What Is ETL Data Mapping And Why Should You Know About It?

In today’s data-driven world, businesses rely solely on data to make informed decisions. For this, they need to efficiently extract, transform, and load (ETL) vast amounts of data. While ETL is an essential process for data migration, data mapping plays an important role in ensuring that the data is aligned correctly from the source system to the target system.

In this blog, we will explore the concept of ETL data mapping, its importance, and how it helps streamline data integration processes. The goal of this blog is to have a clear understanding of data mapping, its challenges, and how to implement it effectively.

What is ETL?

ETL stands for Extract, Transform, and Load. It is a process that extracts data from various sources, transforms it according to business requirements, and loads it into target systems such as a data warehouse or data lake.

The ETL process enables businesses to store the data centrally from multiple sources and use them for analysis and reporting. The three primary steps of ETL are –

1. Extract: In the extract step of the ETL process, the data is extracted from various sources like databases, applications, IoT devices, etc. The files can be structured, semi-structured, or unstructured.
2. Transform: In the transform phase, the extracted data is cleaned, filtered, validated according to business requirements, and converted into the required format for the target system. The transformation step may include data operations such as data aggregation, validation, and calculations.
3. Load: This is the final step of the ETL process. In this step, the transformed data is loaded into the target system, such as a data warehouse, database, or cloud storage, or sent to an API for further use.

What is ETL Data Mapping?

ETL data mapping is the process of defining the relationship between different data fields in source systems and target systems. It defines how a field from the source is mapped to the target system.

Example: In a customer database, one of the source systems has a field called “Name,” whereas the target system has separate fields called “First Name” and “Last Name.” Another example is that the source system has a field called customer_id, whereas the target system calls it client_id. The data mapping ensures that all of these fields are mapped accurately between the systems. 

Purpose of Data Mapping

The primary purpose of data mapping is to establish a connection between source and target systems for accurate data transfer.

For example, a source system has a currency field called ‘Dollar,’ but the target system uses ‘USD.’

An efficient and accurate data mapping ensures the data transformation process runs smoothly, minimizing data loss, redundancy, or inconsistencies.

Importance of ETL Data Mapping in Data Integration

Data mapping is crucial because it ensures that the data remains accurate, consistent, and reliable during integration. It helps align data systems where the data structures and formats differ. Without a proper mapping strategy, there are significant risks and challenges.

Why Data Mapping in ETL is Crucial:

• Data Integrity: Data integrity guarantees that the migrated data is accurate and that its integrity is maintained. Like the primary-foreign key relationship, parent-child relationship, etc.
• Consistency: Proper data mapping ensures that similar data elements are transformed similarly, promoting consistency in reporting and analysis.
• Efficiency: A well-mapped ETL process automates data transformation, making the process faster and more reliable.

Risks of Poor Data Mapping:

• Data Loss: Incorrect data mapping can cause data loss due to non-mapped fields, and data could be filtered out during the ETL process.
• Inaccuracies: Poor data mapping may lead to incorrect or incomplete data. This could result in inaccurate analysis and faulty decisions.
• System Errors: Abrupt data types or structures can cause errors during the ETL process.

Key Components of ETL Data Mapping

• Source Fields: These are the source system’s fields or attributes containing the raw data. These fields will be then mapped to the target systems.
• Target Fields: These are the corresponding fields in the target systems. For example, a source field customer_id can have the target fields as client_id.
• Mapping Rules: Mapping rules define the relationship between the source and target, such as how the source fields are transformed into the target systems. They contain business rules, aggregation logic, type conversions, etc. For example, a source field ‘name’ needs to be converted into lowercase before loading into the target systems.
• Transformation Logic: This contains the transformation logic that source data must undergo before reaching target systems. Transformational logic can vary from simple to complex, such as calculating averages or currency conversions.
• Metadata: Metadata is information about the data, such as data types, field names, field types, constraints, etc.

Types of Data Mapping Techniques

There are three major types of data mapping techniques, as explained below-  

1. Manual Mapping

Manual mapping defines the relationship between the source and target fields. Manual mapping offers complete control over the mapping process. However, this can be time-consuming and is prone to human error when large datasets are concerned.

