Apache Spark has introduced Spark Declarative Pipelines (SDP), a framework that fundamentally changes how engineers build and manage data transformation workflows. By adopting a declarative programming model, SDP shifts focus from pipeline mechanics to business logic – developers specify what data should look like rather than how to compute it. This framework handles orchestration, parallelization, and fault tolerance automatically across both batch and streaming workloads.

Core Architectural Concepts

At its foundation, SDP operates through three key abstractions:

  • Flows: The fundamental processing units that read from sources, apply transformations, and write to targets. For example:

    @dp.table()
def process_data():
    return spark.read.stream.format("kafka") \
        .option("subscribe", "topic1") \
        .load()

  • Datasets: Queryable objects generated by flows that other pipeline components can consume

  • Pipelines: Collections of flows and datasets defined in Python or SQL files, with automatic dependency resolution

Development Workflow

SDP introduces a CLI tool (spark-pipelines) that simplifies project lifecycle management:

# Initialize project structure
spark-pipelines init --name my_pipeline

# Validate pipeline without execution
spark-pipelines dry-run

# Execute pipeline
spark-pipelines run --spec spark-pipeline.yml

Projects include YAML configuration files (spark-pipeline.yml) and support programmatic pipeline construction. Developers can create materialized views and streaming tables using decorators in Python:

@dp.materialized_view(name="aggregated_sales")
def create_agg():
    return spark.sql("SELECT region, SUM(sales) FROM raw_sales GROUP BY region")

Or equivalently in SQL:

CREATE MATERIALIZED VIEW aggregated_sales AS
SELECT region, SUM(sales) FROM raw_sales GROUP BY region;

Advanced Capabilities

SDP enables sophisticated patterns like:
- Multi-destination writes: Multiple flows appending to the same target
- Dynamic pipeline generation: Python loops creating tables programmatically
- Stream-batch unification: Consistent interfaces for both streaming and batch processing

The framework explicitly prohibits certain Spark operations that conflict with its declarative paradigm – including manual checkpointing, direct RDD manipulations, and imperative control of execution plans.

Industry Implications

By abstracting infrastructure concerns, SDP significantly lowers the barrier for building production-grade Spark pipelines. Data engineers can now iterate faster while maintaining reliability guarantees previously requiring complex Airflow or Databricks configurations. As organizations increasingly adopt Spark for real-time analytics, this framework could accelerate deployment of streaming use cases from fraud detection to IoT analytics.

Early adopters report reduced boilerplate code and easier maintenance, though the approach requires mindset shifts from imperative programming. With its open-source availability, SDP represents Apache Spark's continued evolution toward developer-friendly big data processing.

Source: Apache Spark Declarative Pipelines Documentation