ETL Data Quality Testing: Tips for Cleaner Pipelines

Data is often available in dispersed sources and in varied formats. Whenever you migrate this data into a centralized repository for enhanced accessibility, outliers are expected to disrupt the data quality in most cases. It becomes essential to eliminate the data quality issues before beginning analysis, as analyzing low-quality data might result in biased insight generation.

This article comprehensively covers ETL data quality testing, its importance, common issues, and the procedure to maintain high-quality data.

What Is ETL Data Quality Testing?

ETL data quality testing is the process of evaluating the accuracy and consistency of the data flowing through an ETL (Extract, Transform, and Load) pipeline. This process ensures proper data extraction from source systems, transformation based on business requirements, and effective data loading to the target platform.

Importance of Data Quality Checks in ETL

According to a study conducted by Gartner in 2021, every year, poor data quality costs organizations an average of $12.9 million.

Here are a few factors that make ETL data quality testing essential:

• Decision Making: Data quality testing in ETL enables you to enhance the accuracy of the insights generated from the analysis. ETL pipelines that produce high-quality data facilitate the generation of actionable insights that can positively impact business performance.‍
• Eliminating Inconsistencies: By performing ETL data quality testing, you can identify and resolve data-related issues at an early stage before proceeding with further analysis. This might involve discarding error values, outliers, and inconsistencies.‍
• Ensuring Compliance: ETL data quality evaluation aids in maintaining compliance with industry standards and regulations. Sensitive information can be identified and masked before migrating the data to secure it from unauthorized access. This allows your organization to adhere to data privacy regulations and governance policies.

Common Data Quality Issues

Here are some of the commonly encountered data quality issues:

1. Data Redundancy

Data redundancy is one of the most frequent data quality issues. It occurs when duplicate data is stored in a database. In this scenario, the data can skew the analysis toward repetitive values. It can also lead to increased storage costs and performance issues.

For example, a customer database may contain multiple records for the same individual. To avoid data redundancy, you can compare key fields, such as customer ID, in the table. Data deduplication techniques can also help remove redundant records to maintain data integrity.

2. Missing Data Values

Missing data values can significantly affect the accuracy and reliability of the analysis, making the analysis output inaccurate and biased.

For example, an e-commerce company with missing customer records may have difficulty personalizing recommendations or reaching specific target customers. This results in higher churn rates, negatively impacting business.

To identify missing and incomplete data, you can rely on various techniques, such as data profiling and statistical analysis.

3. Data Anomalies

Anomalies are the values that do not fit the expected pattern in a data repository. If not handled properly, they can skew the analysis, leading to biased insights.

For example, suppose you are analyzing the income and educational background of professionals. Often, the higher the education level, the greater the income. However, if the dataset contains an outlier like a young billionaire entrepreneur, the analysis can produce unexpected results.

To identify the anomalies, you can use statistical modeling techniques like quantiles, visual detection methods like scatter plots, and machine learning models like isolation forests.

4. Outdated or Irrelevant Data

Using obsolete data for insight generation results in inaccurate predictions. This is especially applicable to processes that require up-to-date information to optimize business performance.

For example, relying on outdated customer preferences in marketing campaigns can lead to reduced engagement and lower conversion rates.

You can use tools that offer incremental data updates to ensure that the source data changes are always replicated in the destination system. Another method to keep your data updated is to use data synchronization tools with change data capture functionality. This aids in identifying and capturing only the changes made to the data source system.

5. Data Inconsistency

Similar data may be available in different formats across various sources. When gathering information from multiple sources for analysis, such data can cause confusion that can lead to errors in reporting.

For example, one data field in the source can contain the United States of America, while another can have the USA as a value. Although both values represent the same entity, this can cause analysis issues if it is not identified.

You can use data harmonization principles to counter this challenge. By applying these principles, you can standardize data and the various data formats available in a single feature or column.

How to Perform Data Quality Testing in ETL?

Conducting ETL data quality checks requires you to follow a step-by-step approach. This process spans from establishing data quality criteria to preparing data monitoring strategies for generating high-quality data throughout the ETL workflow. Let’s explore the key steps involved in performing data quality testing in ETL.

1. Establish Data Quality Criteria

The first step to performing data quality testing in ETL is to define clear objectives that highlight acceptable data quality. It involves establishing the properties that data must have, including accuracy, consistency, and completeness. By defining benchmarks for each stage of the data processing within an application, you can formulate rules that allow the generation of high-quality information.

2. Data Profiling

Data profiling is the process of analyzing datasets to understand their structure and fixing issues like missing data values, duplicates, or anomalies. Understanding the structure of the data assists in ensuring consistency. Data profiling enables you to assess data quality, identify error values, and ensure regulatory compliance. Some of the most frequently used data profiling techniques include column profiling, cross-column profiling, data distribution analysis, and data pattern profiling.

3. Data Validation

Implementing data validation strategies for every stage of the ETL process is beneficial for facilitating high data integrity. These data quality checks include techniques like constraint validation, range checks, consistency checks, data type validation, uniqueness checks, and referential integrity validation. Following certain best practices, such as thorough documentation and automation, can also assist in streamlining data validation.