2. Semi-Automated Mapping

Semi-automated mapping is done using tools that automatically suggest the mapping based on the data type and field names. These tools use mapping templates or mapping assistants to identify the mapping between source and target systems. Since it is semi-automated, human intervention is still required to handle complex mapping.

3. Automated Mapping

Automated mapping uses machine learning and Artificial Intelligence algorithms to detect the relationships between source and target systems automatically. Automated Mapping suggests that mapping is based on pre-defined rules or machine learning models. Automated mapping is way faster than manual or semi-automated mapping and is less likely to have human error. However, this technique requires initial setup and customization based on business needs.

Data Mapping Process For ETL: Step-by-Step Guide

The ETL data mapping process involves the following steps – 

1: Source Data Identification: The first step of this process is to analyze the source system from where the data needs to be extracted. This involves understanding the database schema, API integrations, reviewing data files, and many more. This is a crucial step, as incorrect analysis of the source system may lead to failures in the later stages.

2: Define Target Data Structure: After examining source data, the target system is defined. This involves understanding how the source data will be organized/mapped into the target systems. The target systems could be database schema, data warehouse, or cloud storage format.

3: Map Source to Target Fields: This is a crucial step, as, it defines the mapping of the source data field to the target. Each source field is mapped to the target field according to the business rule in this step. For example, the “user_id” from the source systems is mapped to the “customer_id” of the target system. This ensures the correct data flow during migration.

4: Define Transformation Logic: After the data mapping is completed, a transformation logic is defined (according to the business requirement) to ensure the data from the source is correctly transformed and matches the schema of the target systems. The transformation logic could be simple, like changing data formats, or complex, like aggregations, merging fields, etc.

5: Implement and Test the Mappings: The ETL tool implements the entire pipeline after defining the transformation logic. After deployment, it is then thoroughly tested to ensure zero defects. Testing the pipeline helps to ensure that the data mapping is correctly implemented.

6: Maintain and Update Mappings: The source and target systems may evolve over time, so reviewing the mappings periodically for changes and updates is essential. An ETL tool needs updated mapping to prevent failover. Regular maintenance ensures that the data integration process continues smoothly without disruptions.

Common Challenges and How to Overcome Them

During a data mapping process, many challenges arise; some of the common challenges that an organization may face are listed below- 

1. Complex Data Structures: Often, the data model of the source and target systems differs. For example, the source system may use a relational database model, whereas the target system uses a NoSQL model. Thus, an effective ETL tool is required to handle such a complex transformation.

2. Data Quality Issues: Source data often contain errors, missing values, or inconsistent formats. During the transformation phase, a robust data cleansing and validation process must address these issues before loading the data into the target system.

3. Inconsistent Data Formats: Different systems store data in different formats, such as dates and currencies. These formats must be standardized before the data is mapped to the target systems.

4. Scalability Concerns: In today’s big data era, data grows exponentially, and manual or semi-automated mapping can cause bottlenecks. Automating the mapping process using machine learning or specialized ETL tools can solve the scalability issues.

5. Data Security and Privacy: Sensitive data, such as PII, needs to be handled very carefully, ensuring compliance with GDPR or HIPAA. Implementing strong security measures and compliance checks to protect data throughout the ETL process is essential.

Top Data Mapping Tools and Software For ETL

Various ETL tools are available on the market to facilitate data mapping and data integration. Some of the top ETL data mapping tools include:

1. Hevo Data: A no-code ETL tool that offers automated data mapping and transformation capabilities, making it ideal for data integration without much manual effort.
2. Talend: Talend is a widely used open-source ETL tool that provides a complete solution for data integration and data mapping.
3. Informatica: Informatica offers an enterprise data grate integration solution with robust data mapping capabilities.
4. Microsoft SQL Server Integration Services (SSIS): A powerful ETL tool within the Microsoft ecosystem that provides a range of mapping and transformation capabilities.
5. Apache NiFi: Apache NiFi is an open-source tool that provides an excellent interface for automating data flow, including mapping and transformation features.

Conclusion

Data mapping in ETL is essential to ensure accurate data integration from various sources into a unified target system. Organizations can ensure data accuracy, integrity, and efficiency by understanding the process and applying best practices. 

If you are looking for an automated tool to map your ETL data, check out Hevo. Sign up for a 14-day free trial and experience the ease of automation. 

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