The role of data scientists, data engineers, and analysts at financial institutions includes (but is not limited to) protecting hundreds of billions of dollars worth of assets and protecting investors from trillion-dollar impacts, say from a flash crash. One of the biggest technical challenges underlying these problems is scaling time series manipulation. Tick data, alternative data sets such as geospatial or transactional data, and fundamental economic data are examples of the rich data sources available to financial institutions, all of which are naturally indexed by timestamp. Solving business problems in finance such as risk, fraud, and compliance ultimately rests on being able to aggregate and analyze thousands of time series in parallel. Older technologies, which are RDBMS-based, do not easily scale when analyzing trading strategies or conducting regulatory analyses over years of historical data. Moreover, many existing time series technologies use specialized languages instead of standard SQL or Python-based APIs.