Tool.data Engineering.55c8f35d7b3cac72

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Data Engineering Practice

Domain-specific patterns and checklists for data engineering work.

Scope

• Data pipelines: batch and streaming
• ETL/ELT processes and ingestion patterns
• Orchestration: Airflow, Dagster, Prefect
• Data quality and observability
• Data lakes, lakehouses, and warehouses
• Streaming: Kafka, Kinesis, Flink, Spark Streaming
• CDC (Change Data Capture) patterns
• Data contracts and schema registry
• Data governance, lineage, and cataloging
• Data mesh architecture

Code Review Checklist

Pipeline Design

• Idempotent operations (safe to re-run without duplicates)
• Atomic writes (all-or-nothing, no partial state)
• Backfill strategy defined
• Failure handling and retry logic
• Appropriate task granularity (not monolithic)
• Dependencies explicitly declared
• No hardcoded dates or file paths
• SLA defined for production pipelines

Data Quality

• Schema validation on ingestion (not just at transform time)
• Null handling explicit (expected vs unexpected nulls)
• Data type coercion documented
• Uniqueness constraints where needed
• Freshness checks (data not stale beyond SLA)
• Volume anomaly detection (row count within expected range)
• Data quality tests defined (not just schema tests)

Orchestration

• DAG structure is clear and documented
• Task dependencies are correct and minimal
• Timeouts configured at task and DAG level
• Alerts on failure routed to right channel
• SLAs defined and monitored for critical pipelines
• Concurrency limits set to prevent resource contention
• Catchup behavior explicitly configured

Performance

• Partitioning strategy appropriate for access pattern
• Incremental processing where possible (no full refreshes on large tables)
• No full table scans on large fact tables
• Appropriate file formats (Parquet/Iceberg/Delta, not CSV)
• Compression enabled
• Resource allocation sized appropriately (not default)

Architecture Patterns

Pipeline Structure

pipelines/
  ingestion/
    source_name/
      extract.py        # Pull from source system
      validate.py       # Schema and quality checks at landing
      load.py           # Write to bronze/raw zone
  transformation/
    domain/
      staging.sql       # Clean, dedupe (silver)
      intermediate.sql  # Business logic joins
      marts.sql         # Aggregated final models (gold)
  orchestration/
    dags/
      source_name_dag.py
  quality/
    checks/
      source_name_checks.py

Idempotency Pattern

# Good: Overwrite partition on re-run — idempotent
def load_partition(date: str):
    df = extract(date)
    (df.write
       .mode("overwrite")
       .partitionBy("date")
       .format("parquet")
       .save(path))

# Bad: Append creates duplicates on re-run
def load_partition(date: str):
    df = extract(date)
    df.write.mode("append").parquet(path)  # ❌

Incremental / Watermark Pattern

def load_incremental(source_table: str):
    watermark = get_watermark(source_table)
    new_records = source.filter(col("updated_at") > watermark)
    if new_records.isEmpty():
        return  # Nothing to do
    write_data(new_records)
    set_watermark(source_table, current_timestamp())
    log_metrics(source_table, new_records.count())

Data Lakehouse and Table Formats

Apache Iceberg

# Preferred for new lakehouses — ACID, schema evolution, time travel
spark.sql("""
    CREATE TABLE catalog.db.orders (
        order_id BIGINT NOT NULL,
        customer_id BIGINT,
        amount DECIMAL(10,2),
        created_at TIMESTAMP
    )
    USING iceberg
    PARTITIONED BY (days(created_at))
    TBLPROPERTIES (
        'write.delete.mode' = 'merge-on-read',
        'write.update.mode' = 'merge-on-read',
        'history.expire.max-snapshot-age-ms' = '604800000'  -- 7 days
    )
""")

# Incremental merge (ACID upsert)
spark.sql("""
    MERGE INTO catalog.db.orders t
    USING staging_orders s ON t.order_id = s.order_id
    WHEN MATCHED THEN UPDATE SET *
    WHEN NOT MATCHED THEN INSERT *
""")

Table Format Decision

Medallion Architecture

Bronze (raw)  → Silver (cleaned/conformed) → Gold (business-ready)

• Bronze: Exact copy, no transformations. All fields including errors. Partition by ingest_date. Never delete rows.
• Silver: Deduplicated, type-cast, renamed. One row per entity or event. Conforms to data contract.
• Gold: Aggregated, dimensional, business-ready. Equivalent to dbt mart layer.

CDC (Change Data Capture)

Debezium + Kafka Pattern

Source DB (Postgres/MySQL) → Debezium → Kafka → Kafka Sink Connector → Warehouse/Lake

// Debezium connector config (Kafka Connect)
{
  "name": "postgres-source-connector",
  "config": {
    "connector.class": "io.debezium.connector.postgresql.PostgresConnector",
    "database.hostname": "postgres",
    "database.port": "5432",
    "database.user": "debezium",
    "database.password": "${secret:db-password}",
    "database.dbname": "production",
    "table.include.list": "public.orders,public.customers",
    "publication.name": "dbz_publication",
    "slot.name": "dbz_slot",
    "transforms": "unwrap",
    "transforms.unwrap.type": "io.debezium.transforms.ExtractNewRecordState",
    "transforms.unwrap.add.fields": "op,ts_ms"
  }
}

CDC Checklist:

• Replication slot lag monitored (Postgres slots cause disk bloat if consumer falls behind)
• Schema registry integrated (schema evolution without breaking consumers)
• Dead letter queue for unprocessable messages
• Exactly-once or at-least-once semantics explicitly chosen (and compensated if at-least-once)
• Initial snapshot strategy defined (full table snapshot or skip)

Data Contracts

A data contract is the schema + SLA agreement between a producer and consumer.

