WHEN TO USE DELTA LIVE TABLES (DLT)

Delta Live Tables (DLT) is most useful when you want automated, reliable, and declarative ETL pipelines with built-in data quality and orchestration. Below are the primary scenarios where DLT is the best choice.

1. Continuous or Streaming Ingestion

Data arrives frequently (minutes or seconds).
You need automated incremental ingestion.
DLT works naturally with Auto Loader and STREAMING LIVE TABLES.

2. Built-in Data Quality (Expectations)

You want to validate data with declarative rules.
DLT can warn, drop, or fail on bad records.
No custom validation framework needed.

3. Automatic Bronze → Silver → Gold Orchestration

DLT builds dependency DAGs automatically.
Ensures correct ETL ordering.
Retries, error handling, and job coordination handled by Databricks.

4. Data Lineage, Monitoring, and Observability

DLT provides a full graphical lineage view.
You can monitor row counts, errors, and latency.
Ideal for production-grade auditing and compliance.

5. Automatic Table Optimization

DLT performs background OPTIMIZE and VACUUM.
Improves performance without manual maintenance jobs.

6. Managing SCD Type 2 with Minimal Code

dlt.apply_changes can implement SCD Type 2 automatically.
No need to write long MERGE statements.
DLT handles versioning, current flags, and historical tracking.

7. Declarative Pipelines Instead of Manual Jobs

You define what the tables should be, DLT figures out how to build them.
Reduces boilerplate and makes pipelines easier to maintain.

8. Teams That Prefer Low-Code / No-Ops Data Engineering

DLT reduces operational overhead.
Good for teams without heavy Spark expertise.
Ensures consistent engineering practices across the project.

Summary

Use DLT for streaming, continuous, or reliable incremental pipelines.
Use DLT when you want built-in data quality, monitoring, and orchestration.
Use DLT to simplify complex logic such as SCD Type 2.
Use DLT when you want automatic optimization and fewer maintenance tasks.