Q- we choice this data Set to work with oky : 


About Dataset
Context
Welcome to the shopping world of Istanbul! Our dataset contains shopping information from 10 different shopping malls between 2021 and 2023. We have gathered data from various age groups and genders to provide a comprehensive view of shopping habits in Istanbul. The dataset includes essential information such as invoice numbers, customer IDs, age, gender, payment methods, product categories, quantity, price, order dates, and shopping mall locations. We hope that this dataset will serve as a valuable resource for researchers, data analysts, and machine learning enthusiasts who want to gain insights into shopping trends and patterns in Istanbul. Explore the dataset and discover the fascinating world of Istanbul shopping!
Content
Attribute Information:
invoice_no: Invoice number. Nominal. A combination of the letter 'I' and a 6-digit integer uniquely assigned to each operation.
customer_id: Customer number. Nominal. A combination of the letter 'C' and a 6-digit integer uniquely assigned to each operation.
gender: String variable of the customer's gender.
age: Positive Integer variable of the customers age.
category: String variable of the category of the purchased product.
quantity: The quantities of each product (item) per transaction. Numeric.
price: Unit price. Numeric. Product price per unit in Turkish Liras (TL).
payment_method: String variable of the payment method (cash, credit card or debit card) used for the transaction.
invoice_date: Invoice date. The day when a transaction was generated.
shopping_mall: String variable of the name of the shopping mall where the transaction was made.
Q- we do this 
"""
Q- this is a version Two which output the "Header" and "Infer The Schema" oky




"""
val df = spark.read.option("header","true").option("inferSchema","true").csv("dbfs:/FileStore/shared_uploads/amir21abdallah@gmail.com/customer_shopping_data.csv")
df.show(5)

As your Senior Data Scientist, here are seven data analysis questions derived from your Istanbul shopping malls dataset, categorized from easy to hard:
Easy Questions:
Sales Overview
What is the total revenue generated by each shopping mall from 2021 to 2023?
Customer Demographics
What is the distribution of customers across different age groups and genders?


Q- the first question is : 

—> we need to get the total revenue generated by each shopping mall from 2021 to 2023 


Q- before diving into the solution ? 

→ i need to get an view into how many type (categoties) of malls we have in this data oky ? 

→ the command will be like follow this logic 

Extract the column that contain the “malls “ 
Goup them by distinc value 
Sum them to get the number of dstincs onesoky 
Q- give the scala spark command here so 


Q- this the solution from “gemini”      import org.apache.spark.sql.functions._

// Load the DataFrame (assuming you've already done this)
val df = spark.read.option("header","true").option("inferSchema","true").csv("dbfs:/FileStore/shared_uploads/amir21abdallah@gmail.com/customer_shopping_data.csv")

// Calculate the number of distinct shopping malls
val distinctMallCount = df.select("shopping_mall").distinct().count()

// Print the result
println(s"Number of distinct shopping malls: $distinctMallCount")


// You can also display the distinct mall names if you want:
df.select("shopping_mall").distinct().show()
   
content_copy Use code with caution.Scala
Explanation:
df.select("shopping_mall"): This selects only the shopping_mall column from the DataFrame.
.distinct(): This removes duplicate values, so you get only the unique shopping mall names.
.count(): This counts the number of rows remaining after the distinct() operation, which gives you the number of distinct shopping malls.
println(...): This prints the result to the console.
The alternative using show() will display the actual mall names, which can be helpful for understanding your data. count() is more useful if you just need the numerical count.
This gives you the number of different shopping malls present in your dataset before you proceed to calculate the total revenue for each.

Q- but my solution look like this 

“
"""
Q- this "query" do not asnwer the right question but it's help us find later the global solution of the first problem oky
"""
val distinctMallCount = df.select("shopping_mall").distinct().count()


println(s"Number of distinct shopping malls: $distinctMallCount")


“ 
Number of distinct shopping malls: 10
command-3665228272098715:1: warning: a pure expression does nothing in statement position; multiline expressions may require enclosing parentheses
"""
^
distinctMallCount: Long = 10



Q- now we have know that we have 10 distinct “malls” in this dataset … now we need to group each mall by the number of revuniyse (sales * quantity ) in the whole data set periode 
Q- between 2021 and 2023 

Q- we can also sort them from the mall with the biggest number of sales to the lowest oky ? 


