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Challenges when Analyzing Time-Series Data

As all industries continues to evolve, the significance of time-series data becomes clear. This type of data is characterized by its vast volume, presenting a challenge and an opportunity for most smart industries. With various data formats, enclosing a broad range of signals and sensors, it is crucial to combine high-frequency and low-frequency data to derive meaningful insights. By harnessing the right time-series database, companies can unlock the potential for valuable insights, revolutionizing their operations and driving growth. 

Here are the three main challenges when analyzing time-series data.


High availability must be considered as it represents one of the key challenges for analyzing time-series data. Availability refers to the proportion of time that a system can be used. In industries where data is produced around the clock, data availability becomes crucial. It ensures uninterrupted production, enables real-time data analytics, and guarantees high performance and quality.

Availability ensures time series data is current and up-to-date, essential for accurate analysis. Having access to the data at all times makes it easier to identify trends crucial for decision-making.

CrateDB provides a perfect solution for overcoming time-series data availability concerns with its shared-nothing architecture, efficient data replication, automated failover mechanism, advanced logical replication capabilities, and enhanced data locality optimization.  

Semi-structured data

It is common to encounter complex machines consisting of various submodules in industrial processes. These submodules can further consist of further additional modules, creating a nested structure. When collecting data from a single machine and its nested submodules, it is crucial to maintain a relationship between the data and understand how each module contributes to the overall data from the machine. This inherent structure in the data often involves multiple time series. It is advisable to preserve this nested data structure in the database to facilitate the analysis and ensure fast and efficient search and aggregations, especially for semi-structured data. This can be easily achieved by using CrateDB as a time-series database that ingests and manages massive amounts of semi-structured data from diverse sources. 

Data frequency

Another common challenge in many use cases is the frequency of data. Frequencies play a crucial role in time series data, determining how often the data is sent within a given interval. In industrial settings, we often encounter data with varying frequencies. For instance, machines and sensors may send hundreds or even hundreds of thousands of signals per second. 

This data must be combined with business and static data to provide a comprehensive picture. Storing this data can be tricky, as many time series data require a data model that holds one record per measurement. While this approach may bring some overhead, using a different database for non-time series data is often necessary. 

This raises the question of whether one database is enough. With CrateDB, however, this challenge is overcome. You can effectively store and analyze all your data - both sensor and business data - in CrateDB by using features such as arrays, objects and joins, which enables you to gain insights from different sources. CrateDB provides a convenient and efficient solution for handling various types of data and is also a perfect fit as an IoT database.

To learn more, watch this 40-minute talk with CrateDB's Developer Advocate, Marija Selakovic.