If your company or organization is operating with time series data, you need to take advantage of the automated power of a time series serverless database.
It seems but a short few 500 years ago that the world was introduced to the incredible innovation known as the Gutenberg printing press. A marvel of modern engineering, the printing press took a relatively tricky task – writing data onto paper – and automated it.
When humble goldsmith Johannes Gutenberg threw the switch on his printing press in 1440, the revolution began. Suddenly, the world could benefit from a single printing press producing 3,600 pages of printed text each day. The ability to automate the recording of data on paper took the world by storm.
The printing press’s revolutionizing power would remain relatively unchallenged until the emergence of the electronic age – and the introduction of the ability to store data on an even more mysterious system: the digital world.
The Automation Revolution
Fast forward 560 years later, and the power of automation has come so far as to become – dare we imagine – normal.
Self-driving electric vehicles are beginning to fill the streets, with integrated data systems powering shipping lanes across the land. Stock market buying and selling are guided by automated, learning algorithms that account for the tiniest change in market sentiment and drive dollars. Online buying and selling are powered by digital currency transactions, which turn clicks into cardboard boxes that travel the globe automatically.
While these operations and activities may seem vastly different, the underlying power source that guides each is shockingly similar – time series data recorded and stored via constantly-updating algorithm systems.
If the concept appears a bit mysterious, break down each of the above-listed activities by their time-based data characteristics:
- Electric, self-driving vehicles are continuously recording location, environment, and system data, and sending that information to a “brain” that decides the next turn of the wheel or the press of the gas pedal.
- Wall Street’s automated algorithm collect data through thousands of transaction points and formulates buying or selling decisions in the span of a digital heartbeat.
- Smart online shopping algorithms hone in on consumer search behavior, recorded keywords, and digital routines to market items of interest directly to our smart devices when we log on. When we respond to an ad with a purchase, that data is sent directly to a database that directs the pulling of the product from the shelf and begins its two-day journey to your home – on the other side of the globe.
Each of these examples shows a strict reliance on precise data that is recorded in a moment of time. Complex algorithms designed by tech wizards for various industries utilize this time series data points to make automated decisions and respond in lightening-fast speed to changes as they occur.
Like the moment that Gutenberg’s fateful printing press hummed to life, time-based data has dynamically altered the world as we know it. However, while Gutenberg could easily transport a day’s worth of printed paper, the enormous amounts of time-stamped data collected continuously have to live somewhere.
Enter the Time Series Database or TSDB for short.
The Rise of Time Series Databases
Each of the above examples utilizes the power of real-time data to make decisions and act. With time series data, timestamped data is recorded on a time-specific axis, and changes in data over time are used to drive processes. In order to store these time series data points, database engineers have created time series databases (TSDB) built to operate with this type of data.
In the past several years, TSDBs have exploded in use and popularity with industries and enterprises for their ability to quickly record time series data and allow teams to analyze the streamed data quickly. However, as the world of automation grows and TSDBs respond to the need for data management, the massive size of these databases is calling for a new storage system. As we will see below, standard servers can no longer handle the huge amounts of data coming to TSDBs.
To answer the call for new options for time series database storage, serverless TSDBs are becoming an increasingly popular option. By harnessing the power of the digital cloud, these databases can grow with both usability and scalability – increasing the potential for this new method of data analytics.
Time Series Databases: A Primer
Wondering how time series databases work? In essence, a TSDB is filled with fixed-value data connected to dynamic values. The data is captured on a continuum that is marked with a time-stamp – thus the moniker time series data.
Consider a smart device – such as a smart thermostat – that records the number of times you interact with particular elements of the system. The thermostat (Hallway Monitor) is the fixed data, and “71 degrees at 4:25 PM” is the dynamic data applied. What if someone walks by and turners the Hallway Monitor down a few degrees at 5:00 PM? Another data point is captured and recorded.
This data, when recorded on a time-based axis, allows for algorithms to analyze the data for learning purposes. The next day, if the same behavior occurs at nearly the same time, the algorithm compares the time series data and “learns” to automatically shift the temperature the next day.
