Building real-time data pipelines on Azure Databricks

Introduction

Modern data platforms demand real-time capabilities — from ingestion to transformation to serving data for BI and ML use cases. Azure Databricks offers three powerful tools to help with this:

• Auto Loader: For scalable, file-based ingestion.

• Delta Live Tables (DLT): For managed, declarative data pipelines.

• Spark Structured Streaming: For advanced, low-level stream processing.

Each serves a unique purpose within the Lakehouse architecture. Let us explore how these components work together on Azure Databricks, when to use each, and how to build a real-time architecture that’s robust, efficient, and cloud-native.

1. Spark Structured Streaming: Low-Level Power

Apache Spark Structured Streaming is the core API for building real-time applications in Databricks. It supports continuous ingestion and transformation of streaming data (like Kafka, Event Hubs, or files), and enables advanced features like windowed aggregation, watermarking, and stateful operations.

When to Use:

• You need full control over your streaming logic (and say DLT or auto loader does not meet your requirements).

• You need complex, custom streaming logic (e.g., windowed joins, real-time aggregations).

• You are building low-level real-time applications such as fraud detection or alerting systems.

• You need full control over source/sink options and custom checkpointing.

Example use cases:

• Real-time anomaly detection using streaming joins between IoT sensor streams.

• Stateful aggregations

• Complex event processing

• Real-time dashboards that refresh every few seconds

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2. Auto Loader: Scalable Cloud File Ingestion

Auto Loader is a source ingestion tool in Azure Databricks that uses an optimized, scalable way to ingest new files from cloud storage (e.g. ADLG gen 2). It leverages Spark Structured Streaming under the hood but abstracts away the complexity.

When to Use:

• You need to ingest files (JSON, CSV, Parquet) as they arrive in cloud storage such as Azure Data Lake Storage (ADLS).

• You want scalable, incremental ingestion without listing full directories.

• You need near real-time file ingestion at scale.

• You want to avoid costly file listing operations.

Supported Features:

• Schema inference or evolution

• File notification mode (via Event Grid)

• Incremental file discovery

• Works with DLT as a streaming source

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3. Delta Live Tables (DLT): Declarative, Production-Grade Pipelines

DLT is a declarative framework built by Databricks to manage batch and streaming data pipelines. It supports ingestion, transformation, data quality checks, and lineage tracking. It uses Spark under the hood, including Structured Streaming for streaming inputs.

When to use:

• You want managed, production-grade data pipelines.

• You want built-in observability, auto-recovery, and deployment support.

• You want to enforce data quality with expect() rules.

• You prefer a declarative approach with built-in error handling.

• DLT supports both batch and streaming sources and can be used alongside Auto Loader.

Example use cases:

• Building Bronze → Silver → Gold pipelines

• Maintaining clean, audited tables for analytics and ML

• Monitoring data health with auto-generated metrics and lineage

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Summary:

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Sample codes / usage patterns:

• Common data loading patterns - Azure Databricks | Microsoft Learn

• Tutorial: Run your first DLT pipeline - Azure Databricks | Microsoft Learn

• dlt-babynames-python - Databricks

• Structured Streaming patterns on Azure Databricks - Azure Databricks | Microsoft Learn

References:

• Azure Databricks Auto Loader Docs

• Azure Databricks Delta Live Tables Docs

• Structured Streaming Programming Guide