02 - Multiplex - Many to One Mapping

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The multiplex ingestion pattern involves ingesting data from multiple sources into a data platform like Databricks. This pattern is more complex than the singleplex ingestion pattern and is designed to handle diverse and varied data sources, ensuring that data from different origins is integrated and made available for processing, transformation, and analysis.

Key Characteristics

Multiple Data Sources: The pattern deals with ingesting data from various sources, such as databases, file systems, streaming services, APIs, and more.

Complexity: This pattern is more complex than singleplex ingestion due to the need to handle different data formats, structures, and update frequencies.

Integration: Aimed at integrating data from different origins to create a unified view, facilitating comprehensive analysis and decision-making.

Scalability: Designed to scale as the number of data sources and volume of data increase, ensuring efficient and reliable ingestion.

Steps in Multiplex Ingestion

  • Source Identification and Analysis: Identify and analyze all the data sources, understanding their structures, formats, and update frequencies.
  • Data Extraction: Use various tools and techniques to extract data from each source. This could involve SQL queries, API calls, file readers, and streaming connectors.
  • Data Transformation and Normalization: Transform and normalize the data to ensure it fits the target schema and format. This may involve data cleaning, enrichment, deduplication, and harmonization to resolve schema differences.
  • Data Integration: Integrate the data from different sources to create a cohesive dataset. This often involves matching records from different sources and ensuring consistency.
  • Data Loading: Load the integrated and transformed data into the target system, such as Databricks, ensuring it is stored efficiently and is accessible for further processing and analysis.

Monitoring and Maintenance: Implement monitoring to ensure the ingestion process is running smoothly and maintain the process to handle changes in source data or requirements.

Advantages

  • Comprehensive Data Integration: Enables a unified view of data from diverse sources, facilitating comprehensive analysis and decision-making.
  • Flexibility: Capable of handling a wide range of data sources, formats, and update frequencies.
  • Scalability: Designed to scale with increasing data sources and volumes, ensuring reliable ingestion.

Challenges

  • Complexity: Managing and integrating data from multiple sources adds complexity to the ingestion process.
  • Performance Tuning: Requires careful tuning to handle different data sources efficiently.
  • Data Quality: Ensuring data quality and consistency across multiple sources can be challenging.

Use Cases

  • Enterprise Data Warehousing: Integrating data from various departmental databases, external APIs, and other sources into a centralized data warehouse.
  • IoT Data Ingestion: Collecting and integrating data from multiple IoT devices and sensors for real-time analytics and monitoring.
  • Customer 360 Views: Aggregating data from various customer touchpoints, such as CRM systems, transaction databases, and social media, to create a comprehensive view of customer behavior.

Streaming from Multiplex Bronze Table

from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder.appName("MultiplexBronzeStreaming").getOrCreate()

# Define schema for the data (example schema)
schema = "id INT, name STRING, event_type STRING, event_time TIMESTAMP"

# Read streaming data from the multiplex bronze table
bronze_stream = (spark.readStream
                 .format("delta")
                 .option("readChangeData", True)
                 .table("bronze"))

# Create a temporary view for SQL queries
bronze_stream.createOrReplaceTempView("bronze_tmp")

# Example transformation: filtering and selecting data
transformed_stream = spark.sql("""
    SELECT id, name, event_type, event_time
    FROM bronze_tmp
    WHERE event_type = 'purchase'
""")

# Write the transformed data to a silver table
query = (transformed_stream.writeStream
         .format("delta")
         .outputMode("append")
         .option("checkpointLocation", "/path/to/checkpoint/dir")
         .table("silver"))

# Start the streaming query
query.awaitTermination()