Accelerate Data with Databricks’ Spark Declarative Pipelines in Apache Spark 4.0

Bringing Declarative Pipelines to Apache Spark

When you’re juggling batch jobs, streaming ingests, backfills, retries, and dependency chains, it’s all too easy to end up knee-deep in boilerplate “glue code.” That’s exactly why Databricks has handed over Spark Declarative Pipelines—previously known as Delta Live Tables—to the Apache Spark™ open-source project. Instead of wrestling with orchestration details, you simply declare what your end tables and transformations should look like, and Spark figures out how to execute them, optimizing for performance and resilience all the way.

Why Declarative Pipelines Matter

In traditional ETL you spend hours wiring up incremental loads, checkpoint locations, retry logic, and DAG ordering—work that every data team ends up reinventing. With declarative pipelines you:

Drop the boilerplate: No more manual read(), write(), and dependency loops.
Scale reliably: Automatic checkpointing and retry logic keep streaming jobs humming.
Stay consistent: A shared standard across teams means fewer “works on my cluster” surprises.

Key Features at a Glance

Feature	What It Does	Why It Matters
Declarative APIs	Use `@dlt.table` (Python) or `CREATE PIPELINE` (SQL) to define tables & views	Focus on business logic, not plumbing
Unified Batch & Streaming	One API for both historic backfills and real-time ingestion	Simplifies development and maintenance
Automatic Orchestration	Spark infers execution order, parallelism, and backfill strategy	No external scheduler required
Checkpointing & Retries	Built-in support for saving state and retrying failed stages	Robust pipelines, even under heavy load
Execution Transparency	Inspect the underlying Spark plan for every stage	Tune performance without black-box abstractions
SQL & Python Support	Define pipelines in your language of choice	Low barrier to entry; integrates with existing codebases

How It Works in Practice

Below is a real-world Python example using the current Databricks Lakeflow (Delta Live Tables) API. It mirrors what you’ll see in the upcoming Apache Spark release—just a change of namespace from dlt to the Spark-native module when it lands in open source:

import dlt                                     # import the Databricks declarative module
from pyspark.sql import SparkSession

spark = SparkSession.builder.getOrCreate()

@dlt.table(
    name="users",                             # final table name
    comment="All users loaded from Delta Lake"
)
def users():
    return spark.read.format("delta") \
        .load("s3://path/to/users")           # batch read :contentReference[oaicite:3]{index=3}

@dlt.table(
    name="events",                            # streaming source
    comment="Raw events from Kafka topic"
)
def events():
    return spark.readStream \
        .format("kafka") \
        .option("kafka.bootstrap.servers", "broker:9092") \
        .option("subscribe", "events") \
        .load()                               # streaming read :contentReference[oaicite:4]{index=4}

@dlt.table(
    name="user_event_summary",
    comment="Aggregated event counts per user"
)
def user_event_summary():
    return (
        dlt.read_stream("events")             # pull in streaming table
           .join(dlt.read("users"), "user_id")
           .groupBy("user_id")
           .count()                           # group-by aggregation
    )

Notice there’s no explicit .write() or manual DAG management—Spark’s engine handles that for you.

Who Stands to Gain

Data Engineers reclaim dozens of weekly hours by cutting out “glue code.”
Analytics Teams get consistent pipelines that simplify testing, lineage, and CI/CD.
Platform Operators standardize deployment, monitoring, and governance across every workload.

Getting Started

Upgrade to Apache Spark 4.0+ (Declarative Pipelines is included in this release).
Explore the Lakeflow/Delta Live Tables docs to try out Python and SQL examples.
Contribute: Follow the open JIRA proposal and community discussion to shape the API’s future.

By moving from imperative scripts to a declarative model, Spark Declarative Pipelines empowers you to declare intents, not steps—so you can ship reliable data products faster, and spend your time on insights instead of infrastructure. Give it a spin today, and see how much cleaner your ETL can become.

Tecyfy Takeaway