Big Data Testing: Benefits, Challenges & Tools

When testing ETLs for big data applications, data engineers usually face a challenge that originates in the very nature of data lakes. Since we’re writing or streaming huge volumes of data to a central location, it only makes sense to carry out data testing against equally massive amounts of data.

You need to test with the real volume of data – and not just the number of users, but the volume, complexity, and variety of your data set. All the different tools and software versions in the test and production environments need to be the same. 

That makes sense, doesn’t it? Only it’s easier said than done. Replicating production data is time-consuming and expensive. In today’s economic reality, requesting a budget for data testing purposes is doomed to fail. 

Luckily, there’s a way out of this mess. Keep reading to explore all the nuances of big data testing and get a practical solution.

What is Big Data Testing?

Big data testing is the process of testing big data applications to ensure that they work as expected. It entails testing and validating the functionality of big data applications that typical storage systems cannot manage.

Why is big data testing challenging?

Object stores may be cheap, but they’re certainly not free. In the big data world, teams deal with data lakes that are often petabytes in size – and rapidly growing as the organization expands. Copying files to separate buckets for production-like testing can take hours. 

If your data lake consumes 100 TB of data on Amazon S3, creating a single copy of the data for a continuous testing environment will cost roughly $25,000 annually. Want to run several test environments in parallel? Just multiply that figure – and prepare for a tricky conversation with the finance manager!

Traditional Database Testing vs Big Data Testing

	Big data testing	Traditional testing
Data Types	Big data testing encompasses a wide range of data formats, including structured, semi-structured, and unstructured. Semi-structured data includes XML files and NoSQL databases, whereas unstructured data includes text files, photos, audio, video, and social media posts.	Traditional database testing often involves structured data, which is predictable and frequently recorded in relational databases or spreadsheets. CRM, ERP, and transaction databases are all examples of data sources.
Infrastructure	Big data uses a distributed architecture in which data is dispersed over several servers or nodes, which may be physical or cloud-based. This distributed technique improves scalability and performance by allowing data to be processed in parallel, but it requires specialized products and protocols.	Traditional data has a centralized database architecture, which means that all data is aggregated and controlled in a single location, such as a physical server or a cloud-based platform. While this centralized approach makes data management and security easier, it may hinder scalability and performance.
Data volume	As the name implies, big data contains a vast amount of information, ranging from terabytes (TB) to petabytes (PB) and even exabytes (EB). Because of its sheer size, traditional data processing technologies are insufficient to handle it. Specialized processing frameworks, such as Hadoop or Spark, are required for practical analysis and administration.	Traditional data is relatively small. It’s manageable and can be handled using ordinary data processing tools. The data volume typically ranges between kilobytes (KB) and terabytes (TB).
Validation Tools	The sheer amount and complexity of big data make it difficult to process and evaluate using traditional data management solutions. As a result, specialized technologies such as Hadoop, Spark, and NoSQL databases have emerged to meet the unique challenges of storing, managing, and analyzing large volumes. These tools are designed to handle the volume, velocity, and variety of big data.	Traditional database testing involves handling and accessing traditional data using Structured Query Language (SQL) and other traditional data analysis tools. These solutions are specifically built to manage structured data, allowing for easy processing and analysis to generate business insights.

Engineering Culture as the First Step to Big Data Testing

In software development, rigorous testing is the best way to improve software quality. The same is true for data engineering; teams need to build and execute a comprehensive testing strategy to achieve the Holy Grail of high-quality data in production.

Since data teams often face hard deadlines, it’s common for engineers to create functional data pipelines that are not necessarily built with best practices in mind. 

That’s why organizations looking to achieve a high quality of data need to build a culture that supports incorporating all the best practices that are valuable in the long run.

This is especially important given that not all data engineers (or data engineering leaders, for that matter) have a software engineering background. They might not be that familiar with SWE development principles and best practices.

Note that the industry itself is only catching up with these practices. Running an automated suite of tests and automated deployment/release of data products still can’t be considered mainstream.

