Now that you've had some hands-on experience with TimescaleDB, hopefully you can see how many of Timescale's powerful features can help you manage your time-series data while easily mining for deeper insights. 💥
To continue your exploration of TimescaleDB, here are some valuable next steps to help you on your way to becoming a time-series superhero.
One of the first things most developers want to do is look at the data they're currently working with in the database. There are a number of methods for importing data that you currently have, whether it exists in another database or a .csv file.
Look at the how-to guide on Migrating Data for more help and suggestions of where to start.
Working with sample data can teach you a lot about TimescaleDB, but you might like to try ingesting market data in real time. Check out our related tutorial Ingest real-time financial websocket data and continue ingesting data directly from the Twelve Data financial API.
Time-series data is perfectly suited for viewing with tools like Grafana, Tableau, and Power BI, to name a few. Once you can see trends and query for specific data features using relational data, a whole new world of insights begins to open up.
Check out the growing set of visualization tutorials, showing you how to become a Grafana Superhero and connect to other third party visualization tools.
While this may be the century of big data, the greatest power often happens in connected applications that help turn data into value to users. Using time-series data effectively means you need to get your code connected and working as efficiently as possible.
See the growing list of language Quick Starts to get you up and running with TimescaleDB, including best practices.
Sometimes it's just easier to explore further by having access to additional datasets. We have you covered! 🙌
Have a look some of the other datasets provided for you to dig deeper into time-series data and data analysis using TimescaleDB.
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