Your data is one of your business’s most valuable assets, whether it’s intellectual property or sensitive customer information. Not only do many industries require strict compliance with data privacy laws, but properly using data storage methods will protect your business from hackers while reassuring your clients that you prioritize their privacy.
However, with so many data storage options available today, choosing the best data storage solution for your business can be challenging. In our guide to business data storage methods, we’ll explain the differences between data storage systems and their pros and cons so you can feel confident in how you store and protect your data.
Forms of data storage
There are three main data types for corporate storage:
File storage
Also referred to as file-level or file-based storage, this type is presented as files that are stored in folders and subfolders in a directory. If you’ve used a PC and saved a document to the hard drive, for instance, you have experienced file storage. Files are accessible through a path that users must know. File storage is the most common type of storage on computer hard drives and network-attached storage (NAS) devices.
Block storage
Rather than storing data in a complete file, block storage breaks apart data and stores it in blocks that can hold 256 KB up to 4 MB. The blocks are then placed randomly on the storage device – which doesn’t hinder access speeds because each block is tagged with a unique identifier. When a user or application needs a file, the computer queries the block storage system, which rapidly identifies and collects all the needed blocks and reassembles them into a complete file. Block storage is ultra-efficient because it doesn’t use metadata, which takes up room on the storage device. Because it’s so fast, businesses tend to use block storage when they want to scale up quickly and when read/write performance is a critical priority.
Object storage
This type of data storage separates information into distinct objects with unique identifiers and metadata. It doesn’t form a hierarchy like file storage and is considered “flat.” Metadata is a critical component of object storage and each object can have quite a bit of metadata, such as information about the creator of the data and keywords, as well as policies for security, privacy, and access. Object storage works well with APIs so it’s simple to use with existing software and systems. It also scales very well. It could be spread across hundreds or thousands of devices and locations and still be super speedy because all the data is stored in one namespace. It’s the storage type of choice for public cloud providers, such as AWS, and organizations that deal with a lot of unstructured data such as video files, emails, IoT sensor data, social media content, and more.
Types of data storage solutions
Private Data Storage
Private data storage, often referred to as on-premises storage, is the practice of storing all of your business’s data in-house. This includes overseeing all the other necessary aspects of data storage to protect your hardware, such as server maintenance, physical security and temperature control. On-premises storage falls into three main categories: direct-attached storage, network-attached storage and storage area networks.
Direct-Attached Storage (DAS)
Most people use direct-attached storage, or DAS, even if they don’t know it. For example, nearly every laptop has a DAS hard drive. Directed-attached storage is exactly what it sounds like: storage hardware like an external hard drive or USB drive connected directly to a device.
Direct-attached storage is one of the most affordable data storage options available. For example, you can purchase a 5TB external hard drive for less than $200, making it an ideal solution for small businesses that don’t manage a lot of data.
However, unlike other business data storage methods, direct-attached storage is not very shareable. If you wanted to share your data with someone or access it from another location, you would have to either bring the DAS device with you or upload your files to share them online.
Network-Attached Storage (NAS)
Network-attached storage, or NAS, is when a storage device is connected to a network to allow authorized users to store and retrieve data from a centralized location. Essentially, NAS is like having an onsite private cloud over which your business has complete control. Because network-attached storage is like a private cloud, your team can access data remotely with only a network connection so that they can work anywhere, anytime.
Network-attached storage is a great fit for small- to medium-sized businesses with a highly collaborative workflow. Unlike direct-attached storage, network-attached storage allows multiple users to access and edit files on the same hard drive, so they don’t have to make copies and reconcile numerous versions of the same file.
However, performance can be a challenge for network-attached storage. If your network has a lot of activity, the performance could slow down to a crawl, leading to low productivity if you rely on high-performance applications. And while NAS is scalable in theory, you can only expand your storage capacity by adding another NAS device, which can unnecessarily complicate your storage setup.
Storage Area Network (SAN)
A storage area network, or SAN, is a network of storage devices that can be accessed by multiple servers or devices, creating a shared pool of storage space. By connecting to the network, users can access storage on the SAN as if it were a storage drive directly attached to their computer.
