PySpark vs SQL Syntax Breakdown

Are you working with large datasets and wondering whether to use PySpark or SQL? Both have their advantages, but choosing the right one depends on your needs. SQL is declarative, making it easier for structured queries, while PySpark is procedural, offering powerful capabilities for distributed computing. Let's explore a detailed comparison of their syntax for common operations.

1️⃣ Selecting Data

🔹 SQL:

SELECT name, age FROM employees WHERE age > 30;

🔹 PySpark:

df.filter(df.age > 30).select("name", "age").show()

✅ SQL is simple and readable for traditional databases. ✅ PySpark uses a DataFrame API, making it great for distributed processing.

2️⃣ Grouping & Aggregation

🔹 SQL:

SELECT department, AVG(salary) FROM employees GROUP BY department;

🔹 PySpark:

from pyspark.sql.functions import avg
df.groupBy("department").agg(avg("salary")).show()

✅ SQL is easy for analysts familiar with traditional databases. ✅ PySpark provides built-in functions for aggregations in distributed environments.

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3️⃣ Joining Tables

🔹 SQL:

SELECT a.name, b.department  
FROM employees a  
JOIN departments b ON a.dept_id = b.dept_id;

🔹 PySpark:

df1.join(df2, df1.dept_id == df2.dept_id, "inner").select(df1.name, df2.department).show()

✅ SQL uses explicit JOIN clauses. ✅ PySpark joins are handled efficiently in distributed systems.

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4️⃣ Ordering Data

🔹 SQL:

SELECT * FROM employees ORDER BY salary DESC;

🔹 PySpark:

df.orderBy(df.salary.desc()).show()

✅ SQL makes ordering simple and intuitive. ✅ PySpark allows ordering within large-scale distributed data.

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5️⃣ Ranking Functions

🔹 SQL:

SELECT name, department, salary, 
       RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rank, 
       DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS dense_rank, 
       ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS row_number
FROM employees;

🔹 PySpark:

from pyspark.sql.window import Window
from pyspark.sql.functions import rank, dense_rank, row_number

windowSpec = Window.partitionBy("department").orderBy(df.salary.desc())

df.select("name", "department", "salary", 
          rank().over(windowSpec).alias("rank"), 
          dense_rank().over(windowSpec).alias("dense_rank"), 
          row_number().over(windowSpec).alias("row_number"))
.show()

✅ SQL ranking functions are useful for ordered datasets. ✅ PySpark implements ranking via Window functions for distributed computation.

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6️⃣ Handling Null Values (COALESCE)

🔹 SQL:

SELECT name, COALESCE(salary, 0) AS salary FROM employees;

🔹 PySpark:

from pyspark.sql.functions import coalesce
df.select("name", coalesce(df.salary, lit(0)).alias("salary")).show()

✅ SQL COALESCE replaces NULL values with a specified default. ✅ PySpark achieves the same using the coalesce() function.

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Key Takeaways:

🔹 SQL is perfect for structured queries, making it a great choice for data analysts and BI professionals.
🔹 PySpark is designed for big data processing, making it powerful for ETL pipelines, machine learning, and data engineering workflows.