Why the Block Storage vs Object Storage Debate Even Matters

In today’s high technical age, data is everything.

Data is used in various different ways by various different parties; you could use data to make an informed grocery purchase, while huge companies use data to make informed decisions on business-critical issues. Countless jobs and disciplines have also been created in the pursuit of better treating, analyzing, and storing data. 

Everyday, we generate roughly 2.5 quintillion bytes of data, so far. The need for reliable storage sources has become more and more pressing as each day passes. Storage devices are becoming more advanced, storage methods are becoming more and more precise, and companies are dumping billions of dollars every year into refining their storage options. While many storage practices exist, two have risen to prominence amongst cloud users: block storage and object storage. 

Block Storage - The Pros, Cons, and Use Cases

To understand where this debate stems, you’ll have to understand the tech that drives it. 

Block storage involves taking data and cutting it into equally-sized blocks, with these blocks being stored in physical storage that allows for quick extraction once needed. During the blocking process, high level metadata information is not maintained, though a unique identifier is supplied to each block in the creation process.

Block storage provides a direct pipeline to data, making it the ideal storage solution for applications that need fast and strong data access- just one of the many benefits of block storage.

Block Storage Pros and Cons

Known for its ability to quickly access data, block storage provides a ton of benefits to developers in search of something mobile and agile. Such benefits include:

• Performance: Although metadata identifiers are severely limited in block storage, the identifiers given during blocking are used for operations. Metadata can be clunky, having identifiers removes overhead for transferring while allowing for efficient data access and retrieval
• Scaling: In order to meet growing resource requirements, new blocks are able to be added to existing blocks, with the removal of blocks also being possible to optimize resource consumption
• Modification: In the case of data writing changes, users can modify blocks without affecting the performance of their applications. Instead of rewriting an entire file, necessary blocks are singled out and are able to be modified, being easily inserted once changes are made.
• Compatibility and Flexibility: Block storage and related devices operate across different operating systems and file systems, removing the need for network environments. This flexibility allows for increased compatibility by allowing for configuration and optimizations for different operating systems and environments

While the benefits of block storage are certainly undeniable, the use of block storage creates disadvantages and cons that can’t be overlooked, such as:

• Costs: Despite the benefits, block storage solutions can cost a ton at the end of the day and require a highly trained maintenance personnel
• Metadata Limitations: Despite utilizing identifiers for data accessing, removing metadata as a whole can hamper operations in metadata-critical operations
• Higher Complexity: In order to create a well-oiled block storage machine, more elaborate and complex takes on the storage method must be taken into account

These cons are dependent on your personal block storage usage, with the storage method actually being enhanced by your usage and with the benefits quickly outweighing the cons in the long run.

Block Storage Use Cases

Because of block storage’s unique take on data storing and accessing, the tech flourishes in applications that leverage transactional data and are considered critical to a business’ operation. Some popular use cases for block storage include:

• Containers: Containers themselves are already efficient beasts in the tech world, storing a containerized application in a block further exemplifies container flexibility by allowing for easier transportation between servers and different environments
• Virtual Machines: Block storage is a huge support amongst the VM hypervisor community, letting drive size and storage transfer process become easier with block modification and agility.
• Transactional Workloads: Block storage and transactional workloads go hand in hand: the sheer amount of data generated in transactional workloads is easily contained in blocks, letting developers create a scalable yet contained database. 

With the ability to flourish under high-stress, business-critical workloads, block storage and the machines behind it are certainly the go-to storage method for many developers around the world. Despite some limitations regarding the cost and metadata usage, block storage provides users an in-depth look into efficient data access and migration.

Object Storage - The Pros, Cons, and Use Cases

Object storage takes a similar approach to data storage- primarily geared towards storing unstructured data, object based storage sees data stored into objects and further stored into a flat data structure known as buckets. Unlike block storage systems, object storage preserves vital metadata and unique identifiers inside objects, allowing for easy data identification based on function and form.

Because of its use of metadata and adeptness towards bigger data sets, object storage is ideal when working with large bodies of unstructured data.

