How to Read Lake Database Tables & Views in Azure Synapse Using PySpark
📘 Overview
In Azure Synapse Analytics, you can use PySpark to read tables and views from a Lake Database. This allows you to harness the power of Apache Spark for analytics on curated datasets in your Data Lake.
✅ Why Read Lake Database in PySpark?
- Supports reading from Delta and Parquet formats
- Leverages Spark for distributed processing
- Easy integration with Synapse Notebooks
🛠️ Step-by-Step Guide
Step 1: Read a Table from Lake Database
df = spark.read.table("LakeDBName.TableName")
df.show()Replace LakeDBName and TableName with your actual Lake Database and table names.
Step 2: Read a View from Lake Database
view_df = spark.read.table("LakeDBName.ViewName")
view_df.display()Step 3: Register as Temp View for SQL Queries
df.createOrReplaceTempView("temp_table")
spark.sql("SELECT * FROM temp_table WHERE column = 'value'").show()🔎 Sample Use Case
- Read curated sales data from Lake Database
- Register as temp view
- Join with another dataset using Spark SQL
📌 Notes
- Ensure the Spark pool has access to the Lake Database metadata
- Use
display()in notebooks to visualize the output
📺 Watch the Full Video Tutorial
📚 Credit: Content created with the help of ChatGPT and Gemini.