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How to store time series data

Finding the best way to store time series data efficiently is key for time series data analysis, monitoring, and real-time processing. Choosing the best storage solution depends on several factors depending on your use case. This includes your data's volume, speed, query requirements, and scalability needs.  

You should always consider your application's specific requirements when choosing a storage solution for time series data. With that in mind, we listed the 4 main options you can consider: 

  • Time-Series Databases: they are specifically designed to efficiently store, query, and analyze time series data, providing advanced storage structures and query functionalities tailored to time series data. But sometimes they are limited to time series data and struggle to store any other type of data, which are often useful for a more thorough analysis.
  • NoSQL Databases: These databases are well-suited for certain use cases, like the ones with simpler data models. However, this type of database may face some challenges like the complexity when it comes to data modeling when dealing with hierarchical or nested structures, the limited ecosystem for time series analysis, dealing with high write rates and frequent data updates.  
  • Relational Databases: Traditional relational databases such as PostgreSQL or MySQL are a good choice for applications with moderate data volumes and requiring data integrity. These databases can handle structured data with complex relationships and fixed schemas. However, they may not be the best choice for time series data, which is dynamic, high-velocity, and time-sensitive in nature.
  • Data Warehouses: Some data warehousing solutions can be a good choice for time series data when it comes to advanced analytics and reporting features. They may not be the most budget-friendly option for storing large volumes of time series data or applications with high-velocity data streams. 

Storing your data with a Time-Series Database

A time-series database, or TSDB, is a popular choice when it comes to storing time series data. For starters, it is purpose-built to handle the specific characteristics and requirements of time series data. TSDB has the capability to streamline the process of storing, querying, and analyzing time series data in most use cases working with this type of data. 

There are a lot of options on the market, but how to select the best time series database?

CrateDB stands out as a top time series database for its ability to handle and manage massive amounts of data from various sources. As a native SQL DBMS, CrateDB not only simplifies the learning process but also seamlessly integrates with other systems. This tutorial also explains how to optimize storage of historical time-series data

Unlike many time-series databases, CrateDB does not limit you to proprietary data access interfaces. With its distributed database architecture, columnar storage, SQL interface, and robust time series functionality, CrateDB meets the specific requirements of handling time series data. Additionally, being an open-source time series database, CrateDB offers flexibility and adaptability to any business.  

Handling missing values in time-series data

Dealing with missing values in time-series data is crucial to ensure accurate and reliable analysis. Properly handling missing values can also lead to more efficient storage of time-series data. Imputing or addressing missing values helps avoid unnecessary storage of null or undefined entries and optimizes the utilization of storage resources. There are several strategies: 

  • Remove Missing Values: The most straightforward approach is to remove rows with missing values. However, this should be done cautiously, especially if consecutive time points still need to be included, as it may lead to a loss of valuable temporal information.
  • Forward Fill or Backward Fill: Use the value from the previous time point (backward fill) or the next time point (forward fill) to fill in missing values. This method is suitable when missing values occur sporadically.
  • Interpolation: Interpolate missing values based on the values of adjacent time points. Methods like linear interpolation or spline interpolation can be applied.
  • Mean/Median Imputation: Replace missing values with the mean or median of the observed values. This method is simple but may not be suitable if there are significant fluctuations in the data.
It's important to remember that there is no single approach that works for all time series data. The best method depends on its specific characteristics. It's recommended to try out different methods and consider their performance carefully to determine the most suitable approach for your time-series analysis.