_date")
        .agg({"amount": "sum", "order_id": "count"})
    )
```

### Step 6: Schedule the Pipeline

```python
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.jobs import (
    CreateJob, Task, NotebookTask, JobCluster, CronSchedule,
)
from databricks.sdk.service.compute import ClusterSpec, AutoScale

w = WorkspaceClient()

job = w.jobs.create(
    name="daily-orders-etl",
    tasks=[
        Task(task_key="bronze", job_cluster_key="etl",
             notebook_task=NotebookTask(notebook_path="/Repos/team/pipelines/bronze")),
        Task(task_key="silver", job_cluster_key="etl",
             notebook_task=NotebookTask(notebook_path="/Repos/team/pipelines/silver"),
             depends_on=[{"task_key": "bronze"}]),
        Task(task_key="gold", job_cluster_key="etl",
             notebook_task=NotebookTask(notebook_path="/Repos/team/pipelines/gold"),
             depends_on=[{"task_key": "silver"}]),
    ],
    job_clusters=[JobCluster(
        job_cluster_key="etl",
        new_cluster=ClusterSpec(
            spark_version="14.3.x-scala2.12",
            node_type_id="i3.xlarge",
            autoscale=AutoScale(min_workers=1, max_workers=4),
        ),
    )],
    schedule=CronSchedule(quartz_cron_expression="0 0 6 * * ?", timezone_id="UTC"),
    max_concurrent_runs=1,
)
print(f"Created job: {job.job_id}")
```

## Output

- Bronze layer with raw data, Auto Loader schema evolution, and ingestion metadata
- Silver layer with cleansed, deduplicated, type-cast data via MERGE upserts
- Gold layer with business-ready aggregations
- Table maintenance schedule (OPTIMIZE, VACUUM, ANALYZE)
- Optional DLT pipeline with built-in data quality expectations

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| `AnalysisException: mergeSchema` | Source schema changed | Auto Loader handles this; for batch add `.option("mergeSchema", "true")` |
| `ConcurrentAppendException` | Multiple jobs writing same table | Use MERGE with retry logic or serialize writes via `max_concurrent_runs=1` |
| `Null primary key` | Bad source data | Add `@dlt.expect_or_drop` or `.filter(col("pk").isNotNull())` |
| `java.lan