Time Series Databases Software Market Size

The global Time Series Databases Software Market size was valued at USD 320.6 million in 2022 and is projected to reach USD 711.3 million by 2030, growing at a CAGR of 10.68% from 2023 to 2030. In the scope of work, the report includes solutions offered by companies such as InfluxData Inc., Prometheus Authors, Microsoft, Timescale Inc., Amazon Web Services, Inc., DataStax, Trendalyze, Inc., QuasarDB SAS, QuestDB, InfluxDB, MongoDB, Inc., IBM Corporation, KX Systems, Inc. and Others.

Time series databases are particularly useful for monitoring smart security systems and are expected to contribute to the growth of the market for time series database software. These databases provide an efficient way to handle time-stamped data from security sensors and other devices, allowing for real-time monitoring and analysis of security breaches. However, one of the challenges associated with time series database software is the high installation cost. Nonetheless, it's worth noting that while the upfront cost of installation may be a significant challenge for the time series databases software market.

Analyst’s Review

The time series databases software market is expected to grow in the foreseeable future due to the increasing adoption of Internet of Things (IoT) devices and the consequent growth in time-stamped data. Technological advancements that allow for cost-effective product development are anticipated to contribute to this considerable market growth.

The benefits of time series databases, such as their ease in currency, forex, and security price monitoring, are likely to expand the application scope of the product and boost market development. Additionally, the superior applications of these databases, including detecting smart security breaches, storing payment databases, and facilitating energy sector applications, are projected to drive the demand for time series database software on a global scale.

Market Definition

A time series database or TSDB is a software application that is designed and optimized to efficiently store, retrieve, and manage time series data. This type of database is optimized for managing data that has timestamps and value pairs and is able to quickly process and analyze large volumes of data with minimal latency. They are commonly used in various applications such as real-time data analytics, sensor data management, and DevOps use cases, all of which are equipped with IoT.

These databases are engineered and fine-tuned to efficiently handle time-stamped or temporal data and have the capability to expeditiously analyze time series data using SQL. They further feature inbuilt analytic functions for processes such as smoothing, approximation, and interpolation, thus enhancing the overall speed and efficiency of data analysis and processing.

Due to their specialized nature in effectively managing time-stamped data and accommodating varied types of temporal data, time series databases have gained significant popularity in recent years. Their unique architecture allows them to function as a dedicated database designed to handle large volumes of time-series data within a single platform, thereby reducing the need for multiple databases to manage different types of data. This has been a key factor contributing to their rise in popularity, as it simplifies data management and enables faster and more efficient analysis of time-series data.

These databases are designed for speed and can operate optimally in any cloud-based, or web-based environment. They are further designed to handle huge numbers of data points, process them quickly, and allow for rapid search and analysis. Many of these databases offer time series collections for handling time-series data, with some employing compression algorithms to efficiently manage the data.

Overall, time series databases are specialized software systems that excel in storing, managing, and serving time-stamped data. They are designed to handle large datasets and offer features such as built-in analytics, compression algorithms, and support for SQL queries for faster data processing and analysis.

Time Series Databases Software Market Dynamics

Time series databases have seen increasing adoption in recent times due to their ability to efficiently manage and analyze various types of time-series data within a single, purpose-built platform. This has become especially important in the context of Industry 4.0, which encompasses the integration of advanced technologies and automation in manufacturing and production processes.

To capture the opportunities presented by Industry 4.0 technologies, it is critical to establish the foundations for digital transformation. An essential component in this process is having a robust time-series database that can handle high throughput of data and support real-time querying. The aforementioned factors are likely to propel time series databases software market growth in the near future.

The field of time-series database management seems to be facing a shortage of skilled professionals, which could potentially hinder the market growth. However, the scarcity of skilled professionals may limit the effective adoption and use of these tools. Organizations in this field should prioritize investment in training and upskilling their workforce to bridge this gap and leverage the full potential of time-series databases for Industrial IoT and beyond.

Segmentation Analysis

The global market is segmented based on deployment, organization size, and geography.