Object Storage Pros and Cons

Being the primary storage choice of titans such as Amazon, Google, and Microsoft, object storage certainly provides its users with unparalleled data interaction benefits. Some benefits of the storage method include:

• Unlimited Scalability: Object storage supports limitless scalability with its flat architecture, encouraging onboarding new machines to support the scale. Scaling with object storage is as simple as adding another node to the cluster.
• Low Costs to Data Volume Ratio: Object storage goes by a pay-for-what-you-use model, letting you control your costs relative to the data you input. That being said, object storage was actually created with cost being a central factor- rivaling the likes of block storage and file storage.
• Data Resilience Support: In the process, objects are stored across different devices, systems, and even data centers- letting the data become safeguarded while readily accessible in the process.
• Much Needed Support for Unstructured Data: These days, unstructured data is extremely plentiful, with this data type making up 80-90% of the data in enterprises. Because unstructured data presents itself in so many different formats, it becomes hard to fit into a repository- object storage breaks down this data silo while making it easier to perform data analysis.

While object storage flourishes where many data tech fails, it’s shown that object storage does have some flaws. Some cons of this technology are:

• Inefficient Transactional Data Handling: With the speed that transactional data handling requires, locating that data through object storage might not be the most efficient option.
• Modifications: Unlike block storage where modifications are as simple as removing a block, rewriting, and reinserting, object storage modifications require creating a new object that contains the changes.
• Cost and External Problems: Because object storage solutions are typically housed within cloud providers, accessing object storage solutions might be costly depending on your usage and needs, along with other issues that can be posed by external providers.

Object Storage Use Cases

With its roots grounded in providing unlimited scaling, better data resilience, and the much needed support that unstructured data has been clamoring for, object storage uses are plentiful. Some of the most popular use cases for this technology include:

• Data Lakes: In essence, a data lake is a centralized data repository that can be used to store data in any form. Object based storage works perfectly with data lakes because of its ability to scale with the ever-increasing data storage requirements, making the data lake more resilient in the process. 
• Machine Learning: Machine learning models can learn millions to billions of data points and examples every second. Based on the sheer scale of data being consumed and stored every day, object storage is likely the best option for machine learning solutions.
• Data Analytics: To store data efficiently is just the first step in the data analytics process, though an important step. Object storage allows unlimited amounts of data to be stored, allowing for better and more accurate analyses to be formed
• Backups: Objects can be stored virtually anywhere, though the most likely location is in the cloud. Objects can be replicated and distributed across different systems, data centers, geographical locations, and machines. If one version of the data were to become corrupted, a duplicate copy will launch in its place and thrive

Object storage solutions provide a unique take on data storage and cloud storage. By condensing data into individual units called objects, object storage is able to shave off IT costs while providing limitless scaling opportunities- giving developers working with data analytics or machine learning a go-to storage option. While object storage manages data in a way that’s effective towards the scale of a project, its ability to quickly pull data and modify its data causes some pitfalls within the nearly bulletproof technology.

Object Storage vs Block Storage - What Works for You?

One solution tackles mission-critical data head on by creating an efficient data access system, removing metadata in the process. Another provides unlimited scaling capabilities, giving developers the ability to innovate endlessly, though at the expense of slow modifications. Whether object storage or block storage is the best solution depends on your personal usage.

For one, object storage provides unparalleled utility towards unstructured data and breaking down metadata, something that block storage falters with. On the other hand, block storage provides precise, high performance value best suited for structured databases, with its speed and efficiency blowing past those posed by object storage solutions.

Object Storage Solutions with Lyrid

Whether your solution calls for the high performance of block storage, or the scalability and utility of object storage, just know that Lyrid provides a unique take on object storage. 

Our object storage solution uses the traditional object format, but includes a content delivery network (CDN) to access data files faster and more cost effectively. Along with the unlimited storage scaling and affordability comes faster page loads, improved solution performance, and infrastructure and bandwidth cost savings. Backup databases and production-ready databases are also available at your fingertips- all without the need of dedicated servers or virtual machines.

To learn more about Lyrid’s object storage solution, visit our webpage, or book a meeting with our product specialist for a product tour!