The data that is used in financial markets is time oriented and multidimensional, which makes it difficult to manage in either relational or timeseries databases. To make this information more manageable the team at Alapaca built a new data store specifically for retrieving and analyzing data generated by trading markets. In this episode Hitoshi Harada, the CTO of Alapaca, and Christopher Ryan, their lead software engineer, explain their motivation for building MarketStore, how it operates, and how it has helped to simplify their development workflows.
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- Your host is Tobias Macey and today I’m interviewing Christopher Ryan and Hitoshi Harada about MarketStore, a storage server for large volumes of financial timeseries data
- How did you get involved in the area of data management?
- What was your motivation for creating MarketStore?
- What are the characteristics of financial time series data that make it challenging to manage?
- What are some of the workflows that MarketStore is used for at Alpaca and how were they managed before it was available?
- With MarketStore’s data coming from multiple third party services, how are you managing to keep the DB up-to-date and in sync with those services?
- What is the worst case scenario if there is a total failure in the data store?
- What guards have you built to prevent such a situation from occurring?
- Since MarketStore is used for querying and analyzing data having to do with financial markets and there are potentially large quantities of money being staked on the results of that analysis, how do you ensure that the operations being performed in MarketStore are accurate and repeatable?
- What were the most challenging aspects of building MarketStore and integrating it into the rest of your systems?
- Motivation for open sourcing the code?
- What is the next planned major feature for MarketStore, and what use-case is it aiming to support?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Algorithmic Trading
- OHLC (Open-High-Low-Close)
- Timeseries Database List