Big Data has passed the tipping point. It’s no longer a fringe interest being practiced by data scientists. The Big Data industry is currently on track to be worth $77 billion by 2023.
There’s a saying in programming and data analysis – “Garbage In, Garbage Out.” That is to say, your data analytics are only as good as the data that fuels them. This is why Big Data testing is so important.
Testing your Big Data by hand rather defeats the purpose of data-driven strategies in the first place. It might not even be possible to assess all of your data depending on how there is. That’s where Big Data testing tools come into play.
Guide To Big Data Testing
Back in 2018, 92% of businesses reported wanting to incorporate Big Data automation testing tools by 2020. Clearly, this is something that’s been on tech-savvy business owners’ minds for some time. Luckily, with today’s Big Data testing tools, this is more feasible than ever for businesses of all sizes.
Data testing is fairly simple and straightforward for routine data applications. Repetitive business practices like forms are highly predictable. A simple program would likely be enough to catch any potential errors in structured data.
Much business data is unstructured or semi-structured. It is estimated that around 80% of data collected by businesses is either unstructured or semi-structured like JSON.
Here are some steps you can take to incorporate an automated Cloud Big Data testing tool in your data pipeline.
Incorporate an ETL Testing Tool
At the beginning of your data pipeline, it’s highly recommended you incorporate an extract, transform, and load (ETL) testing tool. An ETL testing tool can be configured to monitor an incoming data stream for data relevant to your business.
Once this data is gathered, an ETL testing tool will transform the data into a format suitable for your Big Data cloud platform. Once it’s clean, it’s loaded into your data analytics environment.
Implement A Big Data Testing Strategy
You’ll also want to put a solution in place to make sure your Big Data testing tools are functioning properly. This presents certain challenges when dealing with the monumental amount of data that Big Data involves.
A Big Data testing strategy usually involves putting conditions in place to make sure you’re getting the data you need. Some examples of common data constraints could include:
Trying to assess every byte of data could slow your Big Data analytics down to a crawl, however. You’ll also want to decide on the scope for your testing as representative of the entire body. You might test every 10th entry, for instance, and have a subroutine in place of errors rising above a certain rate.
Big Data Testing is Critical
Structure Each Source
To get the most accurate Big Data testing, you should configure each data entry point to make sure the data is configured correctly. Say you wanted to collect data from your blog for analysis. Examples of data you might collect from blog posts might include:
- Publication data
- Time published
- SEO metadata
- Social shares
You should spend some time figuring out where you want to collect data from when you’re compiling your data testing strategy. Once you’ve got a list of where your data is coming from, you should then think about what data you want to harvest from that particular source.
Taking the time to answer these questions will help you set up your ETL tool properly. When all of these steps have been handled correctly, your Big Data pipeline can truly deliver automated insights!
Some of your data streams are likely to contain repeat data or monitor the same assets. Leaving all that data unstructured is going to bog down your data analytics platform significantly. You might want to implement an additional abstraction layer for additional processing.
Say you’re analyzing temperature data from a list of cities. This data might be entered as a pair, as is often the case, with the name of the city acting as the key and the temperature as the value.
Depending on where this data is coming from, these values could be returned at a specified rate. Or if it’s coming from a scientific sensor it might be a string of continuous data. You’ll want to determine the scope of the data you want returned to your testing platform, for starters.
Setting up an additional layer for each city makes this problem relatively simple to solve. All of the data for that particular city would be returned to that city’s specific layer. You can put additional constraints in place to make sure the data is in a usable and useful form.
Say you put a filter in place to only return the highest and lowest temperature from a particular city. Now it doesn’t matter if it’s a continuous stream from a sensor or collected periodically. It ensures that all of your data will work nicely together.
It also makes it so that your Big Data testing platform can receive data from however many sources you like. This is essential for making your Big Data testing solution scalable and adaptable to any solution you apply it to!
These are just a few things to keep in mind to illustrate how the right data testing tools and the proper foresight sets you and your data-driven business up for success. It ensures your data is formatted properly no matter how much there is or where it’s coming from.
Are You Looking For Big Data Testing Tools?
Big Data is quickly making science fiction become science fact. Disciplines like machine learning and artificial intelligence were still in the realm of sci-fi even 10 years ago. Now they’re available for anybody to benefit from!
If you’re ready to find out how data-driven tools like Big Data testing can empower you and your business, Sign Up for a Demo today!
Established in the year 2010 with the mission of building trust in enterprise data & reports. Datagaps provides software for ETL Data Automation, Data Synchronization, Data Quality, Data Transformation, Test Data Generation, & BI Test Automation. An innovative company focused on providing the highest customer satisfaction. We are passionate about data-driven test automation. Our flagship solutions, ETL Validator, Data Flow and BI Validator are designed to help customers automate the testing of ETL, BI, Database, Data Lake, Flat File, & XML Data Sources. Our tools support Snowflake, Tableau, Amazon Redshift, Oracle Analytics, Salesforce, Microsoft Power BI, Azure Synapse, SAP BusinessObjects, IBM Cognos, etc., data warehousing projects, and BI platforms.