Delta Live Tables: Declarative vs. Procedural Approaches in Databricks

When working with Delta Live Tables (DLT) in Databricks, you have the flexibility to define your data pipelines using either a declarative or procedural approach. Understanding the differences between these methods is crucial for optimizing your data engineering processes.

### 1. Declarative Approach with Delta Live Tables (DLT)

The declarative approach utilizes DLT’s SQL or Python DSL to specify what the output should look like, allowing the DLT engine to manage execution, optimization, and dependencies automatically.

Example: Declarative Approach (DLT SQL DSL)

```sql

CREATE LIVE TABLE transformed_data AS

SELECT

id,

name,

UPPER(name) AS uppercase_name,

date_of_birth

FROM LIVE.raw_data;

```

- This example creates a live table that converts the name field to uppercase from the raw_data table. The DLT framework handles scheduling and maintaining the output state.

Example: Declarative Approach (DLT Python DSL)

```python

from pyspark.sql.functions import upper

import dlt

@dlt.table

def transformed_data():

df = dlt.read("raw_data")

return df.select("id", "name", upper("name").alias("uppercase_name"), "date_of_birth")

```

- Here, the decorator @dlt.table signifies that this function defines a Delta Live Table. DLT automatically manages dependencies and updates output based on changes in raw_data.

### When to Use Declarative:

- Simple transformations (e.g., filtering, renaming).

- Reduced boilerplate code with optimizations managed by Databricks.

- Real-time streaming scenarios where data outcomes are prioritized over processing steps.

### 2. Procedural Approach with PySpark

The procedural approach involves using PySpark code to control step-by-step execution of transformations, which is ideal for complex ETL pipelines requiring customization.

Example: Procedural Approach in PySpark

```python

from pyspark.sql.functions import upper

# Read raw data from Delta table

raw_data = spark.read.format("delta").load("/path/to/raw_data")

# Apply transformations

transformed_data = raw_data.select(

"id", "name", upper("name").alias("uppercase_name"), "date_of_birth"

)

# Write transformed data to Delta Lake

transformed_data.write.format("delta").mode("overwrite").save("/path/to/transformed_data")

```

- In this example, you explicitly load, transform, and write the data while managing dependencies manually.

### When to Use Procedural:

- Fine-tuning transformations (e.g., partitioning, caching).

- Handling complex joins, aggregations, or UDFs that are challenging to express declaratively.

- Implementing custom error handling or manual job orchestration beyond DLT’s capabilities.

### Conclusion

Choosing between declarative DLT and procedural PySpark depends on your specific use case. If your pipeline involves straightforward transformations and you prefer automation, go for declarative DLT. For more complex workloads requiring extensive customization and control, stick with procedural PySpark.

Which approach do you prefer? Let’s discuss how to fine-tune your specific use cases!

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