For many companies, one of the key motivations behind migrating from one cloud platform to another, or from on-premises storage to the cloud, is cutting operational costs and overall business expenses. This is feasible, but data migration to the cloud without a strategy will not generate savings by itself. However, as they rush to leverage the cloud's potential, they often find data storage costs spiraling out of control.
Migrating to Amazon AWS promises flexibility, scalability, and reduced operational overhead, so many businesses choose this provider for cloud migration. If you’re considering this step, you have to carefully analyze the available options as well as the associated data migration costs. Otherwise, you’ll risk overlooking some of the cloud cost optimization opportunities.
In this article, we’re delving into the strategies and best practices for optimizing data storage costs during AWS migration.
AWS storage options explained
To succeed at AWS cost optimization, it’s necessary to become aware of the cloud storage options available to you. AWS offers a wide range of storage options to cater for the different needs in terms of storing and retrieving data. These include:
- Amazon Simple Storage Service (Amazon S3): this is a scalable and highly durable object storage service. It works best when storing and retrieving large amounts of unstructured data, such as images, videos, backups, and logs. S3 offers different storage classes with varying performance and cost characteristics – we explain more about the costs in the next section.
- Amazon Elastic File System (Amazon EFS): this system offers scalable and managed file storage that you can mount to multiple EC2 instances. It's ideal for those use cases that require shared access to files across multiple instances, such as development environments or content management systems.
- Amazon Elastic Block Store (Amazon EBS): the storage volumes provided by EBS can be attached to Amazon EC2 instances. It's designed for storing data that requires low-latency access and is often used for running databases or applications that require continuous storage.
- Amazon File System (Amazon FSx): fully managed file storage systems that are compatible with Windows File Server and Lustre. It works best for applications that require shared file storage with specific compatibility requirements.
- Amazon File Cache: a caching layer for accessing shared Windows file systems. It’s ideal for those applications that require access to large amounts of shared data by providing a cache that stores frequently accessed data closer to the application instances, hence reducing latency.
- AWS Storage Gateway: a hybrid cloud storage service that enables on-premises applications to seamlessly use Amazon cloud storage. It supports different storage protocols and provides options for file, volume, and tape-based storage.
- Amazon Relational Database Service (Amazon RDS): while not purely a storage service, Amazon RDS offers managed relational database instances. It includes storage as part of its offering, providing scalable and managed storage for databases like MySQL, PostgreSQL, Oracle, and others.
- Amazon DynamoDB: a fully managed NoSQL database service that also includes storage as part of its capabilities. It offers seamless scaling, high availability, and low-latency access, making it ideal for applications that require a highly responsive database, such as SaaS products.
How does AWS price data storage?
AWS uses a pay-as-you-go model, where you're charged based on your actual usage. The available pricing for storage options is typically based on several factors, including:
- Type and class of storage service: with AWS, different storage types and classes have varying costs. For example, Amazon S3 has different pricing tiers based on its storage classes like Standard, Intelligent-Tiering, Glacier, etc. Higher performance or low-latency storage options may be priced higher than slower, archival options.
- Amount of storage consumed: customers are charged based on the amount of storage space they use. This is usually measured in gigabytes (GB) or terabytes (TB). The more storage you use, the higher the costs you’ll have to cover.
- Data transfer and retrieval rates: moving data in and out of AWS storage can also incur costs. It may include data transfer between different AWS regions, as well as data transfer out of AWS to the internet or other networks. Retrieving data from certain storage classes may have specific retrieval costs (that’s the case with Glacier, for instance – we provide more details in the following section).
- CPU usage: Amazon charges separately for cloud usage and compute power necessary to access the data. Different storage types will offer different storage configurations, so you should consider them before making any decisions with regards to Amazon cloud storage.
- Data durability: services like Amazon S3 provide high durability and availability. The cost of this durability and redundancy is factored into the pricing.
- Additional features or options: some storage services offer additional features like encryption, versioning, or analytics, and may have their own associated costs.
In addition, when calculating the total cost of Amazon cloud storage, you may want to consider the following:
- Number of requests and operations: some storage services charge based on the number of requests or operations you perform. For instance, Amazon S3 charges for PUT, GET, and other operations.
- Regional availability: AWS pricing can vary based on the specific AWS region and availability zone you choose for your storage resources.
- Data lifecycle management: for services like Amazon S3, if you use features like lifecycle policies to automatically transition objects between storage classes, there may be associated costs.
- Backup costs: creating and managing backups of your data can contribute to your overall storage costs.
- Data transfer acceleration: Services that offer data transfer acceleration, like Amazon S3 Transfer Acceleration, may have additional costs.
With the variety of storage and pricing options, it's critical that you adjust your usage to ensure you're getting the most for your budget when migrating data to the cloud. In addition, you should regularly monitor it and check AWS’ website for the most up-to-date pricing information, as it can change over time.
Best practices for AWS cost optimization
When migrating to AWS, you have to balance between maintaining good performance and stability of your solution and optimizing data storage costs. That’s why selecting the right storage type, understanding cost options and optimizing your data become paramount. Below, we’re presenting a number of best practices that can collectively contribute to generating substantial cloud cost savings while maximizing the value of AWS storage services.
