Over the last few decades, MDT has been used as a primary variable to calculate buildings’ energy efficiency. Next, think about an environmental value like mean daily temperature (MDT), the average of the high and low temperature, for consecutive days at a location. At the end of this process, our bank balances would look the same, so the bank might think, “Oh, nothing changed this month.” But, with a time-series database, the bank would see, “Hey, these two people keep sending each other $10, there’s likely a deeper relationship here.” Tracking this nuance, our month-ending account balance takes on greater meaning. Then, if you send me $10, the same process happens in reverse. If I send you $10, a traditional bank database would debit my account and credit your account. Unfortunately, these totals missed the nuances of how the underlying changes over time contributed to these static values. In the past, our view of time-series data was more static the daily highs and lows in temperature, the opening and closing value of the stock market, or even the daily or cumulative hospitalizations due to COVID-19. Time-series data can track changes over milliseconds, days, or even years. Time-series data is a sequence of data points collected over time intervals, giving us the ability to track changes over time. To answer those questions, let me start with a more in-depth description of what time-series data is and how you might benefit from using a time-series database, and leave you with a few ways to start exploring time-series data and performing your own analysis. Specifically, does it really matter if you start with a database specialized for time-series data – or can you easily transition to one later? In fact, over the past two years, time-series databases (TSDBs) have steadily remained the fastest growing category of databases: Source: DB-Engines, November 2020Īs the developers of an open-source time-series database, my team and I are often asked about this trend and how it should factor into your decisions about which database to select.
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Software developer usage patterns already reflect the same trend. (You can read how some real-world teams, like those tracking real-time flight data or building platforms for sustainable farming mine their time-series metrics in our Developer Q&A series). Businesses, governments, schools, and communities, large and small, are finding invaluable ways to mine value from analyzing time-series data. Having access to detailed, feature rich time-series data has become one of the most valuable commodities in our information-hungry world. COVID-19 and the global pandemic have made billions of people across the globe relentless consumers of time-series data, demanding accurate and timely information about the daily trend of various COVID-19 statistics. Our retail industry monitors how their assets move with such precision and efficiency that cheap same-day delivery is a luxury that many of us take for granted.īut, far from stock trends, self-driving cars, and knowing the exact minute your next online purchase will arrive, 2020 has provided the most personal example of how time-series data collection and analysis affects our daily lives.įor the first time in history, worldwide interest in time-series data has peaked in the most unexpected way.
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Our smart homes monitor what’s going on inside of them to regulate temperature, identify intruders, and respond to our every beck-and-call (“Alexa, play some relaxing music”).(Read how financial applications like Wise and an automated crypto trading bot collect, store, and analyze data.) Autonomous trading algorithms continuously collect data on how the markets are changing to optimize returns, both in the short and long-term.Self-driving cars continuously collect data about how their environment is changing, adjusting based on weather conditions, potholes, and countless other variables.However, if you look closely, you’ll notice that each of these applications requires a special kind of data:
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Here’s a riddle: what do self-driving Teslas, autonomous Wall Street trading algorithms, smart homes, transportation networks that fulfill lightning-fast same-day deliveries, and tracking the daily COVID-19 statistics and air quality in your community have in common?įor one, they are signs that our world is changing at warp speed, thanks to our ability to capture and analyze more and more data faster than ever before. (Note: this post was originally published in November 2018, and republished in December 2020 with updated graphs, new trends, and relevant technical information.)
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A primer on time-series data, what it is, where to store it, and how to analyze it to gain powerful insights.