Data integrity is crucial—data needs to remain accurate, consistent, and reliable throughout its lifecycle. However, as data passes through each phase of its pipeline, it becomes more vulnerable to degradation and corruption. Thankfully, this degradation can be offset using some basic approaches, mainly testing and segmenting your data pipeline accordingly.

While simple in concept, these approaches can be tricky to implement. The following guide explores how you can use and integrate a few testing strategies into your data lifecycle management (DLM) strategy to ensure data integrity.

The Importance of Testing for Data Integrity

Data is a commodity, and as with most commodities, it's only as valuable (and useful) as its purity. There are four key aspects of data integrity:

• Accuracy: Data must be correct and free from errors and discrepancies. It should provide an accurate representation of the true values and facts.
• Consistency: Data must appear uniformly across the various systems and interfaces that access it. Consistent data ensures that there are no discrepancies when data is accessed or used.
• Completeness: All required information in the data should be present and intact. This is important because data drives decision-making. Incomplete data doesn't provide the full picture and can't be reliably analyzed.
• Authenticity: Corruption and unauthorized alterations can occur. That's why, in some instances, data may appear to be complete and consistent, but it's not genuine or reliable because it was modified in some way.

Ultimately, testing for data integrity involves validating these key aspects. Testing is also important to ensure the integrity of your data, which is often a regulatory requirement (such as with HIPAA and GDPR). Additionally, if you're using your data pipelines to feed applications and systems, your data's integrity will impact the quality of these products. High data integrity builds trust among stakeholders, including customers, partners, and regulatory bodies. Trustworthy data enhances the credibility of your organization.

The Current Landscape of Large-Scale Data Management

The mechanisms that move and transmit data are at the root of its degradation. By identifying the weak points in your data pipeline or lifecycle, you can formulate a strategy to help improve data integrity.

Current data pipeline models are subdivided into six key stages:

* Data sources
* Data ingestion
* Data transformation
* Data storage
* Data processing
* Data analysis and visualization

You can strategically place a test at each point to verify your data's integrity. However, the architecture and tools you use to facilitate your data pipeline can make this challenging.

Large-Scale Data Management Challenges

As data volumes grow, pipelines may struggle to scale efficiently. This can result in performance bottlenecks and increased latency. These fluctuations don't just make tracking and ensuring the quality of data difficult; they can also increase the overall risk of corruption.

Pipelines typically gather data from a variety of channels (as opposed to a single one). Integrating data from all these disparate sources is already challenging—especially when you're dealing with different data formats and structures. Your data integrity tests need to account for this nuance.

Additionally, because of the volatile nature of data transmission, your pipeline must undergo regular maintenance, which can be resource-intensive. Pipelines can also be vulnerable to security breaches due to their large attack surfaces. As such, it's important to implement the necessary security measures, such as access management controls and validation checks. High costs for pipeline infrastructure and tools can also be challenging, especially for large-scale operations. Ultimately, many of these limitations and challenges can be overcome through careful planning.

The earlier you can test for integrity, the more efficiently the latter stages of your pipeline will work. SQL-based data sources and repositories are popular because of their availability and ease of integration. You can essentially test and validate your data without having to add another tool if you know how to use Transact-SQL. Nevertheless, because of its limitations, relying on SQL to test data integrity may not be a good idea.

The Limitations of SQL-Based Testing

SQL is primarily designed for structured data. This makes it inefficient for testing unstructured or semi-structured data. To compensate for this, you can try to use manual SQL-based testing. While this may be effective in the early stages, it can become difficult due to the volume of data and the complexity of relational databases.

Generally, SQL-based tests can struggle with large data sets or complex queries. As you run more extensive SQL tests, your system can run into performance overhead, potentially slowing down the database and affecting other operations. Improper management of SQL-based testing can expose your database to security risks like SQL injection attacks. Frequent schema changes also make maintaining SQL test scripts difficult, potentially requiring costly automation tools.

Don't View Testing as an Afterthought

At this point, you may think you're doing fine because you've integrated testing into your data pipelines. However, integrating testing shouldn't be your only focus; you need to prioritize that testing and make sure all your pipelines are covered. Unfortunately, LinkedIn discovered this the hard way when its pipelines unintentionally used older data set versions. This led to outdated information being used in its analytics and decision-making processes.

LinkedIn stores and gathers its data sets from a collection of Hadoop clusters. Some of these data sets are copied and replicated, creating redundancies. To combat this, LinkedIn developed a continuous monitoring tool called Data Health Monitor (DHM) that continuously surveys and tests its data infrastructure at key points.

When it comes to testing, it's a good idea to follow LinkedIn's example and implement tests at multiple stages of your pipeline. This requires a test-first and test-driven development mentality, where you intentionally build highly testable data pipelines. Test-driven development (TDD) is first and foremost a software engineering philosophy. However, you can apply it to data engineering too.

Software Engineering Tests vs. Data Engineering Tests

Software engineering tests focus on the functionality and performance of software applications, while data engineering tests focus on the quality, consistency, and performance of data as it moves through pipelines. The types of tools used in software engineering tests and data engineering tests also differ.

