Time Series Databases Software Market is Expected to Reach USD 711.3 million by 2030

Mar 2024


As per the report published by Kings Research, the global Time Series Databases Software Market 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.

The increasing use of IoT devices has fueled the demand for specialized databases, such as time-series databases, which are optimized for efficient management and analysis of time-stamped or temporal data. Adopting a time-series database for IoT data can enable organizations to gain valuable insights that can be turned into actionable insights that drive better decision-making, real-time monitoring, and predictive maintenance, among others.

Key Insights

  • Based on deployment, the cloud-based segment is anticipated to register a significant CAGR of 12.0%the forecast period from 2023 to 2030
  • Based on organization size, the large enterprises segment is projected to depict a substantial CAGR of 9.7% over the assessment timeline from 2023 to 2030
  • Based on region, Asia Pacific is set to witness the highest CAGR of 11.9% over 2023-2030

InfluxData Launches New Generation Time Series Engine, InfluxDB, to Support SQL Language Series

October 2022: InfluxData, the time-series platform, announced the deployment of its next-generation time-series engine