ETL Data Mapping: Process, Methods, Use Cases, Benefits

CEO, Portable

ETL is vital to make sense of your organization's big data.

But when you're importing data from various sources, not every source uses the same data structures and metadata.

Even tiny differences can make large datasets unreliable or even unusable.

Data mapping solves this problem. It ensures the data from every source is standardized and ready to be used in any ETL data pipeline.

In this article, we'll explain how ETL data mapping works, the types of ETL mapping, and the benefits and use cases.

What is ETL Mapping?

  • ETL data mapping is the step in the ETL process that relates data fields across two databases.
  • The goal of ETL mapping is to integrate one data set into another. It makes your organization's data more structured, accessible, and standardized, making it easy to analyze.
  • For example, data mapping could help map the "First Name" field in an old system to the "Given Name" field in a system or connect the "Phone" and "Home Phone" fields across different databases.

What is ETL, and how does it work?

  • ETL is short for Extract, Transform, and Load.

  • It's the process of combining data from multiple sources into a single database or a data warehouse.

  • ETL has three steps:

  • Extract: Data is extracted from one or more different sources such as databases, flat files, or applications.

  • Transform: The extracted data is then transformed or modified to meet the requirements of the target system. Examples include enforcing validation, changing data formats, and removing duplicates.

  • Load: Lastly, the transformed data is loaded into the target database, which usually is a data warehouse or data lake.

How data mapping fits into the ETL process

  • The ETL mapping process involves cataloging data elements from the source system to the corresponding data elements in the target system. It also involves defining how source data should be transformed to meet the target system requirements.
  • ETL mapping includes how rules on how the data should be converted, cleaned, filtered, and validated.
  • The ETL mapping process is crucial for ensuring that data moved into the target system is accurate, complete, and consistent with the data in the source systems. It also identifies potential data quality or synchronization issues, allowing developers to fix them before loading the data into the target system. 
  • In short, ETL mapping is crucial to ensure the final data flow creates usable data. It ensures you can rely on the resulting data for analysis and decision-making.

How the Data Mapping Process Works: 5 Steps

Data mapping uses several steps to integrate records into a new database successfully.

1. Identify the Source Data

  • The first step in ETL mapping is identifying data elements to be extracted from the source system. Data can come from long-tail data sources, from real-time API calls from a CRM to an on-premise SQL server to a flat file like an Excel spreadsheet.
  • The data team must analyze the source data and understand what to import and discard.
  • The team must understand the source system schema, data types, and relationships between different tables.
  • This will provide the information necessary to create the definitions and structure of how the data will be imported.

2. Create a Data Map

  • Once the data to be transferred has been defined, it must be mapped to the attributes of the destination system.
  • This step connects the source and target data metadata, conventions, format, and more.
  • This includes transformation rules. If a given field requires transformation, the transformation rule is coded.
  • The data team must create a map that defines how source data must be transformed to integrate seamlessly with the target system.

3. Test the Transfer Logic

  • The next step is verifying the transfer map works using a test and source systems. The goal is to ensure data is mapped correctly before implementing the system live.
  • The data team should adjust the mapping rules and logic if there are any errors.
  • Consider testing all data types, including those with unusual or incorrect formats.

4. Deploy the Migration or Transformation

  • Once the logic has been tested and verified, schedule the actual implementation of the data.
  • Stand by, especially during the first deployment, to correct unforeseen data quality issues.

5. Maintain and Update

  • For data mapping you'll be using continuously, you'll need to maintain and update the logic and transformation.
  • If new data sources or fields are added, update the mapping to reflect that.
  • Ensure data sources continue to export the same data schema and correct the system based on any system changes.

Types of Data Mapping in ETL

There are three main types of data mapping.

Manual Data Mapping

  • A developer codes rules to connect or load data from the data source to the destination warehouse.
  • Common tools include graphical mapping features with a drag-and-drop interface.
  • However, given the staggering amount of data that most businesses nowadays have, this approach is challenging to implement in the modern age.
  • Graphical representations can quickly become overwhelming and hard to work with.

Semi-Automated Data Mapping

  • Also known as schema mapping, this technique combines manual data mapping with automated tools to map data faster and easier.
  • It uses software or APIs to assist in identifying and linking data elements between different data sources.
  • In this method, a human analyst is still needed. They play a crucial role in reviewing and validating mapping results generated by the software. 

Automated Data Mapping

  • Here, the software handles the entire mapping process. It'll automatically identify and extract data elements from the source system and map them to corresponding data elements in the target system. 
  • These tools also rely on machine learning for data analysis and to constantly improve data models as sources change.
  • Human analysts have a limited role and must only know how to interact with the tool, not the source applications or target destinations.

