Many new technologies aim to help developers improve productivity without stressing about the underlying infrastructure. None of these innovations makes this goal a reality better than serverless technologies.
Serverless architectures allow developers to focus on driving innovation rather than on the underlying infrastructure. However, while there's no denying the benefits of serverless technology, there are challenges with serverless monitoring, especially when it comes to unstructured data.
Typical Predefined Criteria Used to Handle Unstructured Data
Millions of terabytes of data are generated every day. In a perfect world, this data would be well organized in relational databases. In reality, even though big data is changing everything, 80% of it is unstructured.
Unstructured data can be wildly useful, of course. At the same time, developers must spend ample time organizing it into sensible data. Depending on the nature of the unstructured data, an expert can use custom codes and regular expressions or advanced algorithms to derive practical facts. Below are some of the typical serverless-based criteria that most teams use to get valuable info or draw meaningful conclusions from unstructured data:
- Visualization - Many organizations rely on predefined graphical representations of information such as charts, graphs, and pies.
- Text Analysis - Text analysis is another example of a predefined solution.
- Data Lakes - Data lakes store both non-relational and relational information. The main driver behind their popularity is that they deliver query results at a highly efficient rate, yet they use low storage hence cutting on costs.
How to Determine the Right Predefined Criteria
The above techniques are just some of the main criteria that teams use to extract valuable information from unstructured data. New techniques and approaches evolve, which begs the question, how can teams determine what the ideal predefined criteria should be? Even though there's no direct answer, the right approaches can be determined based on factors such as:
- Nature of the Data
Unstructured data occurs in different forms. From simple images, text, and emails to fingerprints and complex statistical figures. For stats and the like, visualization techniques such as graphs might be the best criteria to use while for text and emails, text analysis might work better.
- The End Goal
Businesses usually have different goals when it comes to data. For instance, some are on a quest to find out what their customers want while others use big data to lower costs. Teams should consider the ultimate goal when determining the appropriate predefined criteria to use when dealing with unstructured data.
- The Data Source
Unstructured data is everywhere. From human-generated sources such as social media and website content to machine-generated sources such as satellite images and log data. The data source is critical when it comes to selecting the right criteria to use in extracting sensible info from the raw data.
Serverless Monitoring Challenges with Unstructured Data
Below are some of the challenges brought on by serverless monitoring:
Serverless Functions are Single Threaded
Unstructured data requires advanced tools and a top-level of technical expertise to make sense. That said, one of the main challenges with unstructured data in a serverless environment is that serverless systems are single-threaded. This fact typically means they are quite slow while raw data processing requires parallelism.
In a serverless architecture, the cloud provider dynamically deals with the implementation, maintenance, and anything else related to the underlying infrastructure. While this approach certainly removes a lot of the burden from development teams, it's also a weak point as it means that they're stateless. In turn, running unstructured data on serverless functions can be quite slow.
There's More to Monitoring
Monitoring unstructured data on a serverless architecture goes beyond setting performance, speed, and timeout alerts. In other words, monitoring raw data in a serverless environment means ensuring you're aware of what you need concerning observability, especially if the patterns in the data are expected to change over time.
Other Challenges with Serverless Monitoring
- Serverless functions do no support GPUs, yet GPU is of the essence when it comes to important metrics used in parsing unstructured data.
- Serverless architecture limits the image size, yet images are an incredibly valuable source of unstructured data.
In a nutshell, serverless architecture is a game-changer. It has helped revolutionize development in a way that no other technology has done. Nonetheless, it does pose some challenges, especially when it comes to monitoring. The good news is that advances in technology are making it better suited for unstructured data.
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