May 19, 2024
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With this second investment round in the past 12 months, Timescale has received $180 million in total funding at a valuation of over $1 billion, demonstrating the success of its business amid the meteoric rise of time-series data. The funding round was led by Tiger Global and other investors like Redpoint Ventures, Benchmark, Two Sigma Ventures, New Enterprise Associates and Icon Ventures. Timescale has experienced a 7x community increase and a 20x revenue growth over the last two years. Tens of thousands of corporations, including Akamai, Cisco WebEx, Comcast, DigitalOcean, GE, IBM, Microsoft, Pfizer, Samsung, Schneider Electric, Uber, Walmart, and others, as well as over 500 paying clients are served by the business today.

Time-series data workloads are a brand-new and quickly expanding subset of software applications that use data gathered over time to observe, comprehend, and anticipate trends. Major trends including the growth of IoT / machine data, IT observability, and web3 / crypto applications all contributed to this wave. It is also due to a bigger trend, which is that businesses are gathering data more reliably than ever before to create significantly superior data-driven product experiences as computing and storage get more powerful and affordable. The most accurate representation of data is through time series, which follow change rather than static data points.           

Today, companies in every sector gather time-series data. This could be done to assist farmers in addressing climate change by measuring the temperature and humidity of the soil, monitoring streaming stats to find the most well-liked songs and performers, real-time monitoring of NFT operations, or creating fresh gaming scenarios and more. It tracks every activity a user makes within a piece of software, as well as the functionality of the infrastructure that supports it, to assist in resolving support issues and boost client satisfaction.      

Traditional databases were not designed to manage the performance and scale issues posed by tracking data at the time-series resolution. For these workloads, Timescale has developed a brand-new, best-in-class database that is based on the industry standards of SQL and PostgreSQL. This is so that TimescaleDB, unlike general-purpose databases, is designed specifically for time-series data. Additionally, TimescaleDB differs from other time-series databases in that it combines a relational database (PostgreSQL database) with a time-series database.