The global landscape is currently amidst a digital transformation that’s pushing the boundaries of data processing and management
According to the association for computing machinery,
‘Software developers spend 35-50 percent of their time validating and debugging software.’
And we can only assume data engineers as they need to debug both code and data, are spending even more. With the growing popularity of streaming data sets, the volume and velocity of data is increasing, and with it, the amount of code requiring debugging itself. The need for methods to reduce these numbers as we modernize our data architectures has become of the utmost importance – but how can this be done?
Debugging Can Apply To Many Things
The google definition of debugging is finding and getting rid of errors in code, but as any engineer knows, debugging is much more complex than that. There’s a huge difference in what is debugging depending on whether you’re debugging software, data pipelines, or, the data itself. Unbeknownst to the masses, data is a beast that requires debugging as well!
With software, we are accustomed to debugging it. We know to consider the code structure, object hierarchy, numerous editors, and multiple code syntaxes. Software engineers have their processes, find the bugs in the code, edit the code, deploy it again, and continue operations.
When these tried and true processes are applied to streamed data like data pipelines the same methods follow suit. Streamed data has a code-based structure, just like software, the difference is it is just more complex. You need to code, deploy and check results, apply some event tracing then go back and debug accordingly until the output needed is achieved. The industry does it with Jupiter all the time and again the process is fairly straightforward.
The data itself, however, requires a pivotal change in thinking as data is one thing that is not coded. With data processing, if the data is flowing as we designed it to do – then we assume it’s got to be right. But this false confidence is the common pitfall many fall susceptible to. We suppose it is accurate but the truth is if you get trash in, trash comes out. There is a depth of knowledge in the data itself that requires scrutinizing outputs and drawing conclusions from plots, trendlines, and more.
The Challenges with Streamed Data
With streamed data, the sheer amount of data that must be taken into consideration screams storage allocation issues. Then we need to consider the computational power and synchronized interplay required for processing at speed and all of our efforts end up there. But what does this mean? It means warehouses are sent terabytes of continuously flowing data that remains unchecked – does the term data swamp ring a bell?
Unchecked, unverified data is useless – what value is streamed data if it lacks accuracy and is of bad quality? The point of gathering data is to extract meaningful insights from the data collected so if the data is noisy, incomplete and the process is error-prone, how can quality data with meaningful insights be extracted?
With streamed data, the most difficult beast to tame is the pinpointing of issues when monitoring and troubleshooting. As we develop our pipelines we get stuck in an end-goal mentality and fail to extract the in-production insights that would normally be attained. Real-time troubleshooting is never on par with production, making problem detection with streaming data similar to finding and fixing issues on a train traveling at 150km/h.
Take the complications of all the aforementioned and you’ve got the beginning of tackling streaming data. It can be a time-consuming, costly, and scrutinizing process – but it doesn’t have to be.
How to Make Data Debugging Simpler and More Efficient
1. Know Your Data In and Out
Some call it data expertise, some data in context, for others it is data understanding but, simply put, it is understanding the data in its entirety. This includes not only what data is entering the stream, but also where it is coming from and its initial format. Achieving complete data understanding means prepping data sets and completing pre-pipeline analysis before data is integrated into flows. By keeping in mind the type and frequency of data entering your pipeline, the goals set for the data itself, and the capabilities required to attain wanted end results – it is much simpler to troubleshoot issues.
2. Having Data Visibility is Key
With so much data flowing from multiple streams with multiple transformations, being able to structure viewpoints is essential. Assuring clear visibility of data assets throughout collection allows easy access to all elements flowing. As a result, common delays between what is in production and what needs to be reassessed are minimized or even eliminated.
3. Check Yourself Before You Wreck Yourself
Once a depth of knowledge and visibility is achieved, make sure to perform constant checkups before and after important transformations. Raw data is easy to lead into production – but in doing so it is not being explored. The issue is compounded if streaming data isn’t analyzed regularly, resulting in easily produced data that is not accurate or helpful.
4. Test in real-time, Fail in real-time, Move in real-time
In most cases, pipelines are designed on a small, controlled scale so that when they are ready to be put into production, it is clear what needs to be modular and what must eventually become production-ready. However, if you don’t understand the in-betweens when moving from small to big, to production – the knowledge gained from this crucial step is forfeited.
It is common to rush the process but in doing so the data richness is lost meaning the information is lost. This loss of information is why debugging takes so incredibly long when something goes wrong. After going to production, data may end up changing or even breaking. To simplify debugging rather than pushing for the fastest move to production – take the time and better prepare now by designing indicators and error traps throughout your pipeline. These indicators can alert you to a particular area of your pipeline, in real-time, easily identifying where and when bugs occur, as well as when something with the data goes wrong.
The Key Takeaways to Simplify Debugging Streamed Data
Working with streamed data doesn’t need to be a time-consuming headache. By taking into consideration the difference between the multiple facets that streamed data represents, from tackling code and non-code structured elements to understanding the sheer velocity and volumes with real-time capabilities – this beast is actually tameable.
Remember that the data itself needs to be debugged, data should be known in and out, processes need to be visible, constant checks should be undertaken and tests need to be done continuously so that any failures occurring in real-time can be strides forward as well.
Now a solution to this conundrum exists with a means to develop, maintain and debug ETl/EtLT data pipelines by allowing for the aggregation of data in real time. With a remedy that helps to guarantee exceptional data quality by providing high throughput and low latency while assisting with data pipeline scaling through complete visibility by exposing data changes in real-time – debugging just became a whole lot simpler.
Check out Datorios’ data debugger for yourself and let us know your thoughts.
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