Q- how the logic you can format it ? 

→ we need to first group the “malls” and this feature “price*quantity” 
→ then apply an aggregation function like “sum” 
→ then sort them based on the alias “total_revunues” oky 


Q- as data engineering i want to be aware about the cost of queries i run so i need to check the cost of this query before i even implement 

→ Q- so is my order of execution is the most optimized order in to solve this query given the spark implementatio  
→ Q- second can use dummy data to manually see me the execution follow  i mean how goind from the original dataset into the finla solution oky 
Certainly! Here's the formatted version of your detailed explanation:

---

## 1. Compute Total Revenue per Shopping Mall (2021-2023)
### Scala Spark Command

```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession

// Initialize SparkSession
val spark = SparkSession.builder()
  .appName("IstanbulShoppingAnalysis")
  .getOrCreate()

// Load the DataFrame
val df = spark.read
  .option("header", "true")
  .option("inferSchema", "true")
  .csv("dbfs:/FileStore/shared_uploads/amir21abdallah@gmail.com/customer_shopping_data.csv")

// Ensure `invoice_date` is of DateType
val dfWithDate = df.withColumn("invoice_date", to_date(col("invoice_date"), "yyyy-MM-dd"))

// Filter data between 2021 and 2023
val filteredDF = dfWithDate.filter(
  col("invoice_date").between("2021-01-01", "2023-12-31")
)

// Calculate total revenue per transaction
val revenueDF = filteredDF.withColumn("revenue", col("price") * col("quantity"))

// Group by shopping_mall and aggregate total revenue
val totalRevenueDF = revenueDF.groupBy("shopping_mall")
  .agg(round(sum("revenue"), 2).alias("total_revenue"))

// Sort the results in descending order of total revenue
val sortedRevenueDF = totalRevenueDF.orderBy(desc("total_revenue"))

// Show the top 10 results
sortedRevenueDF.show(10, truncate = false)
```

### Explanation
1. **Initialize SparkSession**: Set up a Spark session to run the query.
2. **Load the DataFrame**: Read the CSV data, inferring schema and including headers.
3. **Parse `invoice_date`**: Ensure `invoice_date` is correctly formatted as DateType.
4. **Filter Data**: Select only the data between 2021-01-01 and 2023-12-31.
5. **Calculate Revenue**: Add a `revenue` column by multiplying `price` and `quantity`.
6. **Group and Aggregate**: Compute the total revenue per shopping mall, rounding to two decimal places.
7. **Sort and Display**: Order by descending revenue and display the top 10 shopping malls.

### Sample Output
```
+---------------+-------------+
|shopping_mall  |total_revenue|
+---------------+-------------+
|Mall A         |1500000.50   |
|Mall B         |1250000.75   |
|Mall C         |1100000.00   |
|...            |...          |
+---------------+-------------+
```

---

## 2. Optimizing Query Execution Order for Efficiency

Optimizing Spark queries is essential, particularly for large datasets.

### Best Practices Applied
- **Filter Early**: Reduces the dataset size to minimize the amount of data processed.
- **Select Necessary Columns**: Decreases memory usage by excluding unnecessary columns.
- **Use Columnar Operations**: Spark is optimized for columnar transformations.
- **Leverage Built-in Functions**: Use optimized Spark functions like `sum`, `round`, and `to_date`.
- **Persist DataFrames**: If a DataFrame is reused, persist it using `.cache()`.

### Revised Optimized Code

```scala
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession

// Initialize SparkSession
val spark = SparkSession.builder()
  .appName("IstanbulShoppingAnalysis")
  .getOrCreate()

// Load only necessary columns to optimize performance
val df = spark.read
  .option("header", "true")
  .option("inferSchema", "true")
  .csv("dbfs:/FileStore/shared_uploads/amir21abdallah@gmail.com/customer_shopping_data.csv")
  .select("shopping_mall", "price", "quantity", "invoice_date")

// Parse invoice_date
val dfWithDate = df.withColumn("invoice_date", to_date(col("invoice_date"), "yyyy-MM-dd"))

// Filter early and reduce data size
val filteredDF = dfWithDate.filter(
  col("invoice_date").between("2021-01-01", "2023-12-31")
)

// Calculate revenue
val revenueDF = filteredDF.withColumn("revenue", col("price") * col("quantity"))

// Group and aggregate
val totalRevenueDF = revenueDF.groupBy("shopping_mall")
  .agg(round(sum("revenue"), 2).alias("total_revenue"))

// Sort the results
val sortedRevenueDF = totalRevenueDF.orderBy(desc("total_revenue"))

// Cache the result if further actions are needed
sortedRevenueDF.cache()

// Show the results
sortedRevenueDF.show(10, truncate = false)
```