4. Monitoring Data Transformation

Data transformation is the intermediate stage of the ETL pipeline that enables you to structure extracted data into an analysis-ready format. When monitoring this process, you must track the transformations made to the source data. This involves identifying data changes, determining any transformation issues, and ensuring data compliance with the predefined rules. With the aid of real-time data monitoring, you can perform regular checks to address issues for maintaining high data quality standards.

5. Verify Against Data Quality Metrics

The final step of the ETL data quality testing is to verify the output of the ETL pipeline aligns with the key performance indicators. This stage involves the generation of a comprehensive report outlining the performance of the ETL pipeline. The performance review includes data quality trends, issues, and areas for improvement. Examining the report allows you to enhance the results of your ETL processes.

Tools and Technologies Used to Check ETL Data Quality

Ensuring data quality in ETL pipelines requires specialized tools and technologies. These tools can enable you to validate, profile, and synchronize data to ensure consistency, accuracy, and compliance with your business requirements. Here are the prominent tools and techniques that you can incorporate to check ETL data quality:

1. ETL Testing Tools

ETL testing tools are used to verify the accuracy and integrity of the data migration process. Tools like IBM InfoSphere Information Analyzer can help you assess data quality. These tools rely on two key techniques: white-box and black-box testing.

White-box testing involves understanding the internal workings of the ETL process. On the other hand, black-box testing primarily focuses on comparing the final output with the expected results without examining the internal logic.

2. SQL Querying Tools

SQL querying tools enable you to store, update, remove, search, and retrieve information from a database. Tools like MySQL workbench or SQL Server Management Studio (SSMS) are usually preferable for inspecting data quality. You can use these tools to write SQL queries that perform data integrity checks. Comparing data across various locations using SQL enables the maintenance of data consistency.

3. Data Profiling Tools

Data profiling tools are essential for enhancing data accuracy. Various tools, like Astera Centerprise and OpenRefine, are beneficial to ensure data quality. Utilizing these tools, you can establish data relationships between different data sources. This facilitates the identification of data trends that can assist in improving customer experience.

4. Automated Testing Frameworks

Automated testing frameworks are beneficial for automatically comparing the performance of an ETL pipeline with predefined benchmarks. Tools like Apache JMeter and Selenium are advantageous in automating the testing procedure throughout every ETL process. These tools can help you validate data throughput, identify performance bottlenecks, and maintain performance standards. 

By leveraging automated testing frameworks, you can minimize the efforts required to perform repetitive tasks, saving time and resources.

5. Data Quality Management Platforms

Data quality management involves the effective maintenance of data throughout its lifecycle. With tools like IBM InfoSphere QualityStage, you can access a comprehensive set of features to execute data quality checks in ETL. It provides you the capability to profile, clean, deduplicate, and validate the data.

6. Data Validation Testing

Data validation testing involves ensuring that data inputs, outputs, and storage mechanisms adhere to the predefined standards. Some of the common validation techniques include manual inspection, range and constraint checking, cross-field validation, and statistical analysis. To perform validation steps, you can rely on tools like Datameer, Informatica, and Alteryx.

7. Data Synchronization Tools

Efficient synchronization of data requires building robust systems capable of handling data changes in the source platform. This ensures that data remains consistent across various platforms within your organization. However, manually building ETL pipelines that ensure data consistency can be a challenging task, requiring additional computational resources. To overcome this complexity, you can utilize data synchronization tools like Airbyte.

Airbyte is a data movement platform that allows you to unify data from diverse sources into your preferred destination. With over 550 pre-built data connectors, it enables you to move structured, semi-structured, and unstructured data to a centralized repository. If the connector you seek is unavailable, Airbyte offers a Connector Builder and a suite of Connector Development Kits (CDKs) for building custom connectors.

Let’s explore some key features of Airbyte:

• End-to-End Testing: Airbyte offers end-to-end testing of data pipelines through its E2E test source connector. This connector allows you to generate arbitrary data streams and check different aspects of the data migration procedure.‍
• ETL Pipeline Development: Although Airbyte primarily supports ELT pipelines, it provides ETL features with PyAirbyte—a Python library. PyAirbyte allows you to extract data from various systems using Airbyte connectors and load it into prominent SQL caches. Cached data is compatible with Python libraries, which further facilitates custom transformations. After transforming the data, you can finally load it into the destination of your choice.‍
• Change Data Capture (CDC): CDC functionality enables you to identify incremental data changes made to the source file and replicate them to the target system. With this feature, you can keep track of updates and maintain data consistency.‍
• Vector Database Support: Airbyte supports popular vector databases, including Pinecone, Milvus, and Chroma. By storing your data in these data stores, you can facilitate the generation of powerful AI applications.‍
• Flexible Pricing: With Airbyte, you get flexible pricing options depending on your specific data synchronization requirements. It has three pricing plans: Cloud, Team, and Enterprise. The Airbyte Cloud version is charged based on the volume of data you replicate. In hindsight, the pricing related to the Enterprise and Team versions depends on the total number of pipelines syncing data at a time.

Conclusion

By following a structured step-by-step guide on ETL data quality testing, you can ensure the generation of high-quality data from your data pipeline. In this process, establishing clear criteria aids in aligning the structure of your data with the business requirements. After creating an outline describing the expected data quality, you can perform tasks such as data profiling, validation, and monitoring. Finally, you can define and compare the resulting data in accordance with certain KPIs. These performance metrics can help you produce information that complies with industry standards.

To optimize data synchronization within your workflow, sign up for Airbyte.