Introduction

Databricks is a powerful cloud-based data analytics platform, designed to handle big data and machine learning. When integrated with Azure services, it becomes a complete data and AI solution, allowing businesses to process, analyze, and visualize data efficiently.

In this article, we'll explore:
Connecting Databricks with Azure Synapse, Power BI, and Event Hubs
Using Azure Machine Learning with Databricks

This guide is perfect for data engineers, analysts, and AI professionals looking to optimize their Azure Databricks workflows.

Why Do We Need Azure Synapse When We Already Have Data Lakes?

Azure Data Lake is great for storing large amounts of raw data, but when it comes to fast querying, structured analytics, and business intelligence, Azure Synapse becomes essential.

Key Differences Between Data Lake & Azure Synapse

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1.Why is Azure Synapse Required if We Already Have a Data Lake?

Even though Azure Data Lake is great for storing raw data, it lacks features for fast querying, analytics, and integration with BI tools. That’s where Azure Synapse comes in!

1.1 Azure Synapse is Built for Fast Analytics

• Querying data directly from a data lake is slow because it processes raw files (CSV, JSON, Parquet).

• Azure Synapse uses optimized storage (like columnar indexing) to make queries 10x faster than querying raw files.

Example:
🔹 Running SQL on raw CSV files in a data lake → ❌ Slow
🔹 Running SQL on structured tables in Synapse → ✅ Fast

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1.2. Synapse Provides a SQL-Like Interface for Data Lakes

• In Data Lake, data is stored in files, and you need Databricks or Spark to process it.

• In Synapse, you can run SQL queries directly on your data lake using Serverless SQL Pools.

Example: Querying Data in Data Lake with Synapse SQL

SELECT * FROM OPENROWSET(
    BULK 'https://datalake.blob.core.windows.net/sales_data.parquet',
    FORMAT='PARQUET'
) AS sales;

This runs much faster than using Spark to process the raw file!

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1.3. Better Performance with Data Warehousing

• Synapse stores data in a structured format, making it easy for BI tools like Power BI, Tableau, and Looker to generate reports quickly.

• Data Lakes store raw data, but BI tools don’t work efficiently on raw files.

Example:

• If you need fast Power BI dashboards, querying structured data from Synapse is 10x faster than from a Data Lake!

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1.4. Handles Large-Scale Data Processing Efficiently

• Synapse can process billions of rows in seconds using Dedicated SQL Pools.

• Data Lakes require additional Spark clusters (which take time and cost money) to process large queries.

Example:
A company wants to analyze customer transactions (10 billion records) to detect fraud.

• Data Lake alone? → ❌ Slow, needs additional Spark jobs.

• Synapse? → ✅ Optimized for fast queries on structured data.

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1.5. Security, Governance & Access Control

• Azure Synapse provides advanced role-based security and data governance features, which Data Lake alone lacks.

• Synapse integrates with Microsoft Purview for data lineage, auditing, and compliance.

Example:

• A bank needs strict access control on financial transactions.

• Synapse allows role-based access so only approved users can see sensitive data.

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When Should You Use Azure Synapse Instead of Data Lake?

🔹 If you need fast SQL querying on large datasets.
🔹 If you want to use Power BI or other BI tools for reporting.
🔹 If you need structured, organized data for analytics.
🔹 If your data is too big for a traditional SQL database but needs fast querying.
🔹 If you need better security & compliance for sensitive data.

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Do You Need Both Data Lake & Azure Synapse?

Yes! A Data Lake is great for storing raw data, but Synapse is required for structured analytics & reporting.
Together, they create a powerful data architecture known as the "Data Lakehouse."

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2. Why Integrate Databricks with Azure Services?

Azure Databricks is a managed Spark-based analytics service that works seamlessly with other Azure services to:
✔️ Process large-scale structured & unstructured data
✔️ Create end-to-end data pipelines
✔️ Enable real-time analytics & reporting
✔️ Use machine learning & AI models

Common Use Cases:
🔹 Real-time data processing from IoT devices
🔹 Data analytics & dashboards for business intelligence
🔹 AI-powered predictions using machine learning models

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1️⃣ Connecting Databricks with Azure Synapse Analytics

Azure Synapse Analytics (formerly Azure SQL Data Warehouse) is a powerful data warehouse for storing & querying structured data.

Why Connect Databricks with Azure Synapse?