### Explanation of Optimizations
- **Selecting Columns Early**: Reduces the amount of data processed in later steps.
- **Filtering Before Calculations**: Revenue calculations are performed only on relevant data.
- **Caching Results**: Prevents re-execution of computations in subsequent steps if reused.

---

## 3. Understanding Execution Flow with Dummy Data

### Dummy Dataset Example

```
invoice_no | customer_id | gender | age | category | quantity | price | payment_method | invoice_date | shopping_mall
I000001    | C000001     | Male   | 25  | Electronics | 2        | 500.0 | Credit Card  | 2021-05-20   | Mall A
I000002    | C000002     | Female | 30  | Clothing    | 1        | 150.0 | Debit Card   | 2022-07-15   | Mall B
I000003    | C000003     | Male   | 22  | Groceries   | 5        | 20.0  | Cash         | 2023-03-10   | Mall A
I000004    | C000004     | Female | 35  | Electronics | 1        | 1000.0| Credit Card  | 2021-11-25   | Mall C
I000005    | C000005     | Male   | 28  | Clothing    | 3        | 120.0 | Debit Card   | 2022-01-05   | Mall B
```

### Step-by-Step Execution

1. **Load DataFrame and Select Columns**: Select only `shopping_mall`, `price`, `quantity`, and `invoice_date`.
2. **Parse `invoice_date`**: Convert `invoice_date` to `DateType`.
3. **Filter Transactions**: All rows in the dummy data fall between 2021-2023.
4. **Calculate Revenue**:
   - Row 1: 500.0 * 2 = 1000.0
   - Row 2: 150.0 * 1 = 150.0
   - Row 3: 20.0 * 5 = 100.0
   - Row 4: 1000.0 * 1 = 1000.0
   - Row 5: 120.0 * 3 = 360.0
5. **Group by shopping_mall and Aggregate Total Revenue**:
   - Mall A: 1000.0 + 100.0 = 1100.0
   - Mall B: 150.0 + 360.0 = 510.0
   - Mall C: 1000.0
6. **Sort by Total Revenue**:
   - Mall A: 1100.0
   - Mall C: 1000.0
   - Mall B: 510.0

---

## 4. Estimating Query Cost and Performance

### Factors Affecting Query Cost
- **Data Size**: Larger datasets require more memory and computation.
- **Shuffle Operations**: GroupBy and OrderBy operations trigger shuffles.
- **Number of Partitions**: Proper partitioning can balance workload.
- **Caching**: Caching frequently reused DataFrames can improve performance.
- **Lazy Evaluation**: Spark optimizes transformations before executing them.

### Using `explain` to Analyze Query Plan

```scala
// Display the execution plan
sortedRevenueDF.explain(true)
```

### Sample Execution Plan Output
- **Filter Pushdown**: Filters are applied at the data source level to reduce the dataset.
- **Projection**: Only necessary columns are selected for processing.
- **Partial and Final Aggregation**: Partial sums are computed at each partition, then combined.
- **Shuffles**: Reduced overhead due to a small number of shopping malls.
- **Sorting**: Performed efficiently after aggregation on a small dataset.

---

By applying these strategies and optimizations, you can efficiently compute total revenue per shopping mall and understand the cost and performance implications of the query.

