Glossary

Our Criblpedia glossary pages provide explanations to technical and industry-specific terms, offering valuable high-level introduction to these concepts.

Table of Contents

Related Terms

Return to Glossary

Data Tiering

Data tiering is an intriguing concept in the digital world. It is a total game-changer for handling, accessing, and storing massive amounts of information. Everyone’s hopping on the data-tiering bandwagon from small start-ups to mega corporations. But what’s the deal with this concept? Let’s dive in and break it down.

What is Data Tiering?

Data tiering is a data management strategy that involves organizing and storing different types of data based on their usage patterns. It consists of moving less frequently used data, or cold data, to lower levels of storage or tiers. This process allows organizations to optimize their storage systems and infrastructure, reduce costs, and improve overall performance.

Originally, “data tiering” comes from shifting data between different storage levels in a storage system. Nowadays, it includes transferring or storing data between other cloud or storage systems, facilitating seamless movement and accessibility.

Data tiering has two primary classifications.

Hot Data
Hot data refers to an organization’s most critical and frequently accessed data. It includes real-time transactional data, such as customer information and financial records. This data must be readily available for fast access and processing to support business operations.

Cold Data
Cold data refers to infrequently accessed or inactive data not regularly used in day-to-day operations. This may include historical records, logs, and backups. While this data is still essential to keep, it does not need to be stored on high-performance storage systems.

Tiered Data Storage
Data tiering involves categorizing data into different tiers based on its importance and usage. This allows organizations to store data cost-effectively using various storage technologies, such as SSDs for hot data and tape or cloud storage for cold data. With tiered data storage, organizations can ensure that their most critical data is stored on high-performance systems while less important data is stored on lower-cost solutions.

How does Data Tiering Work?

Data tiering uses a policy-driven approach to automatically move data between tiers based on its usage. This process is typically performed by software-defined storage solutions that constantly monitor and analyze data usage and then move it to the appropriate tier.

First, data is categorized into hot and cold tiers based on usage. Hot data remains on high-performance storage systems for fast access, while cold data is moved to lower-cost storage solutions.

Next, data can be further classified within each tier based on its importance or sensitivity. This helps organizations prioritize data storage based on performance and cost, optimizing system resources effectively.

Finally, data is continuously monitored and analyzed to ensure it is in the most appropriate tier.

Data Tiering in the Cloud

Data tiering has grown beyond just moving data into one storage place. Now, it’s about saving or moving data not only in one storage spot but also in different clouds and systems. Cloud Data Tiering has gained popularity as customers explore tiering or archiving their data to a public cloud. Public cloud storage offers a variety of storage options, including object and file storage classes like Amazon S3 and Azure Blob (Azure Storage), which provide cost efficiency and the benefits of object storage without the complexities of setup and management. For instance, data can be archived or tiered from a costly analysis system to a lower-cost storage tier like Amazon S3, where it can still be searched easily with a federated search solution. Tools like Amazon Glacier can be used for longer-term storage, but know that this storage tier is for data you’ll likely never need to use in the future – even in the event of a breach. However, to achieve this level of flexibility and avoid treating the cloud as a mere storage locker, tiered data must be easily accessible natively in the cloud without relying on third-party software. This emphasizes the importance of file-tiering over block-tiering for effective data management in the cloud.

Implementation Considerations

Implementing DDAA involves integrating with existing data systems, establishing strong data governance, and ensuring quick search capabilities for efficient data retrieval.

Integrated Systems
DDAA integrates with your current data systems, ensuring that you can manage and access your data without major overhauls to your existing IT infrastructure.

Strong Data Governance
It’s crucial to have clear rules and procedures for managing your data with DDAA, to keep your archived data both safe and functional.

Quick Search Capabilities
DDAA comes with powerful tools to find and retrieve data efficiently, so your archived data can be found and used with ease.

Adopting DDAA isn’t just about upgrading storage—it’s a strategic move. It ensures that your business has a full historical record at its fingertips, ready to guide future growth and decision-making.
Benefits of Data Tiering
The benefits of data tiering stretch far beyond just cost savings. It’s a strategic approach that helps businesses optimize their data storage systems, boost operational efficiency, and, ultimately, drive business growth. Let’s delve into the benefits that data tiering can bring to your organization.
  • Cost Savings: By storing cold data on more affordable solutions, businesses can lower their storage costs significantly. This paves the way for seamless scalability of their storage infrastructure and ensures they don't break the bank in the process.
  • Improved Performance: Organizations can access and process critical data faster with hot data stored on high-performance systems. This helps improve business operations and decision-making.
  • Efficient Data Management: Data tiering allows organizations to efficiently manage their data by automatically moving it between tiers based on usage. This eliminates manual data movement and frees up IT resources for other tasks.
  • Better Data Protection: By categorizing data based on importance, organizations can ensure that their most critical data is always stored on the most secure and protected systems. This helps mitigate the risk of data loss or corruption.
Want to learn more?
Download our white paper on Why Log Systems Require So Much Infrastructure (and Three Ways to Fix Them).
Download White Paper

Resources

So you're rockin' Internet Explorer!

Classic choice. Sadly, our website is designed for all modern supported browsers like Edge, Chrome, Firefox, and Safari

Got one of those handy?