Finally, there’s the complexity that comes from big data itself. In ETL testing, data engineers need to compare huge volumes of data (on the scale of millions of records), often coming from different source systems. This includes comparing transformed data resulting from complex SQL queries or Spark jobs.

Big data testing is a data-centric testing process. To effectively test data pipelines, engineers need production-like data across the volume, variety, and velocity. 

Benefits of Big Data Testing

Businesses need big data testing to ensure the integrity and accuracy of their decision-making data. Testing and giving trustworthy data helps companies find and fix issues.

• Accurate Data – Big data testing helps companies avoid costly mistakes. By finding and fixing data errors, businesses may avoid making bad decisions, which can help them avoid wasting time, money, and resources on ineffective projects.
• Improved Efficiency – Businesses may increase system and process efficiency with big data testing. Addressing data collection, storage, and analysis system concerns can streamline processes and save resources. This boosts production and saves money.
• Rules and Compliance – Big data testing helps firms comply with regulations and standards, making it essential. Verifying data accuracy and completeness helps organizations comply with regulations and avoid fines.
• Smart Business Decisions – Big data testing distinguishes useful from useless big data. Unimportant big data can influence decisions and cause losses, whereas genuine big data can improve decision-making and help managers make better decisions. Implementing proper, trustworthy, and flawless big data testing methods is essential.
• Optimized Business Model – High-quality big data can personalize customer experiences, incorporate predictive behavioral targeting, and boost loyalty. The big data an organization wants to use must be authenticated.

What Data Do Teams Use To Replicate Production Scale For Data Testing?

Testing against production data is risky, so many data engineering teams resort to various tactics that give them access to production-like data for testing purposes.  

1. Using Mock Data

Many data engineers use this approach because creating mock data is relatively easy thanks to the plethora of synthetic data generation tools such as Faker. However, mock data doesn’t reflect production data regarding volume, variety, or velocity. You won’t be testing the full picture and might miss out on issues that snowball into real problems later on.

2. Sampling Production Data To The Test/Dev Environment

Another common tactic is copying a fraction of the production data instead of the entire thing, and then testing it. If you go for this approach, make sure to use the right sampling strategy to ensure the sample reflects real-world production data. Tests that run successfully on sample prod data may easily fail on actual data because volumes and variety aren’t guaranteed.

3. Copying All The Production Data To The Test Environment

If you do that, you’ll have all the real-world production data available for testing. It sounds too good to be true? That’s because it is. 

First, if your production data contains PII data, copying it for testing purposes might lead to data privacy violations. Second, if your production data changes constantly, then the copy of prod data in the test/dev environment will become stale. 

You’ll need to constantly update it. So, while copying prod data guarantees volume and variety, it doesn’t guarantee velocity.

4. Copying Anonymized Production Data To The Test Environment

This tactic again makes all the real-world production data available for your data testing initiative. It also guarantees compliance with all data privacy regulations.

But constantly changing prod data will again create a challenge for the team. The data in test env might become stale quickly and you’ll have to refresh it regularly. 

Also, you’ll need to run PII anonymization every time you copy data out of prod. Running anonymization steps manually every time and maintaining a long-running test data environment is error-prone and resource-intensive. It adds a huge overhead to the already busy data engineering team, which is a problem.

5. Using A Data Versioning Tool To Fully Mimic Production Data To Dev/Test Env

This tactic paves the way for the future. You get access to real-world production data to be used in automated, short-lived test environments available through a Git-like API. All you need to do is add a new tool to your existing data stack.

To help you understand how this last approach works, let’s take a look at this practical example of using lakeFS for big data testing.

Testing Types for Big Data Applications

Success in big data testing requires knowledge of numerous testing methods. Each type works differently to ensure big data infrastructure quality, functionality, and performance. Here are the most important and common testing kinds, with examples:

Data Quality Testing

Data is thoroughly checked for accuracy, completeness, consistency, and business rules before entering your analytics pipeline. Data profiling, cleansing, and anomaly detection are crucial to data integrity.