By Deployment

Based on deployment, the market is bifurcated into cloud-based and web-based. The web-based segment held the largest time series databases software market share of 53.97% in 2022. Several time series database software options are available as web-based solutions, providing businesses with the ability to access and analyze time-stamped data from any location with an internet connection.

Several companies offer web-based interfaces for platforms such as InfluxDB, Amazon Timestream, and Dewesoft Historian, allowing users to monitor and analyze data in real time from any internet-accessible device.

By Organization Size

Based on organization size, the time series databases software market is categorized into large enterprises and small & medium enterprises. The large enterprises segment accumulated the largest market share of 66.86% in 2022. Large enterprises are increasingly investing in time series database software to manage and analyze their time-stamped data.

With the ability to efficiently handle and analyze large volumes of data, time series databases are particularly relevant for large enterprises with complex data management needs. Additionally, as the use of IoT devices and sensors continues to grow, the demand for time series database software is expected to increase, leading to further investments from large enterprises in this area.

Time Series Databases Software Market Regional Analysis

Based on region, the global market is classified into North America, Europe, Asia Pacific, MEA, and Latin America.

Time Series Databases Software Market Size & Share, By Region, 2023-2030

The North America Time Series Databases Software Market share stood around 37.06% in 2022 in the global market, with a valuation of USD 118.8 million due to the increasing number of organizations using time series database software to manage and analyze their time-stamped data. Additionally, utility companies such as National Grid and Con Edison use time series databases to monitor and analyze power consumption data in real time.

Another example is the financial industry, where time series databases can assist with monitoring financial market data, such as stock price changes and exchange rate fluctuations. Hedge funds, investment banks, and financial institutions often use specialized software, such as Kdb+ and TickVault, to manage and analyze time-stamped data related to financial markets.

For instance, in April 2023, KX launched kdb Insights Enterprise on Microsoft Azure, which is a data timehouse platform that offers AI, machine learning (ML), and analytics capabilities for businesses seeking to achieve AI-driven innovation while benefiting from improved time-based visibility on business interactions, decisions, and digital infrastructure.

Competitive Landscape

The time series databases software market report will provide valuable insight with an emphasis on the consolidated nature of the industry. Prominent players are focusing on several key business strategies such as partnerships, mergers and acquisitions, product innovations, and joint ventures to expand their product portfolio and increase their market shares across different regions.

Expansion & investments are the major strategic initiatives adopted by companies in this sector. Industry players are investing extensively in R&D activities, building new manufacturing facilities, and supply chain optimization.

List of Key Companies in Time Series Databases Software Market

InfluxData Inc.
Prometheus Authors
Microsoft
Timescale Inc.
Amazon Web Services, Inc.
DataStax
Trendalyze, Inc.
QuasarDB SAS
QuestDB
InfluxDB
MongoDB, Inc.
IBM Corporation
KX Systems, Inc.

Key Industry Developments

May 2023 (Collaboration): KX and Data Intellect joined forces to offer improved services for time series analytics and AI use cases based on kdb+, benefitting businesses seeking to leverage time-stamped data for AI-driven innovation by enhancing their time-series analytics capabilities.
April 2023 (Launch): InfluxData announced its latest offering, InfluxDB 3.0, which provides enhanced time series capabilities and analytics functionalities across its product range. This rebuilt database and storage engine for time series analytics is readily available in InfluxData's cloud products.
November 2022 (Launch): Timescale announced the private beta launch of an innovative low-cost object storage platform built on Amazon S3. This consumption-based storage layer promises to offer Timescale Cloud customers the ability to store an unlimited amount of data, empowering their applications to leverage massive amounts of time-series data without high overhead costs.

The Global Time Series Databases Software Market is Segmented as:

By Deployment

Cloud-based
Web-based

By Organization Size

Large Enterprises
Small & Medium Enterprises

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- France
- UK
- Spain
- Germany
- Italy
- Russia
- Rest of Europe
Asia Pacific
- China
- Japan
- India
- South Korea
- Rest of Asia Pacific
Middle East & Africa
- GCC
- North Africa
- South Africa
- Rest of the Middle East & Africa
Latin America
- Brazil
- Argentina
- Rest of Latin America