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Building Databricks Pipelines: Job Orchestration and Delta Live Tables Demo

By: Pallari Mujumdar

In this Databricks demo, Green Leaf Data Analytics Specialist, Pallari, walks through two practical approaches to building data pipelines in Databricks.

First, she demonstrates how to use job orchestration to run a layered data model across staging, data vault, and presentation. Then she shows how to use Delta Live Tables (DLT) to create a more declarative pipeline that simplifies orchestration, automation, and pipeline management.

The session also highlights how Databricks supports streaming tables, automatic dependency handling, and SCD Type 2 history tracking with minimal code. By the end, viewers will better understand when to use traditional job orchestration versus Delta Live Tables in Databricks.

This demo is ideal for data engineers, architects, and technical leaders exploring scalable ELT patterns, automated pipelines, and historical data tracking in Databricks.

Watch the full discussion demo below.

Data Analytics Specialist
Pallari Mujumdar is a Data Developer at Green Leaf Consulting Group, where she builds and maintains data pipelines and platform infrastructure for enterprise systems, working across Azure Synapse, Snowflake, and Databricks. Her work spans the full pipeline lifecycle transforming and optimizing large-scale data, designing scalable data models, and engineering reliable ingestion and orchestration workflows. She is SnowPro Core certified and continues to build her expertise in AI-driven data solutions. Beyond the technical work itself, Pallari is drawn to the leadership side of IT and data engineering helping teams build stronger data foundations and shaping how organizations use data strategically. She's especially energized by the intersection of data engineering and AI, and is always looking for opportunities to grow into roles where she can lead teams and drive that direction forward.

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