Examples:

• Looking for missing numbers, anomalies, or irregularities in client records
• Checking reporting financial data correctness
• Compliance with data representation industry standards

Schema Testing

Schema testing corrects and enforces data structures and relationships. It verifies schema definitions, enforces data types and constraints, and eliminates structural mistakes. It also involves evaluating schema definitions, enforcing data types and restrictions, and preventing structural flaws that could disrupt downstream processes.

Examples:

• Checking customer data for schema compliance and expected fields and data types
• Maintaining product catalog data structure across sources

Pipeline Testing 

Data circulates across your big data ecosystem via data pipelines. Pipeline testing finds bottlenecks, synchronization issues, and performance issues to ensure data flow. 

Examples:

• Simulating high-volume data input to discover pipeline bottlenecks and throughput
• Testing data synchronization between systems or databases
• Validating pipeline data transformation correctness

Algorithm Testing

Big data analytics relies on algorithms. To provide accurate findings and prevent model drift, algorithms are tested for accuracy, efficiency, and bias. To verify your ideas, it uses statistical analysis, model validation, and A/B testing.

Examples:

• Precision and recall are used to assess model predictions
• Testing for model bias to avoid discrimination
• Comparing algorithm or model performance via A/B testing

Functional Testing

Big data applications and systems are functionally tested to satisfy business needs. To ensure smooth functioning and user satisfaction, components, user interfaces, and end-to-end procedures are tested.

Examples:

• Testing a big data analytics platform’s search feature for accuracy
• Verifying a data visualization tool’s UI for usability
• Big data report accuracy validation

Performance Testing

Performance testing assesses your system’s capacity to manage massive data, high concurrency, and different workloads. Response time, throughput, and resource utilization are measured to detect bottlenecks and optimize performance for peak efficiency.

Examples:

• Simulations of peak loads to evaluate system response and throughput
• Assessing system performance with varying data volumes and workloads
• Finding and optimizing performance bottlenecks

Security Testing

Security testing protects large data infrastructure against vulnerabilities and unwanted access. To safeguard data and trust, it includes penetration testing, vulnerability assessment, and compliance audits.

Examples:

• Penetration testing for security vulnerabilities.
• Data privacy compliance assessment.
• Securing with encryption and access controls.

Challenges in Big Data Testing

Data amount, variety, and velocity can overwhelm traditional testing methods, requiring creative solutions and specialized talents. Here are some big data testing challenges:

• Data Deluge – Navigating massive datasets with several formats and ever-changing structures can be like looking for a needle in a haystack
• Performance Issues – Optimal performance under heavy data loads and complicated processing calls for advanced methodology and strong testing frameworks
• Security Testing – Protecting sensitive information in broad data ecosystems needs comprehensive security testing and ongoing vigilance
• Skill Gap – Finding skilled testers with competence in big data technologies and remote testing settings might be challenging
• Ecosystem Evolution – Big data tools and technologies evolve quickly, requiring constant adaptation and learning
• Cost –  Implementing a thorough big data testing strategy may requires a large upfront investment in tools, infrastructure, and training

Best Practices for Effective Big Data Testing

By following these best practices, you can ensure that your data is reliable, your performance is great, and your business decisions are based on trustworthy insights.

• Align with Business Needs – Do not get lost in the data maze! Focus your testing efforts on areas that have a direct influence on business goals and essential data points.
• Implement Automation – Repetitive jobs demand a significant amount of manual work and time. Use automation technologies to expedite routine testing and free up resources for more in-depth analysis.
• Build Integrated Tests – Instead of siloing your testing, seamlessly integrate data quality, performance, and security tests throughout the entire data lifecycle, from ingestion to analysis.
• Leverage the Right Tools – Choose strong big data testing frameworks such as Hadoop and Spark, and think about cloud-based options for scalability.
• Cultivate collaboration – Testing is not a solo performance. Encourage open communication and collaboration among data engineers, analysts, and testers to achieve holistic problem-solving.
• Embrace Continuous Improvement – The data landscape is constantly changing, so adapt! Continuously monitor outcomes, fine-tune your testing plan, and stay ahead of the curve.

Common Big Data Testing Tools

In Big Data, various tools are used to address the challenges of managing large datasets, categorized into data storage, processing, and analysis tools.

