As technology advances and more data is generated every day, finding efficient and effective ways to store that data is increasingly important. There are 5 main methods of storing data that are commonly used: file storage, block storage, object storage, database systems, and data warehouses. Each method has its own advantages and disadvantages that make it suitable for certain use cases. In this 5000 word article, we will explore what each of these 5 data storage methods are, how they work, their key benefits, and ideal use cases. This will provide a comprehensive overview of the main options available for storing large amounts of structured and unstructured data.
File Storage
File storage is one of the oldest and simplest ways of storing data. It involves saving data to files that reside on hard drives or other physical storage media. Some key aspects of file storage include:
- Data is stored in a hierarchical file system consisting of folders, subfolders and files.
- Files contain either unstructured data like documents, images, videos etc. or structured data like csv files.
- Access to files is provided via path names.
- Files are stored and retrieved using simple operations like open, read, write and close.
- There is no data redundancy or replication.
- Common file systems for file storage include NTFS on Windows, ext4 on Linux and HFS+ on Mac.
Some benefits of file storage include:
- Simple to understand and use.
- Works with both structured and unstructured data.
- Easy to access and extract specific files.
- Files can be copied and transferred easily.
Use cases where file storage is preferred:
- Storing documents, spreadsheets, presentations, images, audio and video files.
- Distributing software, media files and documents via file sharing.
- Backing up critical data to external hard drives.
However, there are some downsides to file storage:
- No built-in data redundancy – files need to be manually backed up.
- Harder to search and analyze data stored in multiple file formats.
- File management can get complex as the number of files grow.
- Concurrent access and locking capabilities are limited.
- Not suitable for large-scale transactional applications.
Overall, file storage works best for relatively small, standalone data that doesn’t require a lot of querying and analysis.
Block Storage
Block storage divides data into evenly sized blocks which are stored as separate units. Here are some key characteristics of block storage:
- Data is split into equal sized blocks of 512 bytes to 4KB.
- Each block is assigned a unique identifier to locate it.
- Blocks are stored independently and there is no file system.
- Block storage is presented to servers as logical volumes called volumes.
- Volumes appear as ordinary hard drives to the operating system.
- Common protocols for accessing block storage are iSCSI, Fibre Channel and FCoE.
Advantages of using block storage include:
- Volumes can be resized, cloned and backed up easily.
- Fast access as blocks can be read and written randomly.
- Data redundancy can be implemented by replicating blocks.
- Works with structured and unstructured data.
- Easy to scale storage by adding more blocks.
Block storage is ideal for:
- Database servers requiring fast random access.
- Frequent backups and snapshots.
- Virtual machine storage and booting.
- Storing data for transactional applications.
Some limitations of block storage:
- Data has to be reconstructed from blocks to make sense.
- No built-in data lifecycle management.
- Not optimized for storing large files.
- More complex to set up and manage versus file storage.
In summary, block storage provides high performance storage volumes to servers and is commonly used for databases, virtualization and transactional applications. But it requires more administration versus file storage.
Object Storage
Object storage manages data as objects in a flat pool rather than in a hierarchical file system. Some key features of object storage include:
- Data is stored in objects which encapsulate data and metadata.
- Objects are accessed via a unique identifier.
- Software APIs and HTTP protocols provide access to objects.
- Objects are pooled together across distributed commodity servers.
- Built-in data redundancy and replication.
- Designed for web scale workloads.
Benefits provided by object storage:
- Massively scalable to billions of objects.
- Durable and resilient data with built-in redundancy.
- Cost efficient hardware and operations.
- Parallel streaming access improves throughput.
Use cases suited for object storage:
- Storing backups, archives and data lakes.
- Distributing large media files via web apps.
- Storing unstructured data from IoT devices.
- Building content repositories and catalogs.
Limitations of object storage include:
- No standard API across implementations.
- Eventual consistency on updates.
- Not optimized for low latency workloads.
- Hard to update objects in place.
In summary, object storage allows storing massive amounts of unstructured data while providing scalability, redundancy and parallel access. But it is not designed for transactional workloads.
Database Systems
Database systems provide structured storage optimized for defined data types and querying. Key aspects of database systems:
- Data is modeled into predefined schemas and tables.
- Standard methods using SQL are used to access and manipulate data.
- ACID transactions provide data consistency.
- Query optimizers and indexes improve performance.
- Scale horizontally across multiple servers.
- Different types like relational, NoSQL, graph and time-series databases.
Benefits of using a database system:
- Structured storage and validation of data.
- Flexibility to evolve data schema.
- Powerful querying via SQL and other languages.
- Transactional integrity of data.
- Role based access control.
- High availability and scalability.
Databases work well for:
- Transactional business applications.
- Structured data from sensors, metrics and logs.
- Applications needing complex querying and analysis.
- Mobile and web apps via ORM frameworks.
Downsides of databases include:
- Overhead of maintaining schema and indexes.
- Not ideal for unstructured or growing data.
- Can be expensive for petabyte scale data.
- Legacy relational databases not cloud native.
Overall, databases provide structured storage optimized for defined access patterns on relatively small to medium sized data.
Data Warehouses
Data warehouses store large amounts of historical data from transactional systems optimized for analytics and reporting. Here are some key features:
- Central repository that ingests data from multiple sources.
- Store data integrated across the organization.
- Columnar storage and compression reduce costs.
- Separate workloads from operational systems.
- Batch and real-time processing generate aggregates.
- BI tools and SQL queries access data warehouse.
Benefits provided by data warehouses include:
- Cleansed, structured data for analysis.
- Complete data history enabling trend analysis.
- Dedicated managed platform for analytics.
- Improved performance for analytics queries.
- Data organization and cost optimization.
Data warehouses work well for:
- Generating business insights from historical data.
- Operability analytics and reporting.
- Storing summarized time-series data.
- Sophisticated BI analytics.
Challenges with data warehouses:
- Significant effort for integrating data from sources.
- Complex ETL processes required.
- Hard to change schemas once defined.
- Not designed for direct access by applications.
In summary, data warehouses provide a persisted solution optimized for running analytics on integrated enterprise data. But they require significant effort to set up and maintain.
Conclusion
There are 5 primary methods of storing data – file storage, block storage, object storage, database systems and data warehouses. Each method has different architectures, access patterns, use cases and scales.
File storage provides the simplest way of storing data files but lacks redundancy and scalability. Block storage presents fast block level volumes to servers and applications. Object storage pools highly scalable and durable storage using data objects. Databases provide structured storage with querying and transactions. Data warehouses serve as repositories optimized for analytics.
The method chosen depends on how data needs to be stored, accessed, queried and scaled for different applications and use cases. Small data volumes benefit from file storage while big data applications use object storage. Transaction systems leverage databases while analytical systems use data warehouses. Block storage provides the building blocks for devices and filesystems.
By understanding the core capabilities of each storage method, organizations can optimize their data storage architecture using the right tools for their specific needs. The next decade will see new hybrid approaches leveraging multiple methods in a cohesive data management platform.