Data deduplication and clean-up (before & after migration)
Even though this seems like an obvious step, businesses don’t always remember to properly address it. If you have duplicate or redundant data, storing them in the cloud is nothing but a waste of budget. Focus on redundancy here: any data that doesn’t bring value must go.
If you’re going for AWS cost optimization, decide on what is and what isn’t critical and only store the former in the cloud. Data clutter can easily accumulate over time, so implementing regular data deduplication and clean-up processes to eliminate these redundant or obsolete files. Identifying and removing duplicate content will help you cut unnecessary expenses and enhance data integrity.
Optimizing data structure
AWS cost optimization is about minimizing the volume of data. This can be achieved through optimizing its structure in accordance with how it’s going to be used. This is about choosing the right file formats and compression techniques. The below questions can help you choose suitable formats:
- Do you need to store excess data?
- How will the data be used?
- Do you want to have the ability to search through the data? Or is viewing enough? If you don’t need the search option, then you won’t have to index them, which will save you some storage. If you want the search option, you’ll also have to decide on the types of search you want to have (By keywords? Full text search with analyzer?).
- What data volume are you considering? Will integer type be sufficient or perhaps you should opt for long data type?
Optimizing data structure demands that development teams should be well-informed about how the service will be used right at the design stage. Industry-standard data compression methods can help you strike the right balance between data storage and efficient retrieval.
Optimizing EBS volumes
In general, it’s much easier to add extra storage volumes than remove them (that applies to EBS and other storage options). To estimate your needs accurately, you should of course know the current data volume, as well as data growth rate – this will help you select the right volume type and size that align with your application's performance requirements upon cloud data migration.
If your system is well automated, then the volumes will be easy to add as required. You should avoid over-provisioning of storage space, as it will generate unnecessary expenses. Additionally, monitor your application's workload patterns to fine-tune your storage.
Optimize CPU usage
Here is where performance tests will come in handy: by looking at the traffic volume, you’ll be able to determine how much CPU you’ll need. That should also help you determine whether opting for cache layers instead of CPU won’t be more cost effective for you.
It’s also essential to match your application's CPU requirements with the right instance type for cloud cost optimization. AWS provides a variety of instance types with varying CPU capabilities. Choosing the relevant one will prevent you from overspending on what you don’t actually need.
Monitor and analyze with AWS Cost Explorer
In addition to the detailed documentation available for every service, AWS offers a pricing calculator: the AWS Cost Explorer. This tool allows you to visualize your AWS usage and costs in real time. This means that you can not only regularly monitor your expenses, but also identify trends, anomalies, and potential areas for optimization.
It is a free tool that offers invaluable insights that can help you adjust your storage strategies and configurations as you keep using it.
Opt for Amazon EC2 Reserved Instances
RIs are a highly cost-effective solution for optimizing computing capacity for those clients who can opt to reserve resources for a specified term. In comparison to on-demand pricing, committing to either a one- or three-year period up front allows you to enjoy significant discounts.
If you have consistent and predictable workloads, RIs will be particularly beneficial for you. They offer great stability and an easy option for AWS cost optimization.
Data compression for Elasticsearch
Naturally, compressing data before storing it can lead to substantial cloud cost optimization. Modern compression algorithms allow you to easily reduce the size of files without compromising data integrity.
Less obviously, compression is also available with Elasticsearch. It has a built-in mechanism that allows you to compress data so that it could be stored and managed within its indexes. This mechanism optimizes storage space and enhances query performance, but it may also affect application performance if billions of files are concerned.
Implement hot-warm-cold architecture
Segmenting data based on access patterns is a well-established and effective strategy for cloud cost optimization. This approach involves classifying data into different segments, namely “hot,” “warm,” and “cold,” based on the frequency of usage. Categorizing data in this way allows you to make informed decisions about where and how to store it for optimum performance and cost.
“Hot” data is used frequently, so it needs to be made easily available and responsive. It is generally stored in high-performance storage options with fast read/write speeds or memory caches. Data that will be accessed less frequently can be categorized as “warm” or “cold” data.
Warm data may still require relatively fast access times, but not to the same extent as hot data. It can be stored in slightly lower-performance storage options.
Finally, “cold” data includes only that which will be accessed rarely (or no longer used at all, but must be retained for legal or regulatory purposes) and should be saved on low-cost, long-term storage solutions.
Cloud data migration and AWS cost optimization with RST
Optimizing storage costs on AWS requires a thought-through approach that involves applying best practices to data management, allocating resources in a strategic manner and informed decision-making.
Storage footprint can typically be minimized through deduplication, efficient data structuring, and compression. However, striking a balance between application performance and data accessibility, so that it generates savings, requires knowledge of the system as well as engineering your data appropriately.
As a certified AWS partner, we can help you tailor the cloud data migration process and prepare data for storing it in the cloud in a cost-effective and accessible way. For more information, contact us directly and we’ll get back to you within 24 hours.