With each data point recorded with a unique timestamp, time becomes the characteristic of the data and is stored in a database that is built for streaming that time-sensitive data quickly. TSDB is designed to leverage the power of databases that can instantly receive, store, and send this time series data rapidly between systems for analytical and algorithmic purposes.
In the world of artificial intelligence operating in the time-based human world, TSDB will play a huge role in processing the nearly limitless amount of data that will begin to move between automated systems every second of every day.
Enterprise Use Cases
Convincing your Board of Directors or IT department of the necessity of moving toward using serverless time series databases can be difficult. For some enterprises, time series data is already being collected and stored on-site on servers without error and somewhat efficiently, so many see no sense in changing their entire system. However, the “way things have always been done” may not be enough.
As seen above, the technological advances of time series data are growing exponentially. As your business or organization increases reliance on time-stamped data and the storage of that information, server-based storage solutions will become increasingly burdened by both the speed of data transfer and the amount of data that is being recorded and stored.
Need some examples of time series databases at work? Consider the following use cases:
The most common use of time series databases is in the Internet of Things (IoT) environment. In IoT, remote devices are constantly capturing and sending data and metrics for analytical purposes. Whether used by retail companies to gauge consumer use of particular products (“Hey, Alexa, purchase…) or by trucking companies to track mileage through geo-tagged receivers, remote devices that send time-specific data are sending data somewhere.
With serverless time series databases, IoT devices can communicate to a cloud-based database system in real-time. This allows for quick and flexible data transfers for making split-second decisions without worrying about the state of the server in its location. The TSDB collects the data and the database sends the analytics to the teams that need to act on the data.
Real-Time Computer System Analysis
Regardless of your enterprise size or the industry you operate in, you likely utilize an IT system that tracks computer system states. With a serverless time series database, the IT team can track time-stamped computer readings through a real-time database recording. With ease, the team can react to errors, respond to resource needs, and analyze performance across a variety of systems.
For an added bit of good news, a serverless database removes the need for your technical team to monitor and maintain a server on-site. With the data being stored in a database outside the site, you can trust that the time series information is both accurate and protected. Your IT team will appreciate you taking one more server off their shoulders!
While the two above use cases have been use-cases, an argument must also be made for TSDB use cases for scalability.
Due to its near-constant recording nature, time series data can accumulate incredibly quickly. Consider a single connected semi-truck from the example above. As the vehicle makes its journey across the country, the time series data collected from a single day could be as high as four terabytes. While a single server database may have the ability to record and store that much data on a single truck, consider that American company JB Hunt Transport Services uses 19,000 vehicles in their fleet.
The time-based data needs to collected on these trucks, and it must be collected quickly for real-time decisions to be made accurately. Average relational databases struggle to handle massive databases, and while NoSQL databases can do a bit better, they are no match for that much data a day!
If your enterprise is hoping to scale in size and capability, you must make the move toward serverless databases. Serverless TSDBs are built to handle scale by introducing efficiencies such as higher ingest rates, faster queries at scale, and increased data compression capabilities. With a database built specifically for time series data points, you can trust that your database will comfortably grow with your enterprise.
Discover Your Enterprise Serverless TSDB Solution With Hyprcubd
If you are ready to take the next step in implementing a serverless time series database solution for your enterprise, choose to partner with a team that has the skill and expertise to make the process painless.
Hyprcubd’s serverless time series database is built uniquely for companies and enterprises that are looking for solutions to increase data productivity and harness the advantage of real-time data analytics through TSDB’s efficiency.
The reward is a new world of possibility and potential with a leading data system that is ready to grow as the technology expands. Whether you are a new startup or a huge enterprise, your team can harness the power of the unlimited data ‘cloud’ for any business purpose.
Ready to take your processes to the next level? Contact the Hyprcubd team today! Complete the contact form, and a member of the Hyprcubd team will be in touch to discuss your options.
Did you love this article? Spread the love for time series databases by sharing this post online, and leave a comment below!