Microsoft has entered into a partnership with Timescale that has introduced support for TimescaleDB on Azure Database for PostgreSQL. Timescale is an open-source time-series database powered by PostgreSQL optimized for fast ingest and complex queries. For people looking to build IoT time-series workloads TimescaleDB is a common choice. While it supports traditional SQL syntax, it scales in ways previously reserved for non-acid compliant systems. Some of the features include Transparent time and space partitioning, high data write rates, right-sized chunks, and parallelized operations. We will review how to data model, build the architecture to support ingestion, and then dive into a demo of the New York Taxi data set and figure out Seasonal ARIMA, Autoregressive Integrated Moving Average, with R.

You will learn:

• Understand TimescaleDB and how it can be utilized on PostgreSQL
• Review the architecture for developing data models, table design, and node architecture for TimescaleDB
• Demonstrate TimescaleDB on a billion-row data set and use R to calculate Seasonal ARIMA to find trends in cab fare data