Data Mapping in ETL: What it is & How it Works?

With the rise in data production, there has been a significant increase in the data integration market. According to a report by Precedence Research, the data integration market will reach USD 47.6 billion by 2034, expanding at a CAGR of 12.06%.

Data integration primarily follows two approaches: ETL and ELT, depending on when the data transformation occurs in the process. To ensure efficient data integration, you can rely on the data mapping process—a crucial step that enables standardized data for better accessibility.

In this guide, you’ll learn about ETL data mapping, its core components, and different types. Implementing data mapping during ETL integration enables you to create high-quality datasets. They are useful for varied purposes, including efficient data analysis and governance.

What is Data Mapping in ETL?

Data mapping in ETL is the process of matching related data fields across two or more source databases before consolidating them into a destination. By mapping data, you can identify relationships between diverse data records and transform them to get a consistent dataset. Such a dataset is essential for accurate analytics and business operations.

For example, you need to map data from an SQL source to a destination system ( wrt. above image). First, you compare fields such as customer_id, first_name, last_name, middle_name, and email with their corresponding entities in the destination. In this process, you may need to:

• Rename customer_id to source_id to match the destination schema.
• Merge first_name, middle_name, and last_name as ‘full_name’ in the target data system.

Where Does Data Mapping Fit in the ETL Process?

The ETL data integration technique involves the extraction of data from various sources. You can then transform and load this data to a suitable destination. Here, the role of data mapping comes into play during transformation.

During data mapping, you match the data source elements to corresponding fields in the destination. You can also set transformation rules to modify source data records before loading.

To implement effective ETL data integration, you can opt for semantic mapping that involves finding contextual similarity between differently represented data records in source and destination systems. For example, a field named ‘birth_date’ in the source may be mapped to ‘dob’ in the destination.

Core Components of ETL Data Mapping

ETL data mapping consists of several core components. A few of these are as follows:

1. Source Elements

The source elements include the fields that you have extracted from source data systems. Before mapping, you should first properly examine these data elements to understand their structure, data type, and format.

2. Target Elements

Target data elements are the fields in the destinations, such as data warehouses, cloud storage solutions, BI tools, or data lakes. Understanding these elements allows you to prepare a strategy for data mapping in advance.

3. Mapping Rules

Mapping rules are the set of guidelines that you must follow to transform source data records to match target data fields. These guidelines include formatting rules, data type conversions, and renaming criteria.

4. Transformation Logic

Transformation logic involves data enrichment, filtering, and cleaning techniques. These methods assure accurate and credible data mapping.

5. Metadata

Metadata is data that provides information about different fields within a given dataset, such as table names, column names, primary keys, and data types. For example, in a table named customers, metadata includes fields like customer_id, email, and name. These fields have data types, such as INT or VARCHAR, along with customer_id as the primary key. This metadata helps you map source and target fields.

Importance of Data Mapping in ETL

Data mapping can enhance the operational efficiency of your organization. Here is how it can be useful for you:

Better Data Quality

Data mapping helps you tune datasets by aligning the fields, formats, and different types of data records across multiple databases. Through this, you can eliminate discrepancies between data points stored in different sources. Data mapping also allows you to transform data correctly by merging duplicates and removing missing values. This minimizes data redundancy. Such practices enable you to prepare a high-quality data repository through ETL integration.

Effective Data Analysis

ETL data mapping allows you to achieve a clean and well-integrated dataset. Such a data store helps you in generating meaningful analytical insights. You can also use this data to train machine learning models for enhanced predictive analytics.

Robust Data Governance

Effective data governance ensures the availability of high-quality datasets for workflow operations. A well-defined data governance strategy requires a unified and complete dataset. ETL data integration and proper mapping can help you achieve this.

Compliance with data protection guidelines such as GDPR and HIPAA is another critical aspect of data governance. Mapping source and destination fields simplifies identifying the sensitive data records. You can then choose and implement security mechanisms like masking to protect your data.

Cost Management

Data mapping ensures error-free data workflows, saving the cost of reworking. It allows you to integrate only relevant data, reducing the cost of investment in unnecessary computational resources.

