Databricks Lakeflow Feature

I'm starting a series where I'll share my learnings from my Data Engineering journey—whether it's from certification preparation, self-learning, projects and more.

In this post, let's see what Databricks Lakeflow is and why we need it.

1. What is Databricks Lakeflow?

Lakeflow is a unified solution within Databricks for data ingestion, transformation, and orchestration.

You can create an end-to-end ETL pipeline using only Lakeflow components:

• Lakeflow Connect:
  (ingest data from external src's with minimum effort, lakeflow take care of incremental load, schema change, ingestion pipeline, failures, retries)

• Lakeflow Declarative Pipelines:
  (formerly SDP) (declarative coding approach )

• Lakeflow Jobs: ( orchestration)

Within a single platform, you can build both batch and streaming pipelines, handle data ingestion and transformation, and manage pipeline dependencies, retry policies, error handling, and scheduling.

2. Why do we need Databricks Lakeflow?

Before Databricks Lakeflow, a typical Databricks pipeline required:

Data ingestion --> Auto Loader for incremental ingestion, custom notebooks, ADF
CDC processing --> Manually handling CDC with MERGE INTO
Transformation --> Notebooks
Orchestration --> Databricks Jobs, ADF
Monitoring --> Logs, dashboards
Streaming / Batch --> Different implementations

That means even a simple Bronze → Silver → Gold pipeline could involve:

ADF pipelines
Databricks Jobs
Custom logic for ingestion, CDC handling, and SCD implementation
Custom logic for pipeline monitoring

So, Databricks introduced Lakeflow.

Lakeflow combines:

Data ingestion
Data transformation
Data orchestration
Monitoring
CDC and SCD logic handling
Batch and streaming pipelines

Lakeflow components include:

• Lakeflow Connect
• Lakeflow Declarative Pipelines
• Lakeflow Jobs

In the next posts, we will explore each of these components in detail.

I’ll be sharing more posts on Data Engineering, covering concepts, tools, and practical learnings from my journey.

Follow along if you're interested in Data Engineering! 🚀