Optimizing Cloud Storage Costs and Performance with Data Tiering

 In our role as cloud architects, we constantly have to contend with ever-increasing amounts of data while keeping infrastructure expenses in check. Applications in today's world depend on data virtually like lifeblood, and yet all data that's generated is hardly created equal. A small set of data gets read and written thousands of times a second, and other data sits idle year in and year out. Supporting all this data on high-performance, high-dollar infrastructure is inefficient and economically unsustainable.

It's here that a data management approach with a clear strategy comes into play. A data tiering consists of a foundation architectural pattern that enables organisations to tier and relocate data to multiple levels of storage according to performance expectations, access frequency, and cost targets. With the successful implementation of a data tiering solution on AWS, you could lower data storage expenses considerably, enhance application performance, and achieve data scalability and compliancy in the long run.

What is Data Tiering?

Data tiering is the practice of categorizing data into logical tiers—often referred to as hot, warm, and cold—based on its value and usage patterns. The core principle is simple: match the data's access requirements with the most appropriate and cost-effective storage infrastructure..

Hot data is mission-critical, heavily read, and has millisecond latency. It's on the highest-performance, and typically highest-cost, storage.

Warm data gets accessed less frequently but still must be highly accessible. It might be kept on a lower-cost medium with somewhat higher latency.

Cold data is not very often extracted, like archives or long-term backups. Cost efficiency and lifespan have a greater priority than retrieval efficiency at this tier.

By automating data migration from hot to warm, and from warm to cold tiers over time based on how old and how important data is, companies can create very efficient and cost-effective data infrastructures.

Why Data Tiering Matters

Implementing data tiering has some significant architectural and business benefits. It's also a piece of good architecture, and it's especially valuable in the Cost Optimization pillar.

Significant Cost Optimization: This is the most direct benefit. Cold storage tiers like Amazon S3 Glacier Deep Archive can be up to 95% cheaper than hot storage tiers like Amazon S3 Standard. For petabyte-scale datasets, this translates into millions of dollars in savings.

Improved Application Performance: By keeping your most-active data on high-performance volumes such as Amazon EBS io2 Block Express or Amazon S3 Standard, you can ensure applications provide high throughput and fast latency that users expect. It avoids less-critical data from using valuable resources.

Improved Scalability: Data tiering enables your storage footprint to scale hugely with no linear cost increase. Scalably maintain historical data at your leisure to perform analytics or satisfy reporting and compliance needs without busting your budget.

Streamlined Governance and Compliance: Most companies have stringent rules that require data to be maintained on-hand for several years. Data tiering allows data that fits such profiles to be stored in non-expensive, durable storage in an orderly fashion such that long-term data retention and legal holds become easy to fulfill.

Architecture Layers: Data Tiers on AWS

AWS offers a complete set of storage solutions that can easily integrate into a tiered architecture. Let's take a look at the typical level-based services you'd utilize.

Hot Tier: For High-Performance, Active Data

Hot tier holds data which should have immediate access. This consists of transactional database, caching, actively viewed web pages and real-time data from analytics.

Amazon S3 Standard: 

This is optimized and suitable for data that's constantly read and written, with high throughput and low-latency object storage. This is good for a wide range of applications including websites, content distribution, and big data analysis.

Amazon Elastic Block Store (EBS): 

Offers high-performance block storage to accompany Amazon EC2 instances. Provisioned IOPS SSD (io1, io2) and General Purpose SSD (gp2, gp3) volumes are suitable for latency-sensitive transactional applications.

Amazon Elastic File System (EFS):

 Provides a scalable, convenient, and managed file system. EFS Standard storage class supports low-latency performance of many types of workloads.

Warm Tier: For Less Regular Access

It's a cost-performance trade that's appropriate to data that's not required on a day-to-day basis that shouldn't endure long holds to access it. It's appropriate for monthly business reports, current logs, and semi-active user content.