✔️ Process big data in Databricks and store results in Synapse
✔️ Run SQL analytics on large datasets
✔️ Use Synapse as a centralized data warehouse

Step 1: Set Up Linked Service in Azure Synapse

1️⃣ Open Azure Synapse Studio → Click Manage
2️⃣ Under Linked Services, click New
3️⃣ Select Azure Databricks → Configure with Databricks workspace URL & token

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Step 2: Load Data from Databricks to Azure Synapse

Example: Writing Data from Databricks to Synapse

from pyspark.sql import SparkSession

# Initialize Spark Session
spark = SparkSession.builder.appName("Databricks_Synapse").getOrCreate()

# Load Data into DataFrame
df = spark.read.csv("abfss://datalake@storageaccount.dfs.core.windows.net/data.csv", header=True)

# Write Data to Synapse
df.write \
    .format("com.databricks.spark.sqldw") \
    .option("url", "jdbc:sqlserver://<synapse_server>.database.windows.net;database=<synapse_db>") \
    .option("user", "<your_username>") \
    .option("password", "<your_password>") \
    .option("forward_spark_azure_storage_credentials", "true") \
    .option("dbtable", "sales_data") \
    .mode("overwrite") \
    .save()

Now, your data is available in Azure Synapse for reporting & analysis!

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2️⃣ Connecting Databricks with Power BI for Reporting

Power BI is Microsoft’s business intelligence (BI) tool that helps visualize data with interactive dashboards.

Why Connect Databricks with Power BI?

✔️ Generate real-time reports from Databricks data
✔️ Visualize trends & business insights
✔️ Automate dashboards for decision-making

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Step 1: Enable Databricks Connector in Power BI

1️⃣ Open Power BI Desktop → Click Get Data
2️⃣ Search for Azure Databricks → Click Connect
3️⃣ Enter your Databricks Server Hostname & Token
4️⃣ Select the table or query to load data

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Step 2: Load Data from Databricks to Power BI

Example: Creating a SQL Table in Databricks for Power BI

df.createOrReplaceTempView("sales_view")

Now, you can query this view from Power BI:

SELECT * FROM sales_view

Your Power BI dashboard is now connected to live Databricks data!

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3️⃣ Streaming Data from Azure Event Hubs to Databricks

Azure Event Hubs is a real-time event streaming service that captures logs, IoT data, and event streams.

Why Use Event Hubs with Databricks?

✔️ Ingest real-time event data from devices, applications, or logs
✔️ Process and analyze streaming data for fraud detection, IoT monitoring, etc.
✔️ Feed event streams into ML models for predictive analytics

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Step 1: Set Up Azure Event Hubs in Databricks

1️⃣ Create an Event Hub Namespace in Azure
2️⃣ Configure Event Hub name & connection string
3️⃣ Install the Databricks Event Hub Connector

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Step 2: Stream Data from Event Hubs to Databricks

Example: Reading Event Hub Data in Databricks

from pyspark.sql.functions import *
from pyspark.sql.types import *

# Define Event Hub connection
event_hub_config = {
    "eventhubs.connectionString": "<your_event_hub_connection_string>"
}

# Read Streaming Data
df = spark.readStream \
    .format("eventhubs") \
    .options(**event_hub_config) \
    .load()

# Convert Data to JSON Format
df = df.selectExpr("CAST(body AS STRING)")

# Write Stream to Delta Table
df.writeStream \
    .format("delta") \
    .option("checkpointLocation", "/mnt/checkpoints/event_hub") \
    .start("/mnt/delta/event_data")

Now, you can process real-time events in Databricks for analytics & ML!

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4️⃣ Using Azure Machine Learning with Databricks

Azure Machine Learning (AML) helps train, manage, and deploy AI models at scale.

Why Use Databricks with Azure Machine Learning?

✔️ Train ML models on large-scale Databricks data
✔️ Deploy ML models as APIs in Azure
✔️ Automate ML workflows using Azure ML Pipelines

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Step 1: Set Up Azure ML in Databricks

1️⃣ Install Azure ML SDK in Databricks

!pip install azureml-sdk

2️⃣ Connect to Azure ML Workspace

from azureml.core import Workspace
ws = Workspace.get(name="your_workspace")

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Step 2: Train an ML Model in Databricks

Example: Training a Simple Regression Model in Databricks

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd

# Load Data
data = pd.read_csv("/dbfs/mnt/datalake/housing.csv")
X_train, X_test, y_train, y_test = train_test_split(data[['sqft']], data['price'], test_size=0.2)

# Train Model
model = LinearRegression()
model.fit(X_train, y_train)

# Save Model to Azure ML
import joblib
joblib.dump(model, "house_price_model.pkl")

Now, this ML model can be deployed as an API using Azure ML!

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Summary: Why Use Databricks with Azure?

By integrating Databricks with Azure services, businesses can build powerful, scalable, and real-time analytics platforms!

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