Software engineers typically use tools like JUnit, Selenium, and Jenkins for testing, while data engineers might use tools like Great Expectations, dbt, and Apache Airflow. Each approach addresses a different target as well. Software testing verifies code correctness and user experience, while data testing deals with data integrity and schema validation and handles large volumes of data.

A few common software engineering tests include:

• Unit testing: This assesses individual components or functions of the software to ensure they work as expected.
• Integration testing: This ensures that different modules or services within an application work together correctly.
• System testing: This validates the complete and integrated software to ensure it meets the specified requirements.
• User acceptance testing (UAT): This verifies that the software meets the needs and requirements of end users and is conducted by them.
• Performance testing: This assesses the software's performance under various conditions, such as load and stress testing, to ensure it can handle expected usage.

Many of these tests have been around for decades. For instance, unit testing can trace its origins back to the mid-1950s. In contrast, because data engineering is a relatively new field, many of its testing methodologies are in their infancy. Some examples include:

• Data quality testing: This ensures the accuracy, completeness, and consistency of data as it moves through the pipeline.‍
• Schema validation testing: This verifies that the data conforms to the expected schema, ensuring that any changes in the data structure are detected and handled appropriately.
• ‍Pipeline testing: This tests the entire data pipeline to ensure that data is correctly ingested, processed, and stored.
• ‍Performance testing: Similar to software engineering, this assesses the performance of data pipelines under various conditions, ensuring they can handle large volumes of data efficiently.
• ‍Data consistency testing: This ensures that data remains consistent across different stages of the pipeline and between different data sources.

There are currently more resources for software testing than there are for data pipelines. Fortunately, because of the overlap between these fields, data engineers can apply many of the methodologies used in software testing to data integrity testing.

Using Software Testing for Data Integrity

With an understanding of software and data engineering tests, the next step is to determine how to integrate both into your pipeline and test environments. The following are a few conventional recommendations.

Incorporate Unit Tests for Data Sources

When using data sources like SQL to store your data, you can perform unit tests to ensure that your queries are efficient and that they're fetching the correct data. These tests can be integrated into your pipeline to run automatically when a schema change occurs. Each unit test should be isolated, meaning it should not rely on external data or other tests. This makes it easier to help you identify the source of any issue. You should use assertions to test inputs—particularly fixed inputs.

Apply Table/Data Diffs

Table or data diffs allow you to identify minute data sets and schema differences and involve comparing two data sets or tables. The most common way to do this is by using T-SQL.

For instance, if you were trying to find rows in one table that don't exist in another, you'd use something like the following:

```sql

SELECT * FROM Table1

EXCEPT

SELECT * FROM Table2;

```

This query fetches all rows that exist in `Table1` but don't exist in `Table2`. You can also set SQL triggers and stored procedures to automate your table diff checks.

Develop Comprehensive Audits

Audits encompass multiple test types and various aspects of the data pipeline. In the context of data integrity testing, audits involve structured assessments of data and its related procedures, with a focus on defining the involved parties. Thus, the first step of developing your audit review strategy involves identifying the scope and stakeholders. You then need to evaluate the database architecture, data entry procedures, security and encryption, and backup and recovery processes. These steps help you ascertain the nature of the data that is being handled and where the data is being fed from (and to). Additionally, they give you an idea of the layout of your data infrastructure, making it easier to identify any areas of improvement. Once you have this information, you can implement the necessary data consistency checks and validation procedures. This includes establishing the necessary feedback loops and reporting tools to record and help address any validation issues.

Setting Up Testing Environments: Challenges and Solutions

Keep in mind that integrating tools into your data pipeline can be disruptive. For instance, data diffs performed on large, complex data sets can be resource-intensive and slow down the entire pipeline. Setting up a test environment to evaluate your tests and tools is always a good idea.

Complexity

Data pipelines often involve a litany of tools and layers. These include elements such as storage, orchestration, transformation, integration, and visualization. Establishing a consistent environment across these layers can be complex.

You can minimize the complexity of setting up your data pipeline by implementing infrastructure-as-code (IaC) tools. These allow you to quickly scaffold and automate the setup of testing environments. Additionally, they enable you to ensure consistency and reduce manual errors.

Volume and Diversity of Data

Mimicking and managing large volumes of data in a test environment can be difficult. Yet, it's important that test environments accurately reflect production data to ensure accurate testing. You can overcome this by replicating data from your production environment or using synthetic data generation to create realistic test data sets from neural networks or simulations.

Data Privacy and Security

Using real production data in test environments can pose privacy and security risks that can infringe upon established data protection regulations. To anonymize sensitive data, you can use data obfuscation or masking techniques or stick to using synthetic data generation to create your test samples.

Performance Overhead

Running tests on large data sets can introduce significant performance overhead. This can slow down the development and testing process.

