Learn More About Time Series Databases
What are Time Series Databases Software?
The growing number of different data types leads to the proliferation of different types of databases to facilitate its storage and analysis. Among the fast-growing data types is time series data—data which is timestamped and created over time—which is on the rise with the growth of the internet of things (IoT). Although it is frequently possible to store this data in other types of data stores, time series data has special properties—the data is append-only, making it worthwhile to consider a made-to-order database solution. The first challenge for selecting a database is finding the best structure for the data to be stored. In certain cases there is a natural fit—for example, airline flight information fits very well in a graph database as this mimics real-life patterns—while long-form web content usually slots into document databases.
With time series databases software, users are able to store any data that has a timestamp, such as log data, sensor data, and industrial telemetry data. The use cases are manifold. For example, application developers use this software for the purpose of application monitoring to collect data points in real time and better understand application performance. In addition, IoT developers benefit from time series databases as they store and process sensor data, such as smart home devices, to determine how they are performing over time.
Key Benefits of Time Series Databases Software
- Provide scale and speed, with faster processing time than relational databases
- Offer a tool which is specifically geared toward time series data
- Allow for structured, organized data storage and management
Why Use Time Series Databases Software?
Like other databases, time series databases are primarily maintained by a database administrator or team. Owing to its wide range of coverage, time series databases are also accessible by several organizations within a company. Departments such as development, IT, billing, and others may also have access to time series databases, pending their assigned uses within the company.
Predict future — Make informed predictions about future events, observe real-time changes, and capture historical anomalies.
Understand past — Understand past data with a purpose-built database.
Who Uses Time Series Databases Software?
Time series databases software is highly flexible and is used by diverse teams throughout a company, making it particularly beneficial. For collecting extra large data sets in real time, big data processing and distribution systems are helpful. These tools are built to scale for businesses that are constantly collecting enormous amounts of data. Pulling data sets may be more challenging with big data processing and distribution systems, but the insights received is valuable due to the granularity of the data.
Database administrators — Time series databases have grown in popularity since they are easier to implement, have greater flexibility, and tend to have faster data retrieval times. Database administrators use these tools to maintain and manage their time series data, ensuring it is properly stored.
Data scientists — As data science, including artificial intelligence, is fueled by data, it is key that this data is stored in the most effective and efficient manner. This ensures that the data can be queried and analyzed properly.
Kinds of Time Series Database Software
Although all time series databases store timestamped data, they differ in the manner in which this data is stored, the relation between the various data points, and the method in which the data is queried.
Relational databases — Relational databases are traditional database tools used to align information into rows and columns. The structure allows for easy querying using SQL. Relational databases are used to store both simple information, such as identities and contact information, and complex information critical to business operations. They are highly scalable and can be stored on-premises, in the cloud, or through hybrid systems.
NoSQL databases — NoSQL databases such as graph databases are a great option for unstructured data. If the user needs to render a value that is easily found by its key, then a key-value store is the fastest and most scalable. The drawback is a much more limited querying ability, implying its limitations for analytic data. Conversely, rendering a user’s email address based on the username or caching web data is a simple and fast solution in a key-value store.
Time Series Databases Software Features
Time series databases, designed specifically for time series data, provide the user with the features they need to successfully store, process, and analyze this data.
Querying using time— Time series databases allow users to query data using time, allowing them to search or analyze the data across a given time period, even by a fraction of a second.
Data security — Time series database solutions include data security features to protect the data stored by a business in its databases.
Database creation and maintenance — Time series databases software allows users to quickly create brand-new relational databases and modify them with ease.
Scalability — Times series database solutions grow with the data and is hence scalable, with the only pain point being physical or cloud storage capacity.
Operating system (OS) compatibility — Relational database solutions are compatible with numerous OS.
Recovery — Whether a database needs to be rolled back or outrightly recovered, some time series database solutions offer recovery features in the event any errors occur.
Potential Issues with Time Series Databases Software
Unstructured data — Time series databases struggle when handling unstructured data. Time series databases hinge on data being structured to properly create relationships between data points and data tables. If a company uses mostly unstructured data, they should consider a NoSQL database solution or data quality software to clean and structure unstructured data.
Query lag — Time series databases store massive quantities of data, but with that advantage, such database tools run queries slowly on larger data sets. This is mainly due to the sheer volume of data being queried. In situations where queries might traverse significant quantities of data, they should be based on specific values whenever possible. Also, querying strings takes significantly longer than querying numerics, so focusing on the latter may help improve search times.