Because a storage area network connects multiple drives, they are much more resilient to the issues that plague single-device storage options, like device failures. It can also improve the efficiency of your data storage by consolidating your storage resources into a single network. However, SANs are more expensive and more complex than other private data storage methods, particularly if you don’t plan to use your SAN for cloud computing.
What is Software-Defined storage?
In software-defined storage (SDS), data is decoupled from hardware and then reformatted and organized before being used over networks. The SDS solution scales, which hardwired storage solutions cannot, particularly for workloads that use unstructured data in containers and microservices.
Software-defined storage may be implemented via appliances over a traditional storage area network (SAN), or implemented as network-attached storage (NAS), or using object-based storage.
Public Data Storage
While storing your corporate data onsite seems like the most secure practice, on-premises storage can get pretty expensive. Depending on the specific needs, it may be redundant for smaller businesses.
If you’ve ever accessed data “in the cloud,” then you’ve used public data storage. More commonly known as cloud storage, public data storage moves all of your data to a remote data center. It makes your data available wherever you can connect to the internet.
Cloud Storage (Public Version)
Public cloud storage refers to the practice of purchasing storage space from a third-party provider to store your data. Popular public cloud platforms include Google Cloud, Amazon Web Services and Microsofr Azure. Theese platforms provide storage together with computational resources and modern features, such as AI models. For simpler storage tasks your company can use Google Drive, Microsoft SharePoint or Dropbox. These service providers offer a user-friendly platform that makes it easy for businesses to share files and collaborate in the cloud from anywhere in the world.
Public cloud storage is one of the most scalable data storage options available today, making it simple for you to add storage as your business grows.
Hybrid Data Storage
As the name implies, hybrid data storage combines private and public data storage methods to highlight the benefits of both data storage options. Often managed by your IT department or a managed IT services provider, hybrid storage enables you to protect your data from unauthorized access while minimizing the maintenance required.
Colocation Services
Data colocation, or “colo,” is the practice of keeping your own data storage hardware in a shared, secure space known as a colocation data center or third-party data center. Like a regular storage facility, a business rents space in the colocation facility to store their equipment. In contrast, the colocation company oversees necessary maintenance like temperature control, physical security and bandwidth needs.
One of the main benefits of colocation data storage is higher, more reliable uptime, usually based on service tiers. This makes it easy for businesses to scale their data storage needs as their company grows without dedicating additional staff and resources to manage their data storage. Colocation also offers greater security than standard on-premises data storage methods, often using cameras and biometric readers to monitor the colocation facility 24/7.
However, if your colocation facility is not carrier-neutral, then your connectivity options may be severely limited. Therefore, when considering colocation services, make sure the provider has all the services you’ll need if your business expands in the future.
Storage VPS (Virtual Private Server)
A Storage VPS serves as a dedicated storage environment on a virtual server (typically a third-party service by hosting companies), providing users with scalable, flexible, and secure storage capacity that is accessible from anywhere. This option is particularly useful for businesses and individuals who require robust data protection and remote access without investing in physical hardware. A storage VPS is more private and customizable than public storage options, offering a middle ground between cloud services and physical private servers.
Cloud Storage (Hybrid Version)
Hybrid cloud storage refers to a mix of on-premises private data storage, private cloud services and a public cloud platform to create a unique cloud infrastructure for your business. For example, you could use your private data storage to store more sensitive information, like customer data or intellectual property, while relying on cloud storage for your everyday data access needs.
Because a hybrid cloud storage solution is so customizable, it’s easy to adapt your hybrid infrastructure to the changing needs of your business as you grow or change directions. As industries continue to digitize, flexibility has become a high priority for many organizations, leading them to invest in hybrid cloud storage solutions.
However, a hybrid storage option tends to be more costly than using a single data storage method, so it’s crucial for you to assess your organization’s data storage needs. In addition, hybrid cloud storage requires having some onsite data storage hardware. Therefore, you may want to consider the additional costs of capital investment and added maintenance in your needs assessment.
Popular types of data storage architecture
Data storage architecture is a hot topic in today’s business world as the demand for big data analytics is growing. Businesses generate massive amounts of data and require a robust solution to collect, store, and analyze it effectively. It’s important to choose the right data storage type and optimize it for your current and future needs to ensure optimal performance over time.