Apache Hadoop

A foundational tool in Big Data, Apache Hadoop provides scalable, distributed data storage via the Hadoop Distributed File System (HDFS). HDFS breaks large files into smaller blocks and distributes them across nodes, ensuring data redundancy and high availability. 

This system also supports parallel processing for efficient data analysis and offers replication to protect against node failures. Hadoop’s flexible storage accommodates structured, semi-structured, and unstructured data.

Apache Spark

Apache Spark excels in data processing with its Resilient Distributed Dataset (RDD) system, allowing fault-tolerant parallel data processing. Spark’s in-memory processing significantly speeds up tasks, particularly iterative operations like machine learning. 

It includes high-level APIs such as Spark SQL, MLlib, GraphX, and Spark Streaming for various data processing tasks. Spark’s lazy evaluation feature optimizes performance by executing transformations only when necessary.

Tableau

Tableau is a leading analytics tool known for its intuitive data visualization capabilities. With drag-and-drop functionality, users can create dynamic dashboards and reports, transforming raw data into insightful visualizations. 

Tableau integrates with Hadoop, Spark, and other databases, enabling seamless data analysis across diverse sources. It also scales efficiently to handle large data volumes typical in big data environments.

Big Data Testing & Data Versioning In Practice

Tools like lakeFS let you test your ETL pipelines directly as if you were testing against production data – but without ever wasting time and energy on copying or anonymizing it.

Using lakeFS, you can create an environment that’s exactly like your production – it’s just as complex, and massive, and includes the same configurations. All of this is thanks to a data versioning mechanism.

How Does Data Versioning Help In Big Data Testing?

lakeFS is an object-based file system that sits on top of cloud storage and provides Git-like capabilities (merge, branch, revert, and commit) via an API, a command line interface, or a graphical UI. 

So, the ecosystem of tools that you have can either access the storage as usual or through lakeFS to get versioning capabilities. The only difference is that before you accessed a collection in a bucket – Amazon Web Services Simple Storage Service (S3), Azure Blob Service (ABS), or any object storage that supports the S3 protocol – now you include a name of a branch or a commit identifier. For example, main, prod, gold, or whatever you call your production data branch.

How Does the Zero-Copy Mechanism Work? 

Let’s look at how lakeFS works under the hood and manages metadata. Every commit in lakeFS is a collection of pointers to objects. Since the object store is immutable, you can copy on write. When a new object is created, the next commit will point to that new object.

Files don’t change between commits, so you’ll have multiple commits pointing to the same physical object. This is very useful for developing in isolation against production data. If you want to create an identical environment to production and then create a branch, you can branch out in milliseconds because lakeFS doesn’t copy the data.

Many lakeFS users have massive lakes that count petabytes of data. But they can branch out in milliseconds because it’s a metadata-only operation. 

What does it mean for testing purposes? That you can easily create 20, 50, or 100 test environments without copying any physical objects in the production environment. This leads to serious savings on storage fees by 20 to 80%. This is just one of several benefits of data versioning for this use case.

Advantages Of Using lakeFS In Big Data Testing

Reduction Of Storage Costs By 20-80% 

When you use lakeFS, you can test your applications and operate them in your data lake without ever copying an object. Each developer has a fully functioning copy of the production environment while saving on storage costs.

Furthermore, data lakes retain most of the same files for long periods of time, and only a subset of files changes on a regular basis. lakeFS provides deduplication for the entire data lake over time.

Increased Engineering Productivity

Applying engineering best practices allows engineers to be more effective and less frustrated. They can quickly get the environment they need, work on it, and discard or merge it in seconds.

99% Faster Recovery From Production Outage

If something undesirable happens in your production environment, you can easily go back to the last known good state of your entire cluster in milliseconds. 

Conclusion

You can get all these benefits through our open-source solution hosted locally or our managed hosted solution where we provide service level assurance and other advantages.

Take lakeFS for a spin in the lakeFS playground to see how it works for use cases ranging from big data testing to developing ETL pipelines in isolation.