Unlocking Data Insights: A Step-by-Step Guide to Achieving This Scenario on SQL, SparkSQL, and PySpark in Databricks

Are you tired of drowning in a sea of data without being able to extract meaningful insights? Do you want to unlock the full potential of your data and gain a competitive edge in the market? Look no further! In this comprehensive guide, we’ll walk you through the process of achieving a specific scenario on SQL, SparkSQL, and PySpark in Databricks, empowering you to make data-driven decisions like a pro.

What is This Scenario, You Ask?

The scenario we’ll be focusing on is a common one in data analysis: aggregating data from multiple tables, performing data transformations, and visualizing the results. We’ll use a fictional e-commerce dataset to demonstrate how to achieve this scenario using SQL, SparkSQL, and PySpark in Databricks.

The Dataset

Our dataset consists of three tables:

• orders: containing information about customer orders, such as order ID, customer ID, order date, and total amount
• customers: containing customer information, such as customer ID, name, email, and location
• products: containing product information, such as product ID, name, category, and price

Step 1: Data Ingestion and Preparation

The first step in achieving our scenario is to ingest and prepare the data. In Databricks, we can use the following PySpark code to read the CSV files and create DataFrames:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Data Ingestion").getOrCreate()

orders_df = spark.read.csv("orders.csv", header=True, inferSchema=True)
customers_df = spark.read.csv("customers.csv", header=True, inferSchema=True)
products_df = spark.read.csv("products.csv", header=True, inferSchema=True)

Data Cleaning and Transformation

Next, we need to clean and transform the data to make it suitable for analysis. Let’s perform the following operations:

• Convert the order_date column to a timestamp type
• Extract the year and month from the order_date column
• Create a new column order_total_usd by converting the total_amount column to USD using an exchange rate of 1 EUR = 1.20 USD

We can achieve this using the following PySpark code:

from pyspark.sql.functions import col, to_timestamp, year, month, expr

orders_df = orders_df.withColumn("order_date", to_timestamp(col("order_date"), "yyyy-MM-dd"))
orders_df = orders_df.withColumn("order_year", year(col("order_date")))
orders_df = orders_df.withColumn("order_month", month(col("order_date")))
orders_df = orders_df.withColumn("order_total_usd", expr("total_amount * 1.20"))

Step 2: Data Aggregation and Joining

Now that our data is clean and transformed, let’s aggregate the data and perform joins using SQL, SparkSQL, and PySpark.

SQL Approach

We can use the following SQL query to aggregate the data and perform joins:

SELECT 
  o.order_year, 
  o.order_month, 
  c.location, 
  SUM(o.order_total_usd) AS total_order_value
FROM 
  orders o
  JOIN customers c ON o.customer_id = c.customer_id
  JOIN products p ON o.product_id = p.product_id
GROUP BY 
  o.order_year, 
  o.order_month, 
  c.location
ORDER BY 
  total_order_value DESC

SparkSQL Approach

We can translate the SQL query to SparkSQL using the following PySpark code:

from pyspark.sql.functions import col, sum

orders_df.createOrReplaceTempView("orders")
customers_df.createOrReplaceTempView("customers")
products_df.createOrReplaceTempView("products")

spark.sql("""
  SELECT 
    o.order_year, 
    o.order_month, 
    c.location, 
    SUM(o.order_total_usd) AS total_order_value
  FROM 
    orders o
    JOIN customers c ON o.customer_id = c.customer_id
    JOIN products p ON o.product_id = p.product_id
  GROUP BY 
    o.order_year, 
    o.order_month, 
    c.location
  ORDER BY 
    total_order_value DESC
""")

PySpark Approach

We can also achieve the same result using PySpark’s DataFrame API:

from pyspark.sql.functions import col, sum

orders_df = orders_df.join(customers_df, orders_df.customer_id == customers_df.customer_id)
orders_df = orders_df.join(products_df, orders_df.product_id == products_df.product_id)

result_df = orders_df.groupBy("order_year", "order_month", "location").agg(sum("order_total_usd").alias("total_order_value"))
result_df = result_df.sort("total_order_value", ascending=False)

Step 3: Data Visualization

Finally, let’s visualize the results using a bar chart to showcase the top 10 locations by total order value.

from matplotlib.pyplot import figure, show

result_df.toPandas().head(10).plot(kind="bar", x="location", y="total_order_value", figsize=(10, 6))
show()

Result

The output should look something like this:

order_year	order_month	location	total_order_value
2022	6	New York	100000.00
2022	7	Los Angeles	80000.00
2022	5	Chicago	70000.00

Congratulations! You’ve successfully achieved the scenario using SQL, SparkSQL, and PySpark in Databricks. You can now use this guide as a starting point to analyze and visualize your own datasets.

Conclusion

In this article, we walked you through the process of achieving a specific scenario on SQL, SparkSQL, and PySpark in Databricks. We ingested and prepared the data, performed data cleaning and transformation, aggregated and joined the data, and visualized the results. By following these steps, you can unlock the full potential of your data and make informed business decisions.

What’s Next?

Want to learn more about Databricks and PySpark? Check out our other articles on:

• Data Engineering with Databricks
• PySpark for Data Scientists
• Databricks Best Practices for Production-Ready Workloads

Happy learning, and don’t forget to share your thoughts and feedback in the comments below!

Frequently Asked Question

Wondering how to achieve a specific scenario in SQL/SparkSQL/PySpark on Databricks? We’ve got you covered! Check out these frequently asked questions and get ready to unlock the power of data analytics.