Capstone Project - PySpark - Financial Transaction ETL Pipeline at Sun Life

[akash-coded]

Advanced ETL Pipeline in PySpark for Sun Life

Project Overview

As a data engineer, your task is to build a comprehensive ETL pipeline to process diverse datasets about customers' financial and demographic insights. You'll integrate data from open sources such as Kaggle and other internet databases, cleanse and transform data, optimize the ETL process, and store the results for further use.

Mini-Project Structure

1. Setup and Initialization
2. Data Ingestion from Multiple Sources
3. Advanced Data Transformation and Enrichment
4. Optimization for Performance and Efficiency
5. Data Storage and Reporting

We'll use PySpark's advanced expressions, APIs, and optimization techniques throughout this project.

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Step 1: Setup and Initialization

1. Install Required Packages

   !pip install pyspark
   !pip install mysql-connector-python

2. Create a Spark Session with Advanced Configuration

   from pyspark.sql import SparkSession
   
   spark = SparkSession.builder \
       .appName("SunLife_Advanced_ETL_Pipeline") \
       .config("spark.sql.shuffle.partitions", 300) \
       .config("spark.driver.memory", "8g") \
       .config("spark.sql.broadcastTimeout", "1200") \
       .getOrCreate()

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Step 2: Data Ingestion from Multiple Sources

1. Load JSON Data (Financial Transactions)

   # Load transaction data from a public JSON dataset on Kaggle
   transactions_df = spark.read.json("path_to_kaggle_json_transactions")

   Hint: Ensure the Kaggle dataset is properly downloaded and accessible in your environment.

2. Load CSV Data (Customer Demographics)

   # Load customer demographic data from a public CSV dataset
   demographics_df = spark.read.csv("path_to_online_csv_demographics", header=True, inferSchema=True)

   Hint: Use public datasets from open sources like Kaggle or data.gov.

3. Load Data from MySQL Database (Internal Customer Info)

   jdbc_url = "jdbc:mysql://your_mysql_host:3306/sunlife"
   connection_properties = {
       "user": "your_username",
       "password": "your_password",
       "driver": "com.mysql.cj.jdbc.Driver"
   }
   
   customer_info_df = spark.read.jdbc(url=jdbc_url, table="customer_info", properties=connection_properties)

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Step 3: Advanced Data Transformation and Enrichment

1. Data Cleansing with Advanced Filters

   from pyspark.sql.functions import col, expr, trim, when
   
   # Cleanse data by trimming strings and filtering invalid records
   clean_transactions_df = transactions_df \
       .withColumn("transaction_id", trim(col("transaction_id"))) \
       .filter(col("status") == "success") \
       .filter(expr("amount > 0 AND length(transaction_id) > 0"))

   Hint: Use expr() for complex filter expressions.

2. Enrich Data with Joins and Conditional Expressions

   # Use conditional expressions to enrich data
   enriched_df = clean_transactions_df.join(customer_info_df, "customer_id")
   
   # Add a column indicating high-value customers
   enriched_df = enriched_df.withColumn("high_value_customer", when(col("total_spend") > 5000, "Yes").otherwise("No"))

   Hint: Use when and otherwise for conditional logic.

3. Calculate Aggregations with Expressions

   from pyspark.sql.functions import sum, avg, count, desc
   
   # Calculate total spend and average transaction for each customer
   customer_aggregates_df = enriched_df.groupBy("customer_id") \
       .agg(
           expr("sum(amount) as total_spend"),
           avg("amount").alias("average_spend"),
           count("transaction_id").alias("transaction_count")
       ) \
       .orderBy(desc("total_spend"))

   Hint: Combine expr() with aggregation functions for flexibility.

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Step 4: Optimization for Performance and Efficiency

1. Repartition and Cache Data

   # Repartition data to optimize join and filter operations
   partitioned_enriched_df = enriched_df.repartition("customer_id")
   partitioned_enriched_df.cache()

   Hint: Caching is useful when DataFrames are used multiple times.

   # Bucket tables by customer_id for optimized joins
   customer_aggregates_df.write \
       .bucketBy(20, "customer_id") \
       .sortBy("total_spend") \
       .saveAsTable("bucketed_customer_aggregates")

Hint: Bucketing helps improve performance for specific queries.

3. Use Broadcast Joins for Small Lookup Tables

   # Assume demographics_df is small enough to broadcast
   enriched_with_demographics_df = enriched_df.join(
       spark.broadcast(demographics_df), "customer_id"
   )

   Hint: Use broadcast joins to avoid shuffling large tables.

4. Optimize with Catalyst Hints and Query Explanations

   from pyspark.sql.functions import hint
   
   # Apply optimizer hints and explain query plans
   optimized_df = enriched_with_demographics_df.hint("SORTMERGE")
   optimized_df.explain(True)

   Hint: Analyze and adjust query plans using .explain().

