Unlocking Data Intelligence with Databricks: Cloud Use Cases for Azure, AWS, and GCP

Introduction

In today’s data-driven world, every organizational role, from CEOs to interns, contributes to transforming data into actionable insights. Marketing analysts personalize retail campaigns, while banking fraud analysts detect suspicious transactions. Predictive maintenance helps manufacturing and transportation engineers reduce downtime, and AI/ML professionals address use cases like product recommendations (e-commerce), churn prediction (telecom), demand forecasting (supply chain), and healthcare diagnostics.

Tredence specializes in converting data, AI, and ML initiatives into business outcomes using advanced tools and industry expertise. One such tool is Databricks—a unified, cloud-based platform built on Apache Spark that integrates big data processing, data science, and AI. Operating on AWS, Azure, or GCP, Databricks enables real-time and batch processing, supporting high-performance computing and AI insights. Companies like AT&T, Burberry, and Rivian use Databricks to cut costs, enhance security, and drive innovation.

This blog will cover Databricks essentials, comparing its features and benefits across major cloud platforms, helping businesses choose the right cloud platform with Databricks to build an efficient data pipelines, improve cost-efficiency, and align cloud strategies with operational goals.

High-Level Data Pipeline Workflow

1. The workflow begins with data ingestion from cloud or on-prem storage sources.
2. Data is then loaded into the Bronze Layer of the Medallion Architecture, capturing raw ingested data.
3. The Silver Layer refines and cleanses data, enhancing its usability.
4. The Gold Layer aggregates and curates data for analytics and downstream consumption.
5. Finally, data is loaded into a cloud data warehouse (e.g., Synapse+ADF, Redshift, BigQuery) and connected to BI dashboards for actionable insights.

Azure Databricks

Azure Databricks integrates with Azure Storage (ADLS, Blob) and databases like Azure SQL, Synapse, and Cosmos DB for data storage. Optimized Apache Spark clusters handle large-scale processing within Databricks Runtime. Security and governance are ensured via Microsoft Entra ID, RBAC, and Unity Catalog. Visualization tools include Power BI, AAS, Tableau, and Looker, while Event Hub, Kafka, and Delta Live Tables support real-time streaming.

AWS Databricks

AWS Databricks connects with S3 Storage for data lakes and RDS, Redshift, and DynamoDB for structured and unstructured data. High-performance Apache Spark clusters run within Databricks Runtime. Security is managed by AWS IAM, Unity Catalog, and Lake Formation. QuickSight, Tableau, and Looker support visualization, while Kinesis, Kafka (MSK), and Delta Live Tables enable real-time streaming. AWS Glue, Lambda, and Data Pipeline automate workflows.

GCP Databricks

GCP Databricks integrates with GCS for data lakes and BigQuery, Cloud SQL, and Firestore for data storage. Large-scale processing is powered by Databricks Runtime and Apache Spark. Security is ensured by Google IAM, Unity Catalog, and VPC Service Controls. Visualization tools include Looker, Tableau, and Data Studio, while Pub/Sub, Kafka, and Delta Live Tables handle streaming. Dataflow, Cloud Functions, and Dataproc automate ingestion and pipelines.

Common Features

Across all clouds, Databricks offers Notebooks and Jobs for pipeline execution and supports AI/ML with MLflow, AutoML, TensorFlow, and PyTorch.

Azure Databricks Use Case

Migrating from ADF/Synapse Analytics to Databricks

Use Case: Customer Information Analytics (Person, Phone, Email)

A large enterprise wanted to modernize its data pipeline, migrating from Azure Data Factory (ADF) and Synapse Analytics to Databricks for better performance and scalability.

Solutions: Data Factory ingests customer data (person, phone, email) from various sources into Azure Data Lake Storage (ADLS Gen2). We used Azure Databricks cluster with credential passthrough to securely mount ADLS, processing the data using Apache Spark for faster and more scalable analytics. Workflows in Databricks automate and schedule data pipelines.

Processed data is sent to Power BI, enabling business users to generate reports and insights.

Outcome are:

It reduced ETL processing time by 40% compared to ADF / Synapse Analytics by unifying entire ETL processing inside Databricks. Additionally, it improved data security with Microsoft Entra ID and credential passthrough and enabled self-service analytics using Power BI, increasing business agility.

Why Choose Azure Databricks:

• Seamless integration with Power BI and Azure services like ADF and Synapse.
• Additionally, ADF or Synapse Data pipelines and orchestration can be migrated to Databricks by using spark code and workflows.
• Ideal for organizations using Microsoft’s tech stack.
• Recommended for structured data workloads and business intelligence.

We implemented a straightforward use case to showcase these functionalities, presenting a complete end-to-end Azure Databricks project. Here is the GitHub link.

AWS Databricks Use Case

Sales Data Analytics

Use Case: Sales Performance Monitoring

A global retailer needed a scalable solution to analyze sales data for improving operational efficiency and decision-making.

Solution: For Data Ingestion, we used S3 stores sales data, while AWS Glue Data Catalog organizes and manages metadata. AWS Databricks processes the sales data using Apache Spark, ensuring high performance and scalability. Then we used Unity Catalog which centralizes data governance, ensuring security and compliance. Processed data is visualized using Power-BI, providing business teams with sales dashboards.

Outcome: It improved sales performance tracking across regions, reduced data latency, enabling insights and enhanced data governance using Unity Catalog.

Why Choose AWS Databricks:

• Best for real-time analytics and multi-cloud flexibility.
• Cost-efficient compute scaling with EC2 Spot Instances.
• Ideal for organizations needing scalable AI pipelines.

We demonstrated a simple use case to highlight these features, presenting a full end-to-end AWS Databricks project. Here is the GitHub link.

GCP Databricks Use Case

Converting Airflow DAGs to Databricks Workflows
Use Case: AI-Driven Demand Forecasting
A manufacturing company wanted to enhance its demand forecasting by migrating from Airflow DAGs to Databricks workflows for improved performance and scalability.

Solution: Google BigQuery stores structured demand data as part of data ingestion process. Apache Airflow DAGs were converted to Databricks workflows, automating data pipelines with enhanced efficiency. Apache Spark processes large datasets, enabling faster and more accurate forecasts. For Data Visualization, we used Looker to visualize those processed data which provides teams with actionable insights.

Outcome: We were able to improve forecast accuracy by 20%, reducing supply chain disruptions. Automated workflows reduced manual effort, increasing productivity, scalability and performance.

Why Choose GCP Databricks:

• Best for AI and machine learning workloads.
• Seamless integration with BigQuery and Vertex AI.
• Ideal for organizations focusing on AI-driven insights.

To illustrate these functionalities, we built a simple use case and delivered a comprehensive end-to-end GCP Databricks implementation. Here is the GitHub link.

Cloud Provider Selection for Databricks

While Databricks offers consistent functionality across all three platforms, each cloud provider has unique benefits based on business needs:

Comparison Table:

Key takeaways are:

Summary

Databricks helps businesses to unlock the full potential of their data, whether through real-time analytics, AI-driven insights, or scalable data processing. By choosing the right cloud platform Azure for Microsoft-centric ecosystems, AWS for real-time and multi-cloud capabilities, or GCP for AI and ML workloads organizations can optimize performance, reduce costs, and drive innovation. Regardless of the platform, Databricks provides the scalability, reliability, security, deployability, and collaboration tools needed to transform data into actionable business outcomes.

AUTHOR - FOLLOW
Abhijit Das
DE Senior Architect