Data Lineage and Impact Analysis: Understanding and Dealing with Data Dependencies in Data Pipelines by Fidel V.

Hello Everyone;

The Mad Scientist Fidel V. will bring you an overview and scenario how important for maintaining data quality and understanding the flow of data...

Overview

Data Lineage refers to the life cycle of data: its origins, where it moves over time, and what happens to it as it is transformed. Understanding data lineage helps organizations ensure data quality, trace errors back to the root cause, and understand the impact of changes in data sources on downstream applications.

Impact Analysis involves understanding the consequences of changes in data structures, transformations, or processes within a data pipeline. This analysis helps in identifying the effects of changes and mitigating risks associated with those changes.

The Mad Scientist Scenario:

Consider a retail company that collects sales data from various sources (POS systems, e-commerce platforms, etc.), processes it through multiple stages, and generates reports for business analysis. Here’s how data lineage and impact analysis might be used:

1. Data Ingestion: Raw sales data is collected from different sources and ingested into a data lake.
2. Data Transformation: The raw data is cleaned, transformed, and enriched.
3. Data Storage: The transformed data is stored in a data warehouse.
4. Data Analysis: Business intelligence tools generate reports and dashboards from the data warehouse.

Changes in the data sources or transformations can have significant impacts on the reports generated. Data lineage and impact analysis help in tracking the data flow and understanding these impacts.

Implementation

Phase 1: Setting Up the Environment

Goal: Establish the necessary tools and environment for data processing.

• Tool Selection: We choose Databricks, PySpark, and AWS for our data processing and storage needs. Databricks provides a collaborative environment for data engineers, and PySpark allows for large-scale data processing. AWS offers robust cloud infrastructure for data storage and compute resources.
• Delta Lake: We use Delta Lake as it offers ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.

Code:

python

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("DataLineageExample").getOrCreate()        

Phase 2: Data Ingestion

Goal: Collect raw data from various sources and ingest it into a centralized storage system.

• Data Sources: Sales data from physical stores and online platforms.
• DataFrame Creation: Convert raw data into Spark DataFrames for further processing.
• Storage in Delta Lake: Store the ingested data in Delta Lake, providing a foundation for reliable and scalable data storage..

python

# Sample data from different sources
data_source_1 = [("2024-01-01", "Store A", 1000.0), ("2024-01-02", "Store B", 1500.0)]
data_source_2 = [("2024-01-01", "Online", 2000.0), ("2024-01-02", "Online", 2500.0)]

# Creating DataFrames
df1 = spark.createDataFrame(data_source_1, ["date", "source", "sales"])
df2 = spark.createDataFrame(data_source_2, ["date", "source", "sales"])

# Writing to Delta Lake
df1.write.format("delta").mode("append").save("/mnt/delta/sales")
df2.write.format("delta").mode("append").save("/mnt/delta/sales")

        

Phase 3: Data Transformation

Goal: Clean, transform, and enrich the raw data to make it suitable for analysis.

• Reading Data: Read the ingested raw data from Delta Lake.
• Transformation Logic: Apply business logic to transform the data. In this case, we increase the sales figures by 10%.
• Storing Transformed Data: Save the transformed data back to Delta Lake.

Code:

python

from pyspark.sql.functions import col

# Reading from Delta Lake
df = spark.read.format("delta").load("/mnt/delta/sales")

# Transforming data
transformed_df = df.withColumn("sales", col("sales") * 1.1)  # Applying a 10% increase for example

# Writing transformed data back to Delta Lake
transformed_df.write.format("delta").mode("overwrite").save("/mnt/delta/transformed_sales")
        

Transforms the data by applying a 10% increase to sales figures and writes the transformed data back to Delta Lake.

Phase 4: Data Lineage Tracking

Goal: Track the data flow and transformations to maintain data quality and traceability.

• Delta Lake History: Utilize Delta Lake’s history feature to track changes and transformations applied to the data.
• Understanding Data Flow: By examining the history, we can understand the sequence of transformations and identify potential issues.

Code:

python

# Viewing the history of the Delta table
history_df = spark.sql("DESCRIBE HISTORY delta.`/mnt/delta/transformed_sales`")
history_df.show()
        

Displays the history of changes made to the transformed sales data.

Phase 5: Impact Analysis

Goal: Assess the impact of changes in data sources or transformations on downstream applications and reports.

• Identifying Dependencies: List source tables and transformation logic to understand dependencies.
• Simulating Changes: Apply changes to the source data and reprocess transformations to observe impacts.
• Analyzing New Lineage: Check the updated history to understand the impact of changes.

Code:

python

# Identifying dependencies
dependencies = {
    "source_tables": ["/mnt/delta/sales"],
    "transformation": "sales = sales * 1.1"
}

# Checking impact of a change in the source data
changed_data_source_1 = [("2024-01-01", "Store A", 1200.0), ("2024-01-02", "Store B", 1600.0)]
changed_df1 = spark.createDataFrame(changed_data_source_1, ["date", "source", "sales"])
changed_df1.write.format("delta").mode("overwrite").save("/mnt/delta/sales")

# Reprocessing the transformation
df = spark.read.format("delta").load("/mnt/delta/sales")
transformed_df = df.withColumn("sales", col("sales") * 1.1)
transformed_df.write.format("delta").mode("overwrite").save("/mnt/delta/transformed_sales")

# Checking the new lineage
history_df = spark.sql("DESCRIBE HISTORY delta.`/mnt/delta/transformed_sales`")
history_df.show()
        

Simulates a change in the source data, reprocesses the transformation, and examines the new lineage.

Data Analysis Queries

Goal: Use SQL queries to analyze data and understand lineage and transformations.

• Fetching Data: Retrieve raw and transformed sales data.
• Examining History: Analyze the transformation history to understand data lineage.

SQL Queries:

sql

-- Query to get sales data
SELECT * FROM delta.`/mnt/delta/sales`;

-- Query to get transformed sales data
SELECT * FROM delta.`/mnt/delta/transformed_sales`;

-- Query to get history of transformations
DESCRIBE HISTORY delta.`/mnt/delta/transformed_sales`;
        

Fetches raw and transformed sales data, and describes the history of transformations.

Data lineage and impact analysis are crucial for maintaining data quality and understanding the flow of data within data pipelines. By implementing these practices, organizations can ensure that changes in data sources or transformations are effectively managed and that the impact of these changes is understood. This example demonstrates how to use Databricks, PySpark, and Delta Lake to manage data lineage and perform impact analysis in a real-world scenario.

Fidel V (the Mad Scientist)

Project Engineer || Solution Architect

Security ? AI ? Systems ? Cloud ? Software

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