Amazon S3 Standard-Infrequent Access (S3 Standard-IA):

Provides the high throughput and durability of S3 Standard with lower per-GB storage cost, and per-GB retrieval cost. This is suitable for long-lived data which is less frequently accessed but still requires single-millisecond access.

Amazon EFS Infrequent Access (EFS-IA):

 A cost-optimized file storage class with lower performance capabilities than EFS-STD and EFS-HA. It lowers EFS storage cost up to 92% compared to EFS Standard.

Cold Tier: 

Reserved for Archives and Long-Term Preservation

Cold tier:

This tier's function is to hold data with very minimal accesses. Its main stimulus is minimum-cost storage. Access times will vary from minutes to hours.

Amazon S3 Glacier Instant Retrieval: 

 Where you require access to data in real-time, like medical imaging or news media content. This provides you with lowest-cost storage along with milliseconds retrieval.

Amazon S3 Glacier Flexible Retrieval:

 A cost-effective solution that works well with archives that do not require real-time access but have flexibility in mind. Data retrieval in 1-5 minutes or up to 12 hours in mass retrieve cases at no additional cost.

Amazon S3 Glacier Deep Archive:

 Lowest cost storage in the cloud. Optimized for data that's read once or twice a year and restores in 12 hours. A great substitute for on-premises tape libraries.

Automation: The Key to Efficient Tiering

It's inefficient and error-prone to transfer data manually between levels. AWS provides top-notch automation tools that make data tiering a "set it and forget it" proposition with architects.

Amazon S3 Lifecycle Policies: 

Rules may be set to move objects automatically from one storage class to another based upon age. A lifecycle rule may, for example, move objects from S3 Standard to S3 Standard-IA after 30 days of creation, and to S3 Glacier Deep Archive after 180 days.

Amazon S3 Intelligent-Tiering: 

This is a game-changer for data with unknown or changing access patterns. S3 Intelligent-Tiering automatically moves your data to the most cost-effective access tier based on actual usage, with no performance impact or operational overhead. It monitors access patterns and moves objects that have not been accessed for 30 consecutive days to the Infrequent Access tier and, optionally, to the Archive Instant Access tier after 90 days.

Amazon EFS Lifecycle Management: 

Amazon EFS Lifecycle Management: EFS Lifecycle Management can also be enabled to move files that have been moved back and forth over a specified period of time from the EFS Standard tier to the cost-optimized EFS-IA tier.

Best Practices for Architects

When planning your data tiering architecture, keep the following best practices in mind:

Get to Know Your Data Access Patterns: 

Prior to creating any policy, understand your data. Access tools such as Amazon S3 Storage Lens to learn much about how you utilize and access your object storage.

Align Tiers with Business Needs:

 Align your storage decisions with your company's Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO). Long RTOs exist on cold tiers, which must be reasonable to the data that they contain.

Default to Automation: 

In variable access pattern data sets, it's a good place to begin with S3 Intelligent-Tiering. Cost optimization occurs automatically with no upfront analysis.

Factor in Retrieval Costs:

Remember that cold tiers incur data retrieval expenses. While storage keeps expenses in check, high-volume retrievals will strip away cost advantages. Estimate your projected retrieval volumes to compute overall cost of ownership.

Review and Refine:

Business requirements change. Regularly review your tier and lifecycle strategies to make sure that they remain suitable for your applications' requirements and cost targets.

Conclusion

Data tiering is no longer a specialized optimization trick; it's an underlying discipline to build cost-effective, high-performance, and scalable designs in the cloud. With the plethora of AWS storage options and sophisticated automation functions like S3 Lifecycle policies and S3 Intelligent-Tiering, architects can dynamically align data value and storage expense in an intelligent and automated way. A data tiering approach will pay your AWS bill while also helping your enterprise keep data growth healthy year over year.

A Blog by : Aditya Kumar B-53 

Co Authors -    

Ankit Singh B-54
Prathamesh Biradar B-52