Bridging Data Lakes and Warehouses in Azure

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Introduction

Organizations today face an explosion of data coming from APIs, IoT devices, clinical systems, and legacy infrastructure. Healthcare providers, in particular, are burdened with integrating diverse formats such as HL7 messages, real-time streams, and structured claims data. This data variety makes it difficult to create a unified analytics platform that can support both operational reporting and advanced analytics.

The traditional divide between data lakes and data warehouses adds complexity. Data lakes offer flexible, low-cost storage for all types of data, but lack transactional consistency and query performance. Data warehouses provide optimized performance for structured data, but are less adaptable to change and expensive at scale.

This blog explores how Azure's powerful data services can help you bridge this divide using a Lakehouse architecture. We'll walk through practical implementation details using Azure Synapse, Event Hubs, DuckDB, Azure Blob Storage, and more, including how to process HL7 messages over SMTP and integrate internal and external APIs.

Azure Tools for a Lakehouse

To implement a Lakehouse architecture in Azure, we use a carefully selected mix of services:

  • Azure Blob Storage: This acts as the raw and curated data lake layer, where data lands in its native format (JSON, Parquet, CSV, etc.). It offers virtually unlimited, low-cost storage and supports integration with many Azure analytics services.

  • Azure Event Hubs: A real-time data ingestion service, ideal for high-throughput event streams. It is used here to ingest HL7 messages from various hospital systems as well as other real-time data from APIs.

  • Azure Synapse Analytics: Azure's flagship analytics platform that brings together big data and data warehousing. We leverage:

    • Serverless SQL Pools to query files directly from Blob Storage without loading them into a database.

    • Managed SQL Pools (dedicated) for fast querying and pre-aggregated business logic.

  • Azure SQL Managed Instance (SQL MI): A bridge for transactional workloads. This helps unify traditional OLTP systems with the analytical backend.

  • DuckDB: An embedded analytical database that's incredibly fast for local data exploration. Perfect for prototyping and dev workflows.

  • External/Internal APIs: RESTful APIs from scheduling, billing, and clinical systems serve as valuable data sources. These are integrated using Data Factory, Logic Apps, or custom connectors.

  • SMTP + HL7: Many healthcare organizations still rely on SMTP to send HL7 messages. These messages must be decoded, parsed, and enriched before storage.

Reference Architecture

The following end-to-end data flow illustrates how these components interact:

  1. Data Ingestion:

    • HL7 messages sent via SMTP are intercepted by Logic Apps or custom SMTP parsers.

    • Messages are decoded into JSON using Azure Functions and posted to Event Hubs.

    • Event Hubs streams are processed via Azure Stream Analytics or Spark to Azure Blob Storage in a raw zone.

    • External APIs deliver JSON or XML files into Blob Storage using Azure Data Factory pipelines.

  2. Data Transformation:

    • Azure Synapse Pipelines or Spark Notebooks clean, normalize, and enrich data.

    • Transformed datasets are written to a refined zone in Blob as Parquet files.

    • Structured tables and materialized views are loaded into Managed SQL Pools for fast access.

  3. Query and Serve:

    • Analysts use Serverless SQL Pools to run exploratory queries over raw and refined zones.

    • Managed SQL Pools support curated data marts and BI dashboards.

    • SQL MI acts as an integration point with upstream applications needing transactional consistency.

    • Developers and analysts use DuckDB to analyze small samples quickly without provisioning cloud resources.

  4. Consumption:

    • Power BI connects to Synapse and visualizes dashboards.

    • Business users interact with data through internal APIs built on SQL MI or Synapse endpoints.

    • ML engineers access Parquet files directly from Blob for model training and scoring.

HL7 over SMTP Workflow

Many healthcare systems still send lab results and encounter data via HL7 messages over email (SMTP). These messages must be parsed, validated, and transformed into structured formats suitable for analytics.

Here’s how it works:

  • A hospital system sends HL7 v2.x messages through secure SMTP channels.

  • A Logic App captures the email and extracts the HL7 content.

  • Azure Function parses the HL7 string using hl7apy or a custom parser, converting it to JSON.

  • The structured output is sent to Event Hubs for streaming.

  • Stream Analytics pushes the messages to Blob Storage and/or directly into Synapse tables.

Sample HL7 Parsing Snippet (Python):

from hl7apy.parser import parse_message

hl7_raw = 'MSH|^~\\&|SendingApp|SendingFac|ReceivingApp|...'
msg = parse_message(hl7_raw)
patient_id = msg['PID']['PID.3'].value

Querying with Serverless SQL

Once your data is stored as Parquet in Blob, you can query it directly with no ETL:

SELECT 
  patient_id, 
  test_name, 
  result_value 
FROM 
  OPENROWSET(
    BULK 'https://<storage>.blob.core.windows.net/lake/hl7/parsed/*.parquet',
    FORMAT = 'PARQUET'
  ) AS hl7_data
WHERE 
  test_name = 'Hemoglobin';

This allows data analysts and scientists to derive insights without waiting for data to be loaded into a warehouse.

DuckDB for Local Analytics

DuckDB is a powerful tool for data practitioners who want to run fast SQL queries on local files. It is ideal for dev environments or quick prototyping.

import duckdb

df = duckdb.read_parquet('sample.parquet')
duckdb.query("SELECT COUNT(*) FROM df WHERE result_value < 10").fetchall()

DuckDB supports Parquet and CSV, integrates with Pandas, and requires zero setup.

Governance & Performance Tips

Implementing a Lakehouse on Azure requires attention to governance, performance, and cost efficiency:

  • Use Azure Purview for metadata discovery and lineage tracking.

  • Apply RBAC and Synapse workspace-level security to control access.

  • Store large datasets in partitioned Parquet format for optimal serverless performance.

  • Offload cold storage to Blob and use tiered access policies to manage cost.

  • For high-concurrency workloads, pre-aggregate and store summaries in Managed SQL Pools.

Conclusion

The boundary between lakes and warehouses is fading. Azure provides a robust ecosystem to unify raw, semi-structured, and curated data into a flexible, scalable Lakehouse platform. Whether you’re working with HL7 messages, external APIs, or massive JSON feeds, Azure tools like Synapse, Blob Storage, SQL MI, and Event Hubs enable seamless integration, transformation, and analysis.

Start with Blob Storage and Serverless SQL Pools to build a low-cost foundation. Then, scale into Managed SQL Pools for enterprise reporting, and leverage tools like DuckDB for flexible, local experimentation. With thoughtful design, your Lakehouse can become the cornerstone of both operational intelligence and strategic insights.

Call to Action
Need help designing or deploying a Lakehouse for healthcare or enterprise data? Reach out to discuss architecture reviews, implementation strategies, or hands-on development support!

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