Welcome to HackerNoon’s interview series. This section is curated by our Editorial team to spotlight bold and disruptive startups across the globe. If you’ve been nominated, and answer these questions Startups-Of-The-Year create a brand profile here. Hey Hackers, TDengine has been nominated in HackerNoon's annual Startup of the Year awards in Campbell, CA. Please vote for us here: https://hackernoon.com/startups/north-america/north-america-campbell-ca-usa Read more about us below to understand why we deserve your vote. Meet TDengine is the only open-source time-series database to solve the high cardinality issue with a unique architecture supporting billions of data points while outperforming general-purpose and legacy time-series databases in data ingestion, querying, and data compression. TDengine TDengine combines a database with caching, stream processing, and data subscription as a complete, purpose-built solution for time-series data optimized for the Internet of Things (IoT), connected cars, industrial IoT, and DevOps. In less than four years, TDengine has become immensely popular, garnered over 21,000 stars on GitHub, 4,400 forks, and over 300,000 installations in more than 50 countries worldwide, and drove a 200% increase in customer adoption in 2022. My Role As the founder, core developer, and chief executive officer of TDengine, I have led the development of TDengine from the ground up, guiding the product roadmap and features to build an innovative open-source time-series database. My primary role is providing strategic vision and technical direction for our talented teams of developers and engineers as we seek to continuously advance TDengine as a leading platform for time-series data management and analytics. I oversee all aspects of our company's operations, from products to people, fostering a culture of excellence and innovation. As CEO, I aim to lead TDengine to the forefront of the time-series data space while managing a high-performing global team committed to delivering outstanding value for our users and customers. But having a background in software development myself, I recognize the necessity of community support and developer enablement in the open-source space and am committed to making a product that helps developers succeed above all. How We're Disrupting/Improving the Time-Series Database Industry At TDengine, we recognize that the Internet of Things (IoT) and connected device deployments generate massive amounts of time-stamped data daily. This time-series data is extremely valuable for analysis but also incredibly challenging from a data management perspective. Many existing databases need to be explicitly architected for the demands of IoT and time-series workloads. This often results in organizations hitting scalability or cost barriers trying to leverage IoT data, especially as device volumes grow. With TDengine, our core innovation is providing an open-source time-series database designed from the ground up to ingest, process, and analyze IoT and time-series data at scale. By optimizing everything from storage to query processing for time-series needs, we empower organizations to capitalize on the value of their connected device or sensor data. This unlocks game-changing possibilities for companies to build transformative IoT applications for use cases ranging from predictive maintenance to smart cities and beyond. Standing Out From The Crowd There are a few key ways in which TDengine differentiates itself in the database landscape: We are laser-focused on time-series data workloads. TDengine is purpose-built and optimized specifically for managing and analyzing time-series data at scale. This differentiates us from general-purpose databases that treat time series as just another data type. Our innovative technical architecture delivers game-changing performance for ingesting, storing, and querying time-series data. TSBS benchmarks show that we outperform other databases by orders of magnitude in metrics like ingestion speed while reducing storage costs. A unique advantage of our platform is the ability to share time-series data insights across teams and systems easily. We offer a variety of connectors – including developer-focused options like Kafka and industry interfaces like PI System – enabling customers to unlock the full value of their time-series data for advanced analytics and AI applications. We also support standard SQL for flexibility and ease of use, plus extensions for specialized time-series queries, so users get the best of both worlds. As an open-source platform, we accept contributions from our community and offer our OSS edition to developers for free. Our Predictions/Thoughts on the Time-Series Database Industry in 2023 As we enter 2023, I foresee significant growth for the time-series database market, especially among traditional industrial organizations. The reason is simple - with the rise of IoT, more physical assets and processes are connected, generating massive amounts of time-stamped data. However, most legacy IT systems were not built to handle the extreme ingestion speeds, storage demands, and analytical capabilities required to capitalize on large-scale industrial time-series data. As companies look to modernize, purpose-built time-series databases will be vital in overcoming these challenges. Rather than retrofitting general-purpose databases, we believe businesses need solutions explicitly optimized for time-series workloads from the ground up. This allows them to fully harness time-series data to advance IoT-driven initiatives around predictive maintenance, operational optimization, and more. As IT modernization accelerates, we foresee solid industry-wide growth for time-series databases. To remain competitive, legacy sectors like manufacturing, energy, and transportation must embrace these modern data platforms. The companies that successfully transform their time-series data architecture position themselves to survive and thrive with IoT innovation in 2023 and beyond. It's an exciting time for industrial organizations to tap into the power of time-series data. What word defines the state of time-series databases in 2023? Momentum. That's the word I'd use to define the state of time-series databases in 2023. Time-series databases have tremendous momentum right now, driven by the explosion of IoT devices, the maturing of purpose-built time-series technology, and advances in cloud infrastructure and analytics. Together, these trends are creating a surge in adoption as time-series data transitions from an exciting concept to a core building block underpinning digital transformation across industries. With IoT continuing to increase and time-series databases seeing increasing adoption across industries, 2023 will be a breakout year. We see solid tailwinds for time-series databases to become a mainstream pillar of the modern data stack as organizations embrace time-series data-centric strategies. The space has a fascinating trajectory poised to accelerate in 2023. Why we decided to participate in HackerNoon's Startup of the Year awards We're thrilled to participate in HackerNoon's Startup of the Year awards to gain valuable exposure for our emerging company within the tech community. As a startup, telling our story and showcasing our platform to new audiences is always challenging, so the visibility and credibility associated with these prestigious awards provide an incredible opportunity to reach potential users, partners, and developers we otherwise may not have. Competing also lets us benchmark against peers and learn from the evaluation process. Earning recognition further validates our team's efforts in a crowded startup landscape. Ultimately, we build technology to solve real problems, so participating in these awards helps spread our vision and brings us one step closer to becoming a trusted brand worldwide. It's all about increasing awareness of our innovative solutions. Final Thoughts At TDengine, we are on a mission to revolutionize how organizations harness the power of time-series data. Our journey so far has been exhilarating - from conceptualizing a purpose-built time-series database to leading TDengine's astronomical rise with over 21,000 GitHub stars and 300,000 installations globally in just four years. But this is only the beginning. Looking ahead, we are committed to continuous innovation to make TDengine the go-to open-source platform for time-series workloads. And we are humbled by the opportunity through platforms like HackerNoon to tell our story and bring TDengine to more users worldwide. We hope sharing our vision and unique approach to time-series data will resonate with the HackerNoon community. Thank you for considering our startup on this exciting journey to shape the future of time-series technology. Please vote for TDengine as Startup of the Year today! Your support allows us to spread our mission even further. Vote for us today: https://hackernoon.com/startups/north-america/north-america-campbell-ca-usa
