Data storage is how a machine or device will archive its data in digital or related forms for retrieval, processing, or management by its owners and users. It includes the collective methods and technologies that retain digital information. Consumers and businesses rely on data storage to preserve information ranging from product preferences, viewing history, shipping address, even a profile ID.
Data storage typically takes place on a database which can reside either on-premises in the physical server of a company or over a cloud platform as part of a distributed database system.
Data is the lifeblood of any organization and it must be protected against hackers, natural disasters, viruses or unplanned systems shutdown. Data backup and data security are two essential components to data storage. Common techniques of data backup are periodic backing up of all system, point in time retrieval, and data replication to multiple nodes in a distributed system.
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Based on an application’s specific needs, a project will employ different ways to store their data. This will have implications on how fast they can retrieve it, how easy it will be to manage on the cloud, how efficient it can navigate today’s world of unstructured data, and cost in terms of both performance and financial expenses.
Relational. This has been the standard way to store data since relational SQL databases were first developed half a century ago. Data is stored in rows and columns, with different data sets assigned to distinct tables, linked by a common key. When a data query is made, tables are merged together to return the query set.
NonRelational. Nonrelational or NoSQL databases emerged in the 2000s. They are built to handle real-time web applications, unstructured data, and the massive scale of information being managed over the Internet of Things (IoT). Nonrelational databases take many forms.
Document. A document database is a NoSQL database that receives data into whole documents, eliminating the need to join tables to return query data sets. Optimal for distributed database topologies, document databases have superior performance, can handle massive load, and are less complex than the relational model. Document databases are schemaless, offering huge flexibility.
Graph. Graph database models store relationships among data. Fraud detection systems make use of graph database models to see unnatural relationships between points. Recommendation engines also make use of such models.
Time Series. The database model of the IoT, time-series storage focuses less on values of data points and more on the patterns that emerge among these points over a period of time.
When planning your data storage management strategy, several things must be considered in choosing the right solution:
- Determine how best to meet the compliance requirements of your organization
- Asses the mix of structured data to unstructured data that will be in your system
- Decide on the right platform for your data: disk, on-prem server, cloud platform
- Take into consideration your data security, data backup, and disaster recovery needs
- Minimize the storage of redundant data, especially if you store data on the cloud
- Find the data storage solution that enables you to retrieve and manage your data with the highest performance
Recent events have increased the global push to working with data over distributed cloud systems. As the demand for remote working increases, the use of distributed systems will become more essential.
Enjoy a webinar about the advantages of using a document database as part of a distributed cloud data solution. A document database, with it's reduced fundamental complexity in processing raw data makes it ideal for such distributed functions like data replication and supporting a microservices architecture.
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