Currently, organizations are generating and ingesting more data than ever before. Competing with actual currency, data has grown to become one of the greatest assets to companies worldwide. However, to truly reap the benefits of consuming data, it is crucial to make informed and data-driven decisions. Without a structured data analysis strategy, large quantities of data are about as useful as a room full of Legos. AWS provides a range of services to assist with data analysis, and the AWS Serverless Data Analytics Pipeline Reference Architecture is a blueprint for building data analytics pipelines on the AWS platform.

In this blog post, we’ll take a closer look at the key principles of designing data analytic solutions in AWS, as outlined in the AWS Analytics Lens: Well Architected Framework.

Start with a clear business problem

The first step in designing a data analytic solution is to clearly define the business problem you’re trying to solve. This will help you to focus on the specific data and analytics capabilities that you need, ensuring that your solution is aligned with your business goals.

Design for scale

As your data volume grows, your data analytic solution needs to be able to scale to meet the demands of your business. AWS provides a range of services that allow you to scale your data analytic solution as needed, including Amazon S3 for data storage, Amazon Redshift for data warehousing, and Amazon EMR for large scale data processing.

Choose the right data storage and processing options

There are many options for storing and processing data in AWS, and it’s important to choose the right options for your specific use case. For example, Amazon S3 is a great option for storing large amounts of data, while Amazon Redshift is optimized for fast querying and analysis of structured data.

Secure your data

Security is a critical consideration when designing data analytic solutions. AWS provides a range of security features to help you secure your data including encryption, access control, and network security.

Monitor and optimize your solution

Once your data analytic solution is in place, it’s important to monitor its performance and optimize it as needed. AWS provides a range of tools and services, such as Amazon CloudWatch and Amazon QuickSight, that allow you to monitor and optimize your solution.

Cost Optimization

Cost is an important consideration when designing data analytic solutions, and AWS provides a range of cost-saving options, such as Amazon S3’s infrequent access storage class and Amazon Redshift’s auto-pause and auto-resume features.

By following these principles, you can design a data analytic solution that is scalable, secure, and cost-effective, one that meets the needs of your business.

AWS Data Analytic Lens Reference Architecture

Conclusion

By following these principles, you can design a data analytic solution that is scalable, secure, and cost-effective. For more in-depth information, I highly recommend reading the full AWS Analytics Lens: Well Architected Framework. It provides a comprehensive set of best practices for designing data analytic solutions on the AWS platform.

As an AWS Premier Tier Partner here at Triumph Tech, you can take advantage of experienced and highly knowledgeable engineers and solutions architects who can assist you with designing and implementing a purpose-built, event-driven solution guaranteed to meet your business goals. Furthermore, as a premier AWS partner, we have access to exclusive funding options that can help pay for your solution! Reach out today for a free consultation!

Happy building!

Shane Garnetti, Customer Solutions Architect