In addition (or as an alternative) to using an IaC solution, you can address this by using containerization to manage your test environments more efficiently. Tools like Docker enable you to create isolated and reproducible testing environments. This assists in dependency management and ensures consistency across various pipeline stages. You can also implement data observability tools to monitor data quality and pipeline performance.

Integration Issues

Integrating diverse data formats and structures in the data pipeline can pose challenges. This can be addressed using a staging environment cloned from your production environment. In addition to this, it's always good practice to utilize dependable version control systems like Git to manage any alterations in the data pipeline code.

CI/CD practices and tools can also grant you the ability to automate the testing and deployment of data pipelines. You can configure solutions such as Jenkins, GitLab CI, or CircleCI to run tests automatically on code changes.

This sample CI/CD pipeline configuration is separated into three major parts: the development environment (including the code management solution), the test environment, and the production environment. Before the code is pushed into the repository, engineers must perform a set of manual tests. After passing these tests, the code is built, and automated tests are then run on the compiled code. If the system fails any tests at any stage, feedback is sent to the developer, and the process starts again. This is ultimately how a TDD CI/CD pipeline works. Broken code should never reach the production environment or end user.

Data Pipeline Testing Tools and Frameworks

There are several tools and frameworks available for testing data pipelines. Each tool offers unique features to help ensure data quality, performance, and reliability. Below are some popular options.

Orchestration and Workflow Management Tools

Managing and testing your data pipeline requires you to administrate complex workflows and processes. This involves integrating multiple tasks and ensuring that they seamlessly complement and work together. Two of the best tools for orchestration and workflow management are Apache Airflow, which allows you to define, schedule, and monitor data pipelines, and Apache NiFi, which is designed for data flow automation and provides a web-based interface to design and monitor data flows. Since both tools offer substantial design and monitoring features, they can be used to survey your data as well as create timely logs and reports that you can later use for your audit strategy.

Data Integration and ETL Tools

Data integration tools enable organizations to combine data from different sources into a single, cohesive view. These tools help extract, transform, and load (ETL), moving data across various endpoints and infrastructures.

The two most common data integration tools are Informatica, which offers robust ETL capabilities and built-in testing features, and Talend, an open source ETL tool that provides data integration, data quality, and data governance features. Informatica, in particular, offers test data provisioning, sensitive data discovery and masking, and extensive DevOps support.

Data Quality and Validation Tools

Data quality and validation tools allow you to test and validate the veracity and quality of your data. The two most notable examples include:

• Great Expectations, an open source tool for validating, documenting, and profiling your data to ensure it meets expectations. The tool's most notable testing features include expectation-based testing (assertions), automated data validation, and data profiling and scaffolding.
• dbt, which focuses on transforming data in your warehouse and includes testing capabilities to ensure data quality. With dbt, you can create custom tests that use a combination of SQL and Jinja, validate your data using assertions, and perform comprehensive unit testing. It's the best option for data engineers with a background in software testing.

Monitoring and Observability Tools

Data monitoring tools focus on tracking the performance and health of data systems. They provide real-time insights into data flows, system performance, and potential issues. Your quintessential data monitoring tool should have real-time alerts, performance metrics, and a customizable dashboard.

There are also data observability tools that go beyond monitoring by providing deeper insights into the data's behavior, quality, and lineage. These tools help ensure that data is accurate, reliable, and available.

In addition to data lineage features, a good data observability tool should have anomaly detection and root cause analysis. Having these capabilities helps teams maintain data quality and quickly resolve issues when they arise.

Secoda is a good example of a tool that brings together all these monitoring and observability capabilities. It provides features like real-time alerts, performance tracking, data lineage, and anomaly detection in a single platform.

Secoda’s real-time pipeline monitoring helps catch data quality issues before they cascade downstream, ensuring data integrity at every stage.

Performance Testing Tools

Data pipeline performance testing allows you to identify bottlenecks, optimize the overall performance of your pipeline, ensure scalability, and improve overall reliability. Ultimately, by leveraging performance testing tools, you can ensure that your data pipelines are robust, efficient, and capable of handling the demands of your business operations. Some examples of performance testing tools for data pipelines include:

• Gatling, an open source load testing tool designed for high-performance testing of web applications and APIs. It offers real-time monitoring, detailed reports, and scalability.
• Apache JMeter, which is primarily used for web application performance testing but can also be used to test the performance of data pipelines by simulating load and measuring response times.

Conclusion

Because there's so much data, it's easy to underestimate how much of a commodity it is. However, data is only truly valuable when it's pure. While information (metadata) about the data can give you an idea of its state, you can only truly confirm its integrity by fully observing the data itself. Gaining this full observability and knowledgeability into your data can be difficult—especially as it's continually moving and transforming.

Secoda is an AI data solution that supplies you with all the necessary tools to achieve the observability you need. It's designed to help organizations easily catalog, document, monitor, and search through their data. You don't have to create your own processes to test your data to ensure its integrity. Secoda consolidates and provides you with all the necessary tools to do so. Book a demo today to see all that Secoda has to offer.