In data processing, many projects call for ingesting or processing information in batches. The goal is to minimize the amount of human intervention necessary, allowing analysts and data scientists to focus on developing insights rather than plugging away at getting data. However, this presents several notable challenges so let's look at what you should expect.
Foremost, batch data processing engineers need to devise ways that allow them to automate various tasks. A classic batch data processing problem is scraping websites for information. While there are tools built for this job, they often require fine-tuning. Many companies employ data processing consultants to help them select, configure, and test automated systems. They can assess issues that might catch automated processes off guard so their clients won't discover problems weeks or months later.
Data processing advisors usually advise clients to run their jobs in parallel. This means using multiple processor threads or even machines to break up the tasks. Done properly, parallelization can make batch data processing significantly faster. However, you'll need to ensure that all of the batches are broken up and run the right way, and then you'll have to assemble the output without losing anything.
Designing systems to do this work can be complex, but the payoffs are massive once everything is working smoothly. Suppose your firm provides payroll services for hundreds of clients. Parallelizing the process means getting checks out sooner and being able to commit extra time to verification.
All of these issues are inherently complex, and that means quality control is critical. Remember, just because a process runs without errors doesn't mean it ran properly. Given data processing is largely unattended and involves large batches, you have to deploy quality control software to recognize patterns and flag problems. At the same time, data processing engineers will want to randomly sample output files to confirm that the output is clean and accurate.
Many data processing tools rely on multiple systems to ingest and process information. Consequently, you will have to integrate those systems into your larger setup. This may be as simple as installing the right plug-in for your processing software to connect with an API and a database, or it could be as complex as coding several new gateways.
You will also need to make decisions about platforms and software stacks. For example, a company might elect to deploy its data processing systems to the cloud using a specific combination of an operating system and applications. These decisions will affect the system's maintenance, scaling, security, speed, and stability.
For more information, contact a local company like Data Science & Engineering Experts.