Ephemeral Storage

What is Ephemeral Storage?

Ephemeral Storage, often referred to as temporary or non-persistent storage, is an aspect of cloud computing where the stored data is actively available only for the lifespan of a particular compute instance. Once the instance is terminated, any data held in the Ephemeral Storage is lost, hence the name, which stems from the term "ephemeral," meaning "lasting for a very short time".

Functionality and Features

Ephemeral Storage provides temporary block-level storage for instances. This storage is ideal for temporary content such as buffers, caches, scratch data, and other content that applications generate and manage. Since it is closely associated with the instance, it ensures quick data access, thereby offering high IOPS (Input/Output Operations Per Second).

Architecture

Ephemeral Storage is typically integrated into the server hosting the instance, either through direct attached storage (DAS) or a network storage system. The storage can be extended by adding more drives or arrays, depending on the specific demands of the application or instance.

Benefits and Use Cases

Ephemeral Storage is advantageous for use cases that require temporary storage of information, such as session data, cache, or any data that does not need to persist beyond the life of the instance. The main benefits of Ephemeral Storage include:

  • High IOPS: Fast data access, due to its proximity to the instance.
  • Cost-Efficiency: It typically comes free with the cloud compute instances.
  • Flexibility: Easy to increase storage capacity as needed.

Challenges and Limitations

Despite its benefits, Ephemeral Storage has some limitations. The most significant is the risk of data loss when an instance is terminated. Therefore, it is not ideal for long-term storage or for storing sensitive or critical information. It's also worth noting that Ephemeral Storage may have limitations regarding size and input/output operations per second (IOPS) depending on the cloud provider.

Integration with Data Lakehouse

In a Data Lakehouse setup, Ephemeral Storage can serve as a fast, temporary storage space for data processing and analytics tasks. It is particularly useful for storing intermediate results in data processing pipelines. However, given its transient nature, permanent storage solutions are required for long-term data preservation in a Data Lakehouse.

Security Aspects

Given its short-lived nature, Ephemeral Storage does not traditionally have robust security measures in place. However, it can be secured by following best practices, such as data encryption and secure instance management. It's also critical to ensure sensitive data is not stored on Ephemeral Storage due to the risk of data loss.

Performance

In terms of performance, Ephemeral Storage provides high IOPS due to its proximity to the instance. This makes it ideal for use cases that demand quick data access, such as processing real-time data and temporary storage of computation results.

FAQs

What is Ephemeral Storage? Ephemeral Storage is temporary storage allotted to applications or workloads, and the data it stores is deleted when the application terminates or there is a hardware failure.

How does Ephemeral Storage benefit data analytics? Its fast read/write capabilities can handle temporary data processing tasks efficiently, augmenting the overall data analytical process.

What are the limitations of Ephemeral Storage? The primary limitation of Ephemeral Storage is its temporary nature, making it inappropriate for persistent, long-term data storage.

How does Ephemeral Storage integrate with a Data Lakehouse? Ephemeral Storage can handle temporary data processing in a data lakehouse setup, but for long-term data repositories, a data lakehouse is preferable.

What are the security aspects of Ephemeral Storage? While its volatile nature reduces long-term data retention risks, it's crucial to establish strong security measures to safeguard the temporary data from potential threats.

Glossary

Temporary data: Data that is not required to be stored over long periods and can be discarded after use.
Read/write operations: The processes of reading data from and writing data to a storage medium.
Data Persistence: The characteristic of data which continues to exist even after the application that created the data ends.
Data Lakehouse: A hybrid data management platform that combines the best features of data lakes and data warehouses.
Data Analytics: The science of analyzing raw data to make conclusions about the information they contain.

Dremio and Ephemeral Storage

Dremio, a SQL Lakehouse platform, provides capabilities that overcome the limitations of ephemeral storage for data persistence. It can handle massive data volumes stored over the long term which Ephemeral Storage cannot.

get started

Get Started Free

No time limit - totally free - just the way you like it.

Sign Up Now
demo on demand

See Dremio in Action

Not ready to get started today? See the platform in action.

Watch Demo
talk expert

Talk to an Expert

Not sure where to start? Get your questions answered fast.

Contact Us

Ready to Get Started?

Enable the business to create and consume data products powered by Apache Iceberg, accelerating AI and analytics initiatives and dramatically reducing costs.