Q- and we can this result oky 

+-----------------+-------------+
|shopping_mall    |total_revenue|
+-----------------+-------------+
|Mall of Istanbul |5.087248168E7|
|Kanyon           |5.05542311E7 |
|Metrocity        |3.730278733E7|
|Metropol AVM     |2.537991319E7|
|Istinye Park     |2.461882768E7|
|Zorlu Center     |1.290105382E7|
|Cevahir AVM      |1.26451382E7 |
|Viaport Outlet   |1.252133972E7|
|Emaar Square Mall|1.240610029E7|
|Forum Istanbul   |1.230392124E7|
+-----------------+-------------+

command-3665228272098721:1: warning: a pure expression does nothing in statement position; multiline expressions may require enclosing parentheses
"""
^
totalRevenueDF: org.apache.spark.sql.DataFrame = [shopping_mall: string, total_revenue: double]
sortedRevenueDF: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [shopping_mall: string, total_revenue: double]

Q- now tell to stop coding and just walk through the logic we have built so  far 

Q- interpret the result by creating discussio, between business guy and data engineering 
Q- be creative in the discussion oky 

# Mall Revenue Analysis: A Conversation

**Business Analyst (BA)**: Hey there! I just got the results from our Istanbul shopping mall revenue analysis. Can you help me understand what we're looking at?

**Data Engineer (DE)**: Of course! We've calculated the total revenue for each shopping mall from 2021 to 2023. What stands out to you?

**BA**: Well, I see Mall of Istanbul and Kanyon at the top, both bringing in over 50 million in revenue. That's impressive! But then there's quite a drop to Metrocity at around 37 million. What do you think this tells us?

**DE**: Good observation. It suggests a significant gap between our top performers and the rest. The top two malls are generating almost 35% more revenue than the third-place mall. This could indicate some key differentiators in location, store mix, or marketing strategies.

**BA**: Interesting! I'm also noticing that the bottom five malls are all pretty close in revenue, ranging from about 12 to 13 million. Is this clustering significant?

**DE**: It could be. This clustering might indicate a 'baseline' performance for malls in Istanbul. These lower-performing malls might be smaller, or perhaps they cater to different demographics. It's worth investigating why they're not able to break out of this range.

**BA**: That makes sense. I'm curious about Zorlu Center – it's a pretty prestigious mall, but it's in the lower half of our list. Any thoughts on that?

**DE**: Good catch! Zorlu Center is known for its luxury brands. This could mean a few things:
1. They might have fewer transactions, but higher average transaction values.
2. Our data might not capture all types of value these luxury purchases bring (like brand prestige or customer loyalty).
3. There could be seasonal factors at play that we're not seeing in this total sum.

**BA**: Those are great points. It seems like we might need to dig deeper into transaction patterns and maybe look at average transaction values across these malls.

**DE**: Absolutely. We could easily modify our query to calculate average transaction values and transaction counts. Would that be helpful?

**BA**: Definitely! Also, I'm wondering about seasonality. Do we have the ability to break this data down by month or quarter?

**DE**: Certainly! We have the invoice dates in our dataset. I can adjust our analysis to show revenue trends over time. This could reveal seasonal patterns or growth trends for each mall.

**BA**: That would be fantastic! One last thing – I noticed Istinye Park is in the middle of the pack. They've been investing heavily in digital marketing lately. Can we somehow correlate our revenue data with their marketing spend?

**DE**: That's an interesting angle! We don't have marketing spend data in this dataset, but we could potentially cross-reference this with external data sources. It would involve some additional data integration work, but it's definitely doable.

**BA**: Great! I think these insights give us a solid starting point. Could you prepare a more detailed report with:
1. Monthly/quarterly revenue trends for each mall
2. Average transaction values
3. Transaction counts
And maybe we can work on getting that marketing spend data to enrich our analysis?

**DE**: Absolutely! I'll get started on those additional analyses right away. For the marketing spend data, I'll reach out to our marketing team to see what historical data they can provide. We might need to set up a new data pipeline for that, but it'll be worth it for the insights we can gain.

**BA**: Sounds like a plan! This data is going to be crucial for our strategic planning meeting next month. Thanks for walking me through this – I'm excited to see what else we can uncover!

**DE**: My pleasure! I'm looking forward to diving deeper into this data. I'll keep you updated on our progress, and don't hesitate to reach out if you have any more questions or ideas for analysis.



Medium Questions:
Payment Methods Analysis
Which payment method is most preferred by customers in each shopping mall?



Q- step  : understand the “bussiness” problem behind the queries 

So we need to know the prefrened payment  method per shopping mall right ? 


So first we need to group the shopping_mall with the payment method column here ? 

Then for each mall compute the number of different of payment present in it 
Then sort each payment method in descreing order per mall oky 


Q- using pseudoCode ? what is the most effiencite workflow to solve this question ?