# contracts/orders_v2.yaml
contract:
  name: orders
  version: "2.0.0"
  owner: data-platform-team
  consumers: [analytics, ml-team]

  schema:
    - name: order_id
      type: BIGINT
      nullable: false
      description: Unique order identifier
    - name: status
      type: VARCHAR
      nullable: false
      allowed_values: ["pending", "completed", "cancelled", "refunded"]

  sla:
    freshness_minutes: 30
    availability: "99.5%"

  quality:
    - check: row_count_min
      value: 1000
      window: daily
    - check: not_null
      columns: [order_id, status, created_at]

Schema Registry (Confluent / AWS Glue)

• Register schemas before any producer sends data
• Consumers rely on registry for deserialization — not hardcoded schemas
• Use BACKWARD compatibility by default (new schema can read old data)
• Breaking changes require major version bump + consumer migration plan

Streaming Patterns

Kafka Topic Design

# Naming: {environment}.{domain}.{entity}.{version}
production.commerce.orders.v2
staging.commerce.orders.v2

# Partition strategy:
# - Partition by entity key (customer_id, order_id) for ordered processing
# - More partitions = more parallelism, but more overhead
# - Rule: start with max expected consumers, add headroom

Flink Windowing

// Tumbling windows — non-overlapping, fixed size
DataStream<OrderMetrics> windowed = orders
    .keyBy(order -> order.getRegion())
    .window(TumblingEventTimeWindows.of(Time.minutes(5)))
    .aggregate(new OrderAggregator());

// Always: set watermarks for event time processing
DataStream<Order> withWatermarks = orders
    .assignTimestampsAndWatermarks(
        WatermarkStrategy.<Order>forBoundedOutOfOrderness(Duration.ofSeconds(30))
            .withTimestampAssigner((event, ts) -> event.getTimestamp())
    );

Late Data Handling

# Define what "late" means upfront — don't discover it in production
WATERMARK_DELAY_SECONDS = 30  # Accept data up to 30s late
ALLOWED_LATENESS_SECONDS = 300  # Process up to 5min late (side output)

# Late data should go to: side output → correction pipeline → restatement

Data Quality Framework

Validation Layers

1. Schema validation — column names, types, nullability (fail fast at landing)
2. Semantic validation — value ranges, formats, referential integrity
3. Statistical validation — volume, distribution, anomalies vs baseline

Great Expectations Pattern

# Define expectations as code — version-controlled quality contract
context = gx.get_context()
suite = context.add_expectation_suite("orders.bronze")

suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id")
)
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeBetween(column="amount", min_value=0, max_value=1_000_000)
)
suite.add_expectation(
    gx.expectations.ExpectTableRowCountToBeBetween(min_value=1_000, max_value=10_000_000)
)

# Run as a checkpoint in your pipeline — block if quality fails
checkpoint.run(batch_request=batch, run_name=f"orders_{date}")

Orchestration Best Practices

Dagster (Preferred for new projects)

@asset(
    partitions_def=DailyPartitionsDefinition(start_date="2024-01-01"),
    metadata={"owner": "data-platform"},
)
def orders_bronze(context: AssetExecutionContext) -> None:
    partition_date = context.partition_key
    df = extract_orders(partition_date)
    validate_schema(df)
    df.write_parquet(f"s3://data-lake/bronze/orders/date={partition_date}/")
    context.add_output_metadata({"row_count": len(df)})

@asset(deps=[orders_bronze])
def orders_silver(context: AssetExecutionContext) -> None:
    # Automatic lineage from asset dependency graph
    ...

Airflow (Existing deployments)

• Use TaskFlow API (Airflow 2.x) — cleaner than classic operators
• catchup=False unless backfill is needed
• max_active_runs=1 for sequential pipelines
• Use pools for shared resource limits (DB connections, API rate limits)
• XCom for metadata only (IDs, counts) — not DataFrames

Common Anti-Patterns

Technology-Specific Notes

Spark

• Avoid collect() on large datasets (driver memory)
• Use broadcast joins for tables < 100MB
• Partition by high-cardinality filter columns, not low-cardinality
• Cache (persist()) only if reused more than twice in the same job
• Tune shuffle partitions: spark.sql.shuffle.partitions defaults to 200 — too high for small data, too low for large

Kafka

• Monitor consumer lag as primary health metric
• Consumer group naming: {service}-{purpose} (e.g., analytics-etl)
• Dead letter queue for every consumer — unprocessable messages must not block progress
• Retention: event sourcing topics = infinite; operational topics = 7 days

Snowflake (Data Engineering)

• Use COPY INTO for bulk loads — not INSERT SELECT for large volumes
• Snowpipe for continuous micro-batch ingestion
• Dynamic tables for incremental transformations without Airflow/dbt
• Monitor credits by warehouse; alert on unexpected spend