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Data Historians vs Time Series Databases: A Comparative Analysis

Choosing your data storage solution is a critical decision for your business. How data is collected, stored, and analyzed can impact how you harness the full potential of your data, the operations, and the decision-making process. In this blog post, we will compare two data storage solutions: data historians and time series databases.

Data Historians

Data historians are specialized data storage systems designed to capture, store, and retrieve time-series data. They are commonly used in industrial and manufacturing settings. Like every system, they have their strengths and limitations depending on each use case:


  1. Data handling: Data historians efficiently manage time-stamped data and can be a good fit for certain applications with continuous time-series information.
  2. Aggregation and compression: They offer data compression and aggregation capabilities, that can be helpful for efficient data analysis.
  3. Data retention: Data historians can store large volumes of historical data, which is important for trend analysis, anomaly detection, and to ensure that it is compliant with industry regulations.
  4. Query performance: Data historians are optimized for fast data access, which is useful when decisions need to be made in real-time.  
  5. Scalability: These systems can scale horizontally and vertically to accommodate growing data volumes. 


  1. Niche application: Data historians are tailored for specific use cases, which are industrial and manufacturing sectors. This can be a limitation since they are not flexible for general data storage needs.
  2. Cost and complexity: These systems can be expensive to get and mostly maintain. That refers to costs like licensing, hardware, and specialized training that are often needed to set up the system effectively and optimize performance and configuring it may require domain-specific knowledge. They also have unique features and configuration options that require a deeper understanding.  
  3. Limited data types: They may not support diverse data types, limiting their relevance for businesses dealing with multiple data sources, which is very common. 
  4. Vendor dependency: Choosing a data historian system inevitably restricts businesses to a specific vendor, limiting their flexibility.

Time Series Databases

Time series databases are specialized storage systems designed to handle timestamped data efficiently.  

They allow for storing large amounts of time series data in a format that supports easy insertion and retrieval, enabling complex analysis. These databases are essential for applications dealing with continuous data streams like IoT, monitoring systems, and financial analysis. There are many open-source time series databases options available, making them great for businesses on a budget. Here are the main pros and cons: 


  1. Query performance: Time series databases offer higher query performance and faster data retrieval, making them suitable for real-time and analytical use cases.
  2. Scalability: They are designed to be highly scalable, which means that they can handle large and growing volumes of time-stamped data. This is important in industries like IoT and finance.
  3. Market versatility and data handling: Time series databases are adaptable to multiple industries and use cases since they can handle data from diverse sources. Some databases like CrateDB not only handle efficiently time-stamped data but also other types of data like JSON, geospatial, full-text and more.  
  4. Easy integration with other tools: These databases are normally easy to integrate with popular data analysis and visualization tools like Grafana, Kafka, Apache Airflow and many others.
  5. Data compression: Many time series databases support data compression, reducing storage costs and making them more efficient. 
  6. Real-time and historical data analysis: Time series databases enable real-time analysis for applications requiring immediate insights from streaming data, like monitoring and predictive maintenance. They also facilitate historical analysis, helping organizations gain valuable insights from past data.


  1. Learning curve: Implementing and using time series databases may require a learning curve for staff. Training and adapting to their specialized features can take time and resources, depending on your chosen database solution.  
  2. Data migration: Migrating historical data from legacy systems to time series databases can be time-consuming since the data may need to be transformed, cleaned, and validated.
  3. Complexity of integration: With the proper support, integrating time series databases into an existing infrastructure can be easier because of the need to adapt current systems, data formats, and protocols to meet the requirements of time series databases.

Wrapping Up: Making The Right Choice 

Now that we've explored the pros and cons of both data storage solutions, deciding the best solution for your business should be easier. 

Data historian's strengths make them a good fit for use cases related to industries like manufacturing and process control. However, their effectiveness may be limited in other areas as they excel in specific cases.

On the other hand, time series database adaptability makes them the right choice for many businesses since they can be used in various industries and are versatile in managing time-stamped data. A good example is CrateDB.

CrateDB's hyper-fast speed, and seamless integration with other systems make it a popular choice for businesses of all sizes. Its high scalability and built-in availability allow it to efficiently handle ever-growing datasets, while its flexible data model reduces the need for multiple databases. The continuous support from CrateDB experts and the community simplifies integration processes.

While data historians have traditionally been used in industrial and manufacturing projects, time series databases like CrateDB can be a good fit as well, when businesses require real-time insights, scalability, versatile data handling, high query performance, and adaptability to diverse data sources.  

With all of these features, CrateDB perfect can be for monitoring, predictive maintenance, and quality control in industrial and manufacturing applications. A good example is SPGo!’s digital inspection of conveyor belts that revolutionized the market with CrateDB and other examples where time-series data are key for manufacturers.

In the end, the choice between these two solutions depends on your business's data needs and goals.