Table of Contents
- 1 How do you collect time series data?
- 2 How do you store market data?
- 3 Is SQL good for time series?
- 4 Is Cassandra good for time series data?
- 5 What database is best for storing financial statements?
- 6 Is MySQL good for time series?
- 7 When should I use a NoSQL solution for storing time-series data?
- 8 What is the best database for financial time series data?
How do you collect time series data?
These data points typically consist of successive measurements made from the same source over a time interval and are used to track change over time. Time series data is a collection of observations obtained through repeated measurements over time. Plot the points on a graph, and one of your axes would always be time.
How do you store market data?
How to store financial market data for backtesting
- SQL relational databases.
- Serialized storage of large arrays.
- Key/Value databases (such as Oracle Berkeley DB).
- CSV files.
Is SQL good for time series?
SQL is a widely known, well documented, and expressive querying language (and the 3rd most popular development language as of writing). For these reasons, and many more, we believe SQL is the best language for working with – and getting the most value from – your time-series data.
Is NoSQL good for time series?
NoSQL for time series use cases. Gaining high performance for time series from a SQL database requires significant customization and configuration. Without that, unless you’re working with a very small dataset, a SQL-based database will simply not work properly.
What can you do with time series data?
When time series analysis is used and when it isn’t
- Weather data.
- Rainfall measurements.
- Temperature readings.
- Heart rate monitoring (EKG)
- Brain monitoring (EEG)
- Quarterly sales.
- Stock prices.
- Automated stock trading.
Is Cassandra good for time series data?
Cassandra has good support for modelling time series data wherein each row can have dynamic number of columns. The viewing history data write to read ratio is about 9:1. Since Cassandra is highly efficient with writes, this write heavy workload is a good fit for Cassandra.
What database is best for storing financial statements?
If you had no concerns about storage costs (managed MongoDB Atlas is a bit pricey), MongoDB is a clear winner for storing end-of-day OHLC data. It has the fastest reads and very good writes and multi-record appends.
Is MySQL good for time series?
Usage patterns are similar: a recent survey showed that developers preferred NoSQL to relational databases for time-series data by over 2:1. Relational databases include: MySQL, MariaDB Server, PostgreSQL. We take a different, somewhat heretical stance: relational databases can be quite powerful for time-series data.
What are some examples of time series data?
Time series are one of the most common data types encountered in daily life. Financial prices, weather, home energy usage, and even weight are all examples of data that can be collected at regular intervals.
How do you define time series frequency?
It’s mainly defined by how you perceive your data and the way it’s sampled, but basically you can define a time-series frequency as: ( the number of samples) per your ( sample unit) In your case: 96 per day, since it seems that a “day” is your meaningful sample-unit.
When should I use a NoSQL solution for storing time-series data?
Lesson learned: use a NoSQL solution for storing time-series data when you care about ingesting speed and throughput as much as possible, and for data that is not at the core of your application.
What is the best database for financial time series data?
PostgreSQL turned out to be a pretty solid choice as a general purpose database, which means that both customers data and financial time-series data live in the same database, with strong guarantees of referential integrity. ForecastCycles is a SaaS built with React, Semantic-UI and PostgreSQL.