Benefits of ETL Mapping

Improved data quality

  • More accurate data mapping can lead to more reliable data transfers.
  • These minor errors can have a ripple effect across an entire organization, leading to unusable data.
  • For example, data mapping can ensure similar columns across two databases, like "first_name" and "name_first" are merged instead of maintained separately.

Faster data integration

  • ETL mapping, especially the automated kind, can speed up the merging and integration of data. 
  • Mapping can automate this process and work in real-time, resulting in faster results and more accurate data than manual migrations.

Cost savings

  • ETL mapping helps companies seamlessly integrate data from various sources, reducing the need for manual data entry. 
  • It helps identify and correct data errors quickly, reducing the need for manual checking and saving time and costs.
  • It makes adding new data sources and target systems easier, reducing the need for human involvement.

Reduces siloes

  • In most companies, data is siloed across multiple disparate data sources. ETL mapping helps unify and integrate all the sources into a single source, so all your data is in one place.
  • Without mapping, siloes could still exist even in the same destination due to different naming conventions and file formats.

Examples of Data Mapping in ETL

Data mapping tools can work across multiple industries to improve data quality.

  • Healthcare: Mapping patient demographic data from various sources to a standardized format for analysis. For example, calculating patient age at treatment based on birthdate and grouping those ages into defined ranges.
  • Finance: Mapping financial transaction data from different banking systems to a standardized format for aggregation. For example, assigning human-readable categories to vendors based on numerical industry codes.
  • Retail: Mapping and de-duplicating product data from different suppliers. For example, converting product names to standardized SKUs and eliminating duplicate codes.
  • Manufacturing: Mapping machine sensor data to a standardized format for predictive maintenance. For example, converting timestamped readings to estimate future failure dates and replacement timelines.
  • Education: Mapping student performance data from different assessments to a single standard. For instance, standardizing scores from state and local assessments and mapping those to an estimated grade level proficiency.
  • Telecommunications: Mapping customer usage data from different network systems to a standardized billing format. For example, converting usage categories across states to standardized rate plans.
  • Insurance: Mapping insurance claim data to standard categories. For example, converting regional claim types to national codes.
  • Transportation: Mapping logistics data from different shipping providers to a standardized format for tracking. For example, converting kilometers and miles to a single format.
  • Government: Mapping census data from different regions into convertible formats. For instance, relating demographic categories to predetermined population groups.

Read More: ETL Examples: Use Cases & How to Evaluate ETL Tools

Data Mapping Use Cases

Data Migration

  • This data management process moves data from one system to another. It's done as a one-time event. The original source is retired after transferring the data. 
  • Data migration is done for various reasons, like upgrading to a new system, consolidating data from multiple systems, or moving data from on-premises to the cloud.
  • Data mapping is a key step in the ETL process for integrating data since it ensures all data is usable.

Data Integration

  • Data integration is combing data into a single destination, whether a workflow or a data warehouse.
  • This can involve combing data from databases, spreadsheets, and other sources to create a single data set that can be analyzed and used to make decisions. 
  • Data integration tools make it easy to manage complex data. 

Data Transformation

  • Data transformation is converting data from one format or structure to another.
  • This can involve converting data from a legacy system to a new system or converting file formats like CSV, JSON, or XML.
  • For example, if you're converting from a relational database to a non-relational database, data mapping can help you define the correct format.

Data Warehousing

  • Data mapping can help aggregate data, like a data warehouse, into a single source.
  • ETL mapping ensures that the data in the warehouse is correctly formatted, accurate, complete, and integrated.

Business Intelligence

  • Data mapping helps to gather data from separate siloes or divisions in a company and bring them together.
  • Without ETL mapping, business intelligence data would use different formats and be nearly impossible to analyze and use.

Electronic Data Interchange (EDI)

  • Data mapping is a key component when transferring data between different file specifications.
  • It can restructure data into the correct format using pre-built templates, enabling data exchange between ecosystems like Oracle and Microsoft.

The Bottom Line on ETL Mapping

While there are thousands of SaaS applications, most ETL tools only support the most popular ones. There aren't many tools for transferring data from long-tail applications.

That's where Portable comes in. It has hard-to-find ETL connectors that map and transfer data from over 300+ data sources, including e-commerce and marketing tools.

And if you can't find the connector you need, Portable builds it for you. Plus, Portable handles all ongoing maintenance, so you focus on analysis and not creating an integration from scratch.

This way, Portable centralizes source data from hundreds of business applications into your cloud data warehouse (SnowflakeRedshift, or BigQuery) for analytics, automation, or product development. Try it free today!

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