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Step 5: Data Storage and Reporting

1. Write Processed Data to Parquet for Efficient Storage

   # Write the enriched and aggregated data to Parquet format for storage
   partitioned_enriched_df.write.mode("overwrite").parquet("/path/to/output/enriched_data.parquet")

2. Write Summary Statistics to MySQL

   # Write summary statistics back to MySQL for further analysis
   customer_aggregates_df.write.jdbc(
       url=jdbc_url,
       table="customer_aggregates_summary",
       mode="overwrite",
       properties=connection_properties
   )

   Hint: Use mode="overwrite" to refresh tables with updated data.

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Conclusion

In this comprehensive mini-project, you've constructed an advanced ETL pipeline that handles large and varied datasets. By efficiently using PySpark operations such as expr, broadcast, bucketBy, and cache, you’ve optimized data processing and addressed real-world challenges.

Additional Suggestions

• Explore Real-time Data: Integrate real-time data processing using Spark Streaming for timely insights.
• Machine Learning Integration: Use Spark MLlib to build predictive models on customer data.
• Monitoring and Logging: Implement logging and monitoring to ensure robust pipeline performance.

By completing these tasks, you've gained a deeper understanding of how to manage complex data engineering scenarios effectively with PySpark, meeting diverse business needs and ensuring scalability.

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Let’s add more PySpark concepts to further enhance this mini-project, including advanced use of window functions, working with Apache Arrow for optimized data serialization, leveraging user-defined functions (UDFs), and exploring DataFrame APIs for complex transformations.

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Step 6: Use Advanced PySpark Concepts

1. Window Functions for Time-Based Calculations

   Use Spark's window functions to calculate running totals or other statistics over time-based windows.

   from pyspark.sql.window import Window
   from pyspark.sql.functions import row_number, desc
   
   # Define a window specification for each customer, ordered by transaction date
   window_spec = Window.partitionBy("customer_id").orderBy(desc("transaction_date"))
   
   # Add a row number to identify the most recent transactions per customer
   windowed_df = enriched_with_demographics_df.withColumn("row_num", row_number().over(window_spec))
   
   # Filter to get the latest transaction per customer
   latest_transactions_df = windowed_df.filter(col("row_num") == 1)
   latest_transactions_df.show()

   Hint: Window functions like row_number(), rank(), and dense_rank() allow performing calculations across a "window" of data.

2. Optimize with Apache Arrow for Efficient Serialization

   Use Apache Arrow to improve the efficiency of data transfers between Spark and Pandas.

   # Enable Arrow-based columnar data transfers
   spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
   
   # Convert a Spark DataFrame to a Pandas DataFrame using Arrow
   pandas_df = latest_transactions_df.toPandas()

   Hint: Apache Arrow can greatly enhance performance, especially when operating on large datasets with Pandas UDFs.

3. Apply User-Defined Functions (UDFs) for Custom Logic

   Implement UDFs to apply custom transformations or logic that aren’t directly supported by built-in functions.

   from pyspark.sql.functions import udf
   from pyspark.sql.types import DoubleType
   
   # Define a UDF to apply a discount rate
   def apply_discount(amount):
       return amount * 0.90  # 10% discount
   
   discount_udf = udf(apply_discount, DoubleType())
   
   # Apply UDF to calculate discounted amounts
   discount_df = enriched_with_demographics_df.withColumn("discounted_amount", discount_udf(col("amount")))
   discount_df.show()

   Hint: Use UDFs wisely, as they can impact performance. Always prefer using built-in functions if possible.

4. Leveraging DataFrame APIs for Complex Transformations

   Utilize PySpark’s transformations for complex ETL logic, such as pivoting data for cross-tab reports.

   # Pivot transaction amounts by month and customer
   pivoted_df = enriched_with_demographics_df.groupBy("customer_id").pivot("transaction_month").sum("amount")
   pivoted_df.show()

   Hint: Pivot operations can help reshape data for analysis, though they may introduce complexity.

5. Utilize Data Skew Handling Techniques

   • Identify Data Skew: Use DataFrame operations to explore and identify potential skew in the data distribution.
   • Apply Salting: Introduce a salt column to distribute records more evenly across partitions for joins or aggregations.

   from pyspark.sql.functions import rand
   
   # Add a salt column
   enriched_with_skew_handling = enriched_with_demographics_df.withColumn("salt", (rand() * 10).cast("int"))
   
   # Repartition using the salting technique
   skew_handling_df = enriched_with_skew_handling.repartition(12, "customer_id", "salt")
   skew_handling_df.show()

   Hint: Salting can alleviate skew but may require post-processing to remove salts after operations.

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Conclusion and Next Steps

By completing the enhanced steps above, you explored and utilized more advanced PySpark concepts, demonstrating the creation of a comprehensive ETL pipeline. By managing complex transformations, integrating efficient data handling with Apache Arrow, and customizing data processing with UDFs, you've tackled real-world data engineering challenges.

Additional Exploration

• Integrate Streaming Data: Use Spark Streaming to handle real-time data and integrate continuous ETL processes.
• Distributed Machine Learning: Experiment with Spark MLlib to apply machine learning algorithms to predict customer behavior.
• Graph Processing: Explore GraphFrames for scenarios involving customer relationship analysis using graph algorithms.

These exercises further prepare you for handling complex, large-scale data environments and driving more value through better data processing capabilities with PySpark.