NextGenBeing Founder
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Loading...Introduction to Time-Series Analytics
When I first started working with time-series data, I was surprised by how quickly our database grew. We were handling over 10 million requests per day, and our traditional relational database was struggling to keep up. That's when I discovered InfluxDB, a purpose-built time-series database that could handle our scale. But I soon realized that building a scalable time-series analytics platform required more than just a database - it needed a robust query language, a powerful visualization tool, and a solid understanding of the trade-offs involved.
Why InfluxDB 2.6?
I chose InfluxDB 2.6 for its high-performance, columnar storage, and support for Flux, a powerful query language. With InfluxDB, I could handle large amounts of time-stamped data and perform complex queries with ease. But what really sold me on InfluxDB was its ability to scale horizontally, which meant that I could easily add more nodes to my cluster as our dataset grew.
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