Identifying ETL Requirements

Before you write a single line of code or drag-and-drop a component in an ETL tool, you need to understand exactly what you're trying to achieve. Just like building a house requires a blueprint, building an ETL process requires a clear set of requirements. Skipping this step is a common cause of project delays, budget overruns, and, ultimately, ETL processes that don't deliver the data users actually need.

Identifying requirements involves asking the right questions to understand the data sources, the desired outcome, and the steps needed to get from one to the other. Think of it as gathering intelligence before starting a mission.

Why Spend Time on Requirements?

Taking the time to define requirements upfront offers several significant benefits:

1. Clarity of Purpose: Ensures everyone involved understands the goal of the ETL process and why it's being built.
2. Accurate Design: Guides the design of the extraction, transformation, and loading steps, ensuring the final data structure and content meet the needs.
3. Reduced Rework: Catching misunderstandings or missing information early is far less costly than fixing a completed pipeline later.
4. Realistic Estimates: Helps in estimating the effort, time, and resources needed for development and maintenance.
5. Informed Tool Selection: The requirements (like data volume, complexity of transformations, required speed) influence the choice of appropriate ETL tools or technologies.
6. Testability: Clear requirements make it easier to define test cases to verify the ETL process works correctly.

Essential Questions for Gathering Requirements

To build a solid foundation for your ETL process, you need to gather information by asking specific questions. Here are the fundamental areas to investigate:

1. What is the Business Goal?

This is the "why." What problem are you solving or what opportunity are you enabling with this data?

• Example Questions:
  • What business decision will this data support? (e.g., "We need to see daily sales totals by region to adjust marketing.")
  • What report, dashboard, or application will use this data? (e.g., "This data feeds the main sales performance dashboard.")
  • What specific questions should the final data be able to answer? (e.g., "Which products have the highest return rates?")

Understanding the goal provides context for all subsequent decisions.

2. Where is the Data Coming From? (Sources)

Identify every source system that contains the data you need.

• Example Questions:
  • What systems hold the raw data? (e.g., "Our online store's PostgreSQL database," "Marketing uses Salesforce," "Inventory data is in daily CSV files on an FTP server.")
  • How can we access each source? (e.g., Database credentials, API keys, file paths).
  • What format is the data in? (e.g., Relational tables, JSON objects from an API, fixed-width text files).
  • Do we have permission to access this data?

3. What Specific Data is Needed? (Scope)

Often, you don't need all the data from a source. Define the exact scope.

• Example Questions:
  • Which specific tables, files, or API endpoints are relevant?
  • Which columns or fields within those sources are required?
  • Are there any filter conditions? (e.g., "Only 'completed' orders," "Customers in North America," "Data from the last 12 months.")

4. How Does the Data Need to Change? (Transformations)

This defines the "T" in ETL. How must the raw data be modified to be useful in the target system?

• Example Questions:
  • Cleaning: How should missing values be handled (e.g., remove row, fill with default, estimate)? How should errors or outliers be treated?
  • Formatting: Do dates, numbers, or addresses need standardization? (e.g., "Convert all dates to YYYY-MM-DD format.")
  • Structuring: Do datasets need to be joined together? (e.g., "Combine customer data with order data.") Does data need to be split? (e.g., "Separate address components into street, city, state.")
  • Enrichment: Do we need to add information? (e.g., "Calculate profit margin," "Look up product category based on product ID.")
  • Aggregation: Does data need to be summarized? (e.g., "Calculate total sales per day," "Count unique customers per month.")

5. Where Does the Data Need to Go? (Target)

Specify the destination for the processed data.

• Example Questions:
  • What is the target system? (e.g., "A Snowflake data warehouse," "A specific table in a MySQL database," "Files in Amazon S3.")
  • What is the desired structure (schema) in the target? (e.g., Table names, column names, data types). If the target schema doesn't exist, defining it is part of the requirements.
  • How should data be loaded? (e.g., Overwrite existing data completely (full load)? Add new data and update existing records (incremental load)?)

6. How Often is the Data Needed? (Frequency & Latency)

Determine the timing requirements for the ETL process.

• Example Questions:
  • How frequently does the ETL process need to run? (e.g., Hourly, daily at 3 AM, weekly on Sunday, triggered by an event).
  • What is the acceptable delay (latency) between an event happening in the source system and the data being available in the target? (e.g., "Data must be available within 1 hour," "Next-day availability is fine.")

7. How Much Data Are We Talking About? (Volume & Velocity)

Estimate the size and speed of data flow.

• Example Questions:
  • What is the approximate size of the initial data load? (e.g., Megabytes, Gigabytes, Terabytes).
  • How much new data is generated per day/hour/week? (e.g., "About 10,000 new records per day.")
  • Is the data arriving in batches or as a continuous stream?

These answers influence infrastructure choices and processing strategies.

8. What Defines Correct Data? (Data Quality)

Set expectations for the accuracy and completeness of the data.

• Example Questions:
  • What rules must the data satisfy to be considered valid? (e.g., "Order total must be positive," "Email address must contain '@'," "Product ID must exist in the products table.")
  • How should data that fails validation be handled? (e.g., Reject the record, load it to an error table, flag it for review, attempt correction).

9. Who Needs Access and What Are the Rules? (Security & Compliance)

Consider data protection and user access.

• Example Questions:
  • Are there any sensitive data elements (personally identifiable information - PII)?
  • Are there regulatory requirements (like GDPR, HIPAA, CCPA) that apply?
  • Who are the end-users of the target data, and what permissions do they need?

Documenting Your Findings

Don't just ask the questions; write down the answers! Requirements should be documented clearly and shared with all stakeholders (business users, developers, analysts). This document becomes the reference point for development and testing. It doesn't need to be overly formal for simple projects; a checklist or a structured document often suffices.

The diagram below illustrates how these different areas of inquiry feed into the creation of a requirements document.

This diagram shows the key question categories involved in gathering information, all contributing to the final ETL requirements document.

Simple Example: Daily Sales Summary

Let's apply these questions to a basic scenario: Create a daily summary report of total sales amount per product category.

• Goal: Provide a daily snapshot for the sales manager to see which product categories are performing best.
• Sources: A single CSV file generated daily named orders_YYYYMMDD.csv containing order_id, product_id, quantity, price_per_item. A separate products.csv file (updated infrequently) containing product_id, category.
• Scope: All records from the daily orders file. product_id and category from the products file.
• Transformations:
  • Calculate total_sale for each order line (quantity * price_per_item).
  • Join the daily orders data with the products data on product_id to get the category.
  • Aggregate the total_sale by category.
  • Format the output with columns category and total_sales_amount.
• Target: A new CSV file named daily_category_summary_YYYYMMDD.csv.
• Frequency: Run once per day after the daily orders_YYYYMMDD.csv file arrives.
• Volume: Orders file is approx 5MB daily (~50,000 rows). Products file is small (1MB).
• Quality: quantity and price_per_item must be positive numbers. product_id in the orders file must exist in the products file (ignore order lines with unknown products for this simple case).
• Security: No PII involved. Sales manager needs read access to the output file.

Even for this simple case, answering these questions clarifies the task significantly. As ETL processes become more complex, involving more sources, intricate transformations, and stricter performance requirements, this structured approach becomes indispensable. Investing time in understanding and documenting requirements is the first, essential step toward building effective and reliable ETL pipelines.