Data warehouse
Data Warehouse is a centralized repository for the storage of structured data. The data flows into the storage from various sources and undergoes a processing stage before hitting the Warehouse repository. Data Warehouse storage is designed as a well-organized library of data that can be easily retrieved and analyzed. Hence, organizations get insights faster, which improves their operations and decision-making. Also, with its organized data, Data Warehouse serves as a basis for conducting effective BI analysis.
Pros of data warehouse
- Enhanced ETL (Extract, Transform, Load) performance:
Data Warehouse storage is the right choice for maximizing the efficiency of the ETL due to its structured organization and fast query processing capabilities. - Higher security:
A structured data organization provides more granular data protection. - Fast query processing:
Data Warehouses are originally optimized to process large and complex datasets.
Cons of data warehouse
- Complex data design:
The process of creating a well-structured data repository requires experience and knowledge in data engineering. - Limited agility:
Data Warehouse only stores certain data that is transformed and structured for particular use cases. - High costs:
Data warehousing costs are typically higher than other data storage solutions due to the comprehensive analytical capabilities it provides.
Best-fit use cases
- Business Intelligence analytics
- Supply chain operations improvement
- Evolution of Marketing & sales campaigns
- Financial data & trend insights
Data lake
Unlike Data Warehouse, Data Lake allows businesses to store and process data in various formats (structured, unstructured, and semi-structured) and types (audio, video, and text) in one centralized repository.
According to the 451 Research’s report, Data Lake is a popular solution for businesses of all sizes, as (71%) of enterprises are currently using or piloting a Data Lake environment or plan to do so within the next 12 months.
Pros of data lake
- High agility:
Since Data Lake has no strict requirements for receiving only structured data, it gives organizations more space for maneuvers with analytics. - Lower costs:
Data lakes are less expensive than Data Warehouses as they don’t require any transformations or pre-processing of data before storage.
Cons of data lake
- Lack of structure:
Extraction of specific data from the Lake can be challenging as unstructured data requires more time for queries and management. - Security challenges:
Data Lakes contain vast amounts of data in various formats that come from different sources, so it may be challenging to identify security threats or vulnerabilities. - Query execution:
By default, Data Lakes have no query processing capabilities and need additional big data tools and technologies such as Apache Spark and SQL query engines to run analytics on them.
Best-fit use cases
- Business Intelligence analytics
- Machine Learning projects
- Gathering Marketing Insights
Data lakehouse
Businesses rarely use Data Lake in its pure format. In most cases, they not only need to store data but also effectively process the data. Thus, most companies chose to go with a hybrid approach where Data Lake is appended by a Data Warehouse. The latter acts as a layer on top of the Data Lake and provides a structured and optimized environment for analytics, reporting, and BI. This approach allows users to combine the capabilities of a Data Lake and Data Warehouse and analyze massive amounts of diverse data effectively.
Pros of data lakehouse
- Scalable repository:
Data Lakehouse can preserve large volumes of data and be easily scaled by adding more servers or nodes to the system. - Reasonable costs:
Data Lakehouse allows businesses to get the features and benefits of Data Lake and Data Warehouse in one place. This significantly cuts costs, as businesses do not have to pay for two sets of storage. - Improved data governance:
The built-in features of Data Lakehouse provide advanced data governance capabilities for managing data quality, security, and privacy in a centralized manner. - Fast set up:
Data Lakehouse offers ready-made functionality for data processing. Therefore, organizations can easily start running their analytics without setting up and integrating additional tools, as in Data Lake.
Cons of data lakehouse
- Vendor lock:
There are only a few providers of Data Lakehouse, so the choice of platforms for its implementation is limited compared to the other storages. - Flexibility constraints:
The built-in feature set of Data Lakehouse has some limitations in terms of customization capabilities. So, if an organization needs to modify the Lakehouse architecture at a certain point in time, this may unveil diverse hidden complexities and may require substantial investment.
Best-fit use cases
Data Lakehouse is a go-to solution for organizations seeking to run both Data Warehouse- and Data Lake-like operations on the same data within a single platform. Moreover, this approach is an ideal option for those looking for a fast launch, as Data Lakehouse provides robust functionality by design.
Nevertheless, you should carefully approach the off-the-shelf options and make sure that those address your needs in full. Otherwise, going beyond the default functionality can take tangible effort and investment.