What Are The Challenges Of Batch Data Processing?

Batch data processing is an essential part of many operations. Any organization that's collecting large amounts of data in batches is bound to encounter some challenges along the way. Batch data processing engineers encourage their clients to prepare for these four issues.

Formats and Encoding

Ideally, all the data would be conveniently formatted and encoded. However, there are numerous formats, and these often include further versions of encoding. Even something as seemingly simple as opening a CSV file on a remote server can lead to encoding challenges based on the use of delimiters, text formats, and even unsupported characters. Batch data processing engineers need to account for reading everything and exporting it to a supported data format.

Timing and Scheduling

Many batch fetches of data occur on schedules. Similarly, they often require timing systems. For example, a vendor might limit the number of fetches per second. Likewise, the vendor may have a release schedule. If you want to orchestrate data collection as quickly as possible following each release, you'll need to engineer a solution that accounts for timing and scheduling. Also, the system will need checks to ensure that it reschedules missed batches once connectivity or availability issues clear up.

Orchestration

Batch data processing ultimately requires you to orchestrate many different components. Once you've sorted out your sources and their related concerns, you'll need to address the challenge of connecting data storage, analysis, reporting, and backup processes. Likewise, you'll need to have quality monitoring and control processes in place. The components have to interoperate seamlessly, too.

Architecture

All of these orchestrated components need to operate on top of some kind of architecture. This can get challenging if you need a specific system that might not always play nicely with the rest of a uniform software stack. Batch data processing engineers and their customers need to determine when a stack can do the job and when to implement bespoke solutions.

Similarly, everyone has to appreciate the implications of each choice. Committing to programming languages, databases, and distributed architectures all have consequences for how you can orchestrate batch data processing. You will also have to make a choice about whether you want to host your data on-site, in the cloud, or through some kind of hybrid solution.

The architecture can become more complex if you need to deploy an end product. If you're selling datasets, customers will want dependable and fast access. Similarly, data streaming can impose major resource requirements at the back of your entire process.

Reach out to a company like Data Science & Engineering Experts to find out more.