Automated Data Tiering

Definition of Automated Data Tiering

Automated Data Tiering is a storage management technique that dynamically moves data between various storage devices based on its access frequency, performance requirements, and cost factors. This process automatically classifies and relocates data to the most appropriate storage tier, such as high-performance SSDs, mid-range hard drives, or lower-cost cold storage. As a result, it optimizes storage usage, reduces costs, and improves overall system performance.


The phonetics of the keyword “Automated Data Tiering” can be transcribed with the International Phonetic Alphabet (IPA) as:/ˈɔː.təˌmeɪ.tɪd ˈdeɪ.tə ˈtɪər.ɪŋ/

Key Takeaways

  1. Automated Data Tiering optimizes storage resources by dynamically moving data between different types of storage tiers based on performance needs and access patterns.
  2. It helps organizations to reduce costs, improve overall storage performance, and efficiently manage storage capacity by automatically placing frequently accessed data on high-performance storage and less frequently accessed data on lower-cost storage tiers.
  3. With seamless data migration, Automated Data Tiering ensures minimal impact on application performance and simplifies storage management for IT administrators, allowing them to focus on more strategic tasks.

Importance of Automated Data Tiering

Automated Data Tiering is an essential technology term primarily because it optimizes the management and allocation of data storage resources, improving overall system performance and cost-efficiency.

By automatically and intelligently moving data between storage tiers based on its usage and access patterns, Automated Data Tiering ensures that the high-priority or frequently accessed data is stored on fast, high-performance storage systems, while rarely accessed or less critical data is moved to more cost-effective, lower-performance storage systems.

This results in streamlined data storage management for organizations, reducing both operating costs and time spent on manual data organization.

Moreover, Automated Data Tiering is vital for today’s data-driven world where efficient handling of massive data volumes is crucial for timely decision-making and a competitive edge.


Automated Data Tiering is a powerful technology designed to optimize the storage of data across various media types by prioritizing their access depending on the frequency of usage. The primary purpose of this technology is to improve efficiency and performance in data centers while simultaneously reducing costs associated with data storage.

By dynamically organizing data based on its significance and usage demands, automated data tiering ensures that business-critical information and frequently accessed data are housed on high-performance storage tiers, while less critical and infrequently accessed data are moved to cost-effective, higher capacity storage tiers. This intelligent storage management strategy offers substantial benefits to organizations that deal with vast amounts of data.

One key advantage is the ability to allocate storage resources more effectively, promoting the growth and agility of an organization’s data infrastructure. Automated data tiering also contributes to improved storage performance, as it enables rapid accessibility of frequently used data, thereby streamlining data retrieval processes and enhancing overall productivity.

Additionally, by relocating infrequently accessed data to more affordable storage solutions, organizations can significantly reduce expenses associated with data storage. Ultimately, automated data tiering leverages a flexible and resourceful approach to data management, supporting organizations in maximizing the value of their storage investments while maintaining the desired levels of performance and accessibility.

Examples of Automated Data Tiering

Pure Storage FlashArray: Pure Storage is a leading provider of data storage solutions that utilizes Artificial Intelligence (AI) and machine learning algorithms to enable automated data tiering in their FlashArray series. FlashArray runs the Purity Operating Environment, which employs data reduction technologies and QoS (Quality of Service) techniques to categorize and place data according to its priority and access needs. The result is a cost-effective storage solution that provides high performance and low latency for frequently accessed data while storing less frequently accessed data on lower-cost, high-capacity storage media.

Dell EMC PowerStore: Dell EMC’s PowerStore is a flexible and scalable storage solution that utilizes automated data tiering to optimize storage resources and enhance system performance. PowerStore intelligently and automatically tiers data between different types of storage media based on access patterns and usage. The system uses a combination of advanced algorithms and machine learning to analyze the data and determine the best tier for each data block. This adaptability provides improved performance, maximizes storage efficiency, and significantly reduces operational costs.

IBM Easy Tier: IBM’s Easy Tier is a storage technology implemented in their enterprise storage systems, such as the Storwize and FlashSystem series. Easy Tier uses built-in analytics and monitoring tools to automatically and dynamically move frequently accessed data to high-performance storage tiers, while placing rarely accessed data on lower-cost, high-capacity storage mediums. This automated data tiering solution helps organizations significantly improve storage efficiency, performance, and overall cost management.These real-world examples showcase how automated data tiering technology is being used in various industries to optimize storage, enhance performance, and reduce costs. By making intelligent decisions about data placement, these systems allow organizations to more effectively manage their ever-growing data volumes and storage requirements.

Automated Data Tiering FAQ

What is Automated Data Tiering?

Automated Data Tiering is a storage management technique that automatically moves data between high-cost and low-cost storage media based on the predefined policies and usage patterns. This system ensures efficient utilization of storage resources and reduces overall storage costs.

How does Automated Data Tiering work?

Automated Data Tiering systems analyze the usage patterns of the stored data. Based on certain rules and policies, frequently accessed (hot) data is moved to high-performance storage tiers, while less frequently accessed (cold) data is moved to lower-cost, slower storage tiers. This process optimizes storage allocation and improves the performance of frequently accessed data.

What are the benefits of Automated Data Tiering?

Some benefits of Automated Data Tiering include:

  • Improved storage efficiency and cost optimization
  • Better storage performance for frequently accessed data
  • Reduced manual intervention in storage management
  • Customizable data management policies based on specific organizational needs
  • Extended lifespan of storage devices through efficient load balancing

What are the common tiered storage options used in Automated Data Tiering?

Common storage tiers used in Automated Data Tiering include:

  • Tier 1: High-performance storage, such as SSDs or flash arrays.
  • Tier 2: Mid-range storage, such as 10,000 RPM HDDs or hybrid storage arrays.
  • Tier 3: Low-cost and high-capacity storage, such as 7200 RPM HDDs, tape, or cloud storage.

What factors should be considered when implementing an Automated Data Tiering system?

When implementing an Automated Data Tiering system, consider the following factors:

  • Understanding the data usage patterns in the organization
  • Establishing appropriate tiering policies and rules based on business requirements
  • Choosing suitable storage media for each tier based on performance and capacity requirements
  • Monitoring and adjusting the tiering system as needed for optimal performance and cost savings
  • Ensuring data security and compliance throughout the tiering process

Related Technology Terms

  • Storage Tiering Policies
  • Input/Output Performance Optimization
  • Data Migration
  • Hot, Warm, and Cold Data Classification
  • Block-Level Data Analysis

Sources for More Information


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