Five Tips for Reducing Big Data Architecture Complexity

August 6, 2018

Big data provides a flood of information to enterprises, but it requires a high level of processing and analysis to be useful for detecting customer behavior patterns or inventory backorder problems. For many organizations, big data holds a lot of promise, but the necessary architecture creates new complexity.
Big data architecture is designed to handle the flow of data that’s too large and complex for a regular database, but over time this architecture has introduced its own level of complexity due to a variety of factors.
As enterprises try to handle different types of data on different platforms and choose both on-premise and cloud platforms, they are faced with the challenge of integrating data for meaningful use. Otherwise, each compute cluster that requires its own engine will be separated from the rest of the architecture, leading to data fragmentation and duplication. Here are a few tips for avoiding this complexity:
Choose a single platform. To avoid users pulling different and inconsistent data, use a single platform. This might be an on-premise, cloud or hybrid solution, but choose only one to avoid making business decisions without accurate, reliable data.
Limit the amount of active data. Think about the data you need to access for analysis and then archive or eliminate the remaining data. This limits the number of files that algorithms are sorting through to produce your reports.
Plan for a cloud solution. Even if you’re using on-premise now, you should be making plans to move to the cloud eventually. As big data grows, the cloud will be better equipped to handle the processing. While enterprises in some industries will always have some security barriers due to regulations regarding privacy protection, for most enterprises, cloud will be the best solution.
Include disaster recovery in your plans. When you sign on with a cloud provider, make sure that disaster recovery is included in the contract. The plan should be tested to detect any potential problems. You don’t need to be able to retrieve every piece of data, but there is a subset that the solution will minimally require.
Plan for sandbox areas. When it’s time to test a financial risk analysis or play with new product development, you need ample sandbox areas so that concepts can be refined before they begin interacting with your larger infrastructure. This is another example of where cloud platforms excel, because you can scale up and down to create sandbox space when needed for projects.
When you’re planning for a simplified big data architecture, contact us at Access Tech. You need a partner that understands the kind of cost-effective performance that will meet your business objectives today and into the future.

For more insights on this topic: