Production Pipelines - Databricks Data Engineer Associate¶

Overview¶

This section covers Delta Live Tables (DLT) and Databricks Jobs for production pipeline orchestration, representing 13% of the exam. You need to understand DLT syntax, expectations (data quality), pipeline modes, and job scheduling.

📖 Delta Live Tables - DLT overview 📖 Databricks Jobs - Job orchestration

Key Topics¶

1. Delta Live Tables (DLT) Fundamentals¶

DLT provides a declarative framework for building reliable data pipelines. Instead of writing imperative code that specifies how to process data, you declare what transformations to apply and DLT manages the execution.

📖 DLT Concepts - Core DLT concepts 📖 DLT Python - Python API reference 📖 DLT SQL - SQL API reference

Python DLT Syntax:

import dlt

# Materialized table (persisted to storage)
@dlt.table(comment="Cleaned customer data")
def silver_customers():
    return spark.read.table("bronze_customers").filter(col("id").isNotNull())

# Streaming table
@dlt.table
def bronze_events():
    return spark.readStream.format("cloudFiles").option("cloudFiles.format", "json").load("/data/events")

# View (not persisted, intermediate computation)
@dlt.view
def valid_orders():
    return spark.read.table("bronze_orders").filter(col("amount") > 0)

SQL DLT Syntax:

-- Materialized view
CREATE OR REFRESH MATERIALIZED VIEW silver_customers AS
SELECT * FROM bronze_customers WHERE id IS NOT NULL;

-- Streaming table
CREATE OR REFRESH STREAMING TABLE bronze_events AS
SELECT * FROM STREAM(LIVE.raw_events);

-- Live table reference
SELECT * FROM LIVE.source_table;

Key Concepts: - @dlt.table creates a materialized (persisted) table - @dlt.view creates a temporary view (not persisted, used for intermediate steps) - Streaming tables process data incrementally using Structured Streaming - The LIVE keyword references other tables in the same pipeline - DLT handles dependency resolution automatically based on table references

2. DLT Expectations (Data Quality)¶

Expectations are declarative data quality constraints that enforce rules on your pipeline data.

📖 DLT Expectations - Data quality constraints

Three Expectation Behaviors:

@dlt.table
@dlt.expect("valid_id", "id IS NOT NULL")
@dlt.expect_or_drop("positive_amount", "amount > 0")
@dlt.expect_or_fail("known_country", "country IN ('US', 'UK', 'CA')")
def silver_orders():
    return spark.read.table("bronze_orders")

-- SQL expectations
CREATE OR REFRESH MATERIALIZED VIEW silver_orders (
  CONSTRAINT valid_id EXPECT (id IS NOT NULL),
  CONSTRAINT positive_amount EXPECT (amount > 0) ON VIOLATION DROP ROW,
  CONSTRAINT known_country EXPECT (country IN ('US', 'UK', 'CA')) ON VIOLATION FAIL UPDATE
) AS SELECT * FROM LIVE.bronze_orders;

Key Concepts: - Multiple expectations can be applied to a single table - Expectation results are tracked in the DLT event log - expect logs violations as warnings but keeps all records (default) - expect_or_drop silently removes records that violate the constraint - expect_or_fail stops the pipeline to prevent bad data from propagating

3. DLT Pipeline Modes¶

📖 Pipeline Modes - Triggered vs continuous

Key Concepts: - Triggered mode is used with scheduled jobs for batch processing - Continuous mode keeps the pipeline running for real-time data - Development mode allows faster iteration with relaxed error handling - Production mode enforces strict error handling and retries - Pipeline updates can be full refresh (reprocess everything) or incremental

4. DLT Event Log¶

The event log captures pipeline execution details and data quality metrics.

Key Concepts: - Event log records pipeline start, progress, and completion events - Data quality metrics show how many records pass or fail each expectation - Accessible through the DLT pipeline UI or by querying the event log table - Useful for monitoring pipeline health and data quality trends

5. Databricks Jobs¶

Databricks Jobs allow you to schedule and orchestrate tasks including notebooks, Python scripts, SQL queries, and DLT pipelines.

📖 Create and Run Jobs - Job management 📖 Multi-task Jobs - Task dependencies

Task Types: | Task Type | Description | |-----------|-------------| | Notebook | Run a Databricks notebook | | Python script | Run a Python file | | SQL | Execute SQL statements | | DLT pipeline | Trigger a DLT pipeline update | | dbt | Run dbt transformations | | JAR | Run a Java/Scala application |

Key Concepts: - Jobs can contain multiple tasks with DAG-style dependencies - Tasks can share values using dbutils.jobs.taskValues - Each task can use its own cluster or share a job cluster - Job clusters are more cost-effective than all-purpose clusters for jobs

6. Job Scheduling and Monitoring¶

📖 Job Scheduling - Schedule configuration 📖 Job Monitoring - Monitoring runs

Key Concepts: - Cron-based scheduling with timezone support - Manual triggers for ad-hoc runs - Notifications via email, webhook, or Slack on job events (start, success, failure) - Retry policies: configure number of retries on task failure - Timeout settings prevent runaway jobs - Run history shows status, duration, and logs for each run - Concurrent run settings control whether multiple instances can run simultaneously

7. Job Parameters and Task Values¶

# Set a task value
dbutils.jobs.taskValues.set(key="row_count", value=1000)

# Get a task value from an upstream task
count = dbutils.jobs.taskValues.get(taskKey="upstream_task", key="row_count")

Key Concepts: - Task values enable data passing between tasks in a multi-task job - Parameters can be set at the job level and passed to all tasks - Dynamic values enable conditional execution in downstream tasks

Exam Tips for This Domain¶

1. DLT expectations - Know all three behaviors (warn, drop, fail) and their SQL equivalents
2. Pipeline modes - Triggered for scheduled batch, continuous for real-time
3. DLT table vs view - Tables are persisted, views are intermediate computations
4. Job clusters vs all-purpose - Job clusters are recommended for production jobs
5. Task dependencies - Understand DAG-style orchestration in multi-task jobs
6. Streaming tables - Know the difference between streaming and materialized tables in DLT

Documentation Links Summary¶