Types of Data Mapping

There are three main types of data mapping:

Manual Data Mapping

Manual data mapping involves writing lengthy code to link and understand relations between the data records. This approach is favorable for one-time data transfer or small datasets. Another way to do manual data mapping could be to use graphical mapping features with a drag-and-drop interface. However, you may find this functionality complex to use.

Automated Data Mapping

Automated data mapping could be done using software that can manage all the steps within the mapping process. These solutions use various techniques like rule-based mapping to automatically identify how to map source and target data fields. Some data mapping tools also allow you to use machine learning and AI to correctly map your data.

Semi-automated Data Mapping

Semi-automated data mapping is a hybrid approach that combines manual intervention with automation to map data fields. The tool automatically detects source and target schemas. It also suggests field-to-field mappings based on pattern recognition and metadata analysis. You can manually set custom transformation rules and review and modify suggested mappings.

How Does ETL Data Mapping Work?

Here is a step-by-step explanation of how ETL data mapping works:

Step 1: Recognize the Source Data Elements

First, identify the data elements you want to extract from the source data system. The sources can include databases, flat files, data warehouses, or data lakes.

Step 2: Develop a Plan to Map Your Data

Create a detailed data map to align the source data points with the destination. To accomplish this, match the metadata of the source and target fields. You can then frame mapping rules and transformation logic to convert the source data points into suitable formats.

Step 3: Test

Test data map rules using data validation techniques such as data type check, format check, and uniqueness check. You should also do end-to-end performance testing by measuring KPIs, such as processing speed and error rates. By doing this, you can ensure data flows properly through various stages. If there are any bugs, you can remove them in this step so that the final outcome is accurate.

Step 4: Data Migration

Now, apply the mapping rules to the desired data points. You can then migrate the standardized data to the destination system.

Step 5: Monitor

You must monitor the source and destination to ensure the accuracy of data mapping. If there are any updates in source data systems, you should modify mapping rules and transformation logic to reflect those changes.

How ETL Data Mapping Works in Airbyte?

There are many tools that enable you to map and integrate data efficiently. One such solution is Airbyte. It is a data movement platform that offers a large library of 550+ pre-built connectors. You can use any of these connectors to extract data from a source and load it to a destination. After this, you can transform the data by integrating Airbyte with dbt, an open-source data transformation tool.

Amazing features offered by Airbyte:

• Develop Custom Connectors: Airbyte provides multiple options to build your own custom connectors. These options include Connector Builder, Low Code Connector Development Kit (CDK), Python CDK, and Java CDK.‍
• AI-powered Connector Development: While using Connector Builder to develop custom connectors, you can use AI assistant. It automatically pre-fills necessary fields and also provides intelligent suggestions to fine-tune the connector configuration task.‍
• Schema Change Management: You can manage how Airbyte must handle source schema changes for each connection. For Cloud users, Airbyte automatically checks for schema changes in source data every 15 minutes, and for Self-hosted users every 24 hours. You can manually refresh your schema at any time to detect and sync changes in the destination.‍
• Streamline GenAI Workflows: You can directly load semi-structured and unstructured data into vector store destinations while using Airbyte. It supports vector databases such as Milvus, Pinecone, and Chroma. Integrating these vector databases with LLMs allows you to improve their accuracy. Such functionality enables you to enhance your GenAI-supported workflows.‍
• Change Data Capture (CDC): Airbyte’s CDC feature helps you to incrementally capture changes made to the source data system and replicate them in the destination. CDC feature aids in keeping the source and destination in sync with each other.

Another unique offer of Airbyte is its open-source Python library, PyAirbyte. It offers a set of utilities for using Airbyte connectors in the Python ecosystem. With the help of PyAirbyte, you can extract data from a variety of sources and load it in SQL caches like Postgres.

To map data using PyAirbyte, start by assessing the relationship between your cache and destination data fields. Next, you can clean and transform the data records using Python libraries like Pandas. Once the data is properly structured, you can load it into a suitable PyAirbyte-supported destination.

Conclusion

Data mapping is a necessary step in ETL data integration, ensuring efficient data transfer. This blog gives you a thorough overview of ETL data mapping, covering key components, such as mapping rules, transformation logic, and metadata. These elements help streamline the data mapping process. By implementing ETL data mapping, you can ensure better data quality for effective business analytics and optimize operational costs.