In this blog, you can find a lot of articles related to big data. So, we are not going to be repetitive about its importance in today’s world. But let’s say that its proliferation brought many different challenges to the table. And one of them is big data storage.
People, businesses, and devices now produce and keep information at unprecedented volumes. The virtual pile of dispersed and unstructured data grows taller by the minute. Sources are endless, from traditional ones to new ones like IoT sensors. Groundbreaking technologies like machine learning, embedded systems, and cognitive computing are generating their own data without human intervention.
In this context, studies affirm that big data storage demand will hit an impressive number of 163 zettabytes by 2025. When traditional methods couldn’t handle saving data volumes reaching the terabyte or petabyte scale, companies needed to pursue new ways to keep them without running out of space. Let’s dive into the world of big data storage solutions.
Big Data Storage Solutions: Core Attributes and Benefits
Big data storage is a computing architecture designed to collect and manage large and unstructured data sets in real time. Companies are in need of big data storage before applying big data analysis in their decision-making process.
Big data storage solutions should address the three “Vs” of big data:
- Variety of sources and data types.
- Velocity of data ingesting and running operations.
- Volume of data sets.
Furthermore, it would be advisable for them to also take care of the four V: veracity (a.k.a. trustworthiness of data sources). Below you’ll find the main benefits of big data storage solutions.
Flexibility of data management
Big data solutions handle both structured and unstructured data and are able to work with different data models with greater efficiency. Also, with them, it’s easier to organize data digitally from multiple sorts of connected devices.
Long-lasting and scalable data preservation
With digital methods, big data storage now makes it easy to save large volumes of data for longer periods. Adding more space is also simpler, reducing the physical footprint of data storage.
Painless data accessibility and recovery
Big data storage solutions allow everyone to access and read information in the blink of an eye without the need of going to browse through a room full of file cabinets. Additionally, data backups are effortless, making a recovery much faster and simpler in case a file gets lost or altered.
Seamless work collaboration
With centrally stored data, teams can synchronously access the same files and easily work together across shared documents.
Main Big Data Storage Methods
Like physical warehouses, data warehouses are large and robust digital facilities to store and process big data at all times. They support core activities of big data analytics: queries, reporting, business intelligence, data mining, research, and monitoring, among others. While feeding datasets in and out through online servers, data warehouses translate raw data into valuable insights, trends, and forecasts.
There are 3 types of data warehouses:
- Enterprise Data Warehouse (EDW), which offers a centralized approach to corporate data organization and representation.
- Operational Data Store (ODS), for routine data storage with real-time updating.
- Data Mart, designed specifically for a particular business line.
A data lake is a central repository of raw data. Unlike data warehouses, where data is structured and already filtered for a specific purpose, in data lakes, data is stored in a pool with no specific use. The stored data can be structured, semi-structured, or unstructured, and it’s associated with identifiers and metadata tags for faster retrieval.
There are two types of network-based storage: Network Attached Storage (NAS) and Storage Area Network (SAN).
NAS is a way to store data in a centralized location, usually a single computer or server with a group of redundant storage containers or a RAID (Array of Inexpensive Disks). It makes data accessible to users thanks to an internet connection. It’s easy to set up and deploy and inexpensive. Its scalability is not infinite, but its resiliency is high because of the fault-tolerant multiple drives.
Unlike NAS, SAN is a network of multiple devices -solid-state drives (SSDs), flash storage, and cloud storage- connected by fiber channel. As a consequence of this setup, SAN is faster, and its latency is lower than in NAS systems. Also, it’s highly scalable. On the other hand, it’s more expensive and complex to manage.
One of the most popular big data storage methods. Have you heard of iCloud or Google Drive? Then, you’re storing your data in the cloud. With this technology, data is virtually stored online and easily accessible for everyone, everywhere.
With this system, data is stored in blocks, each one of them considered an “object.” In object storage, the repository can be distributed across multiple physical storage devices communicated by HTTP and REST APIs. Every object is saved with its metadata, enabling flexible analysis and retrieval of data based on features. As every object is placed in a uniform space without hierarchy, it can be found easily with a unique identifier.
Big Data Storage Tools
After going through the ABC of data storage, it’s time to pick your tool of choice. There are a lot of options in the market. Some of the most known are Hadoop, HBase, NetApp, and Snowflake. If you have any doubt about how to implement them or need additional information on data storage solutions, don’t hesitate to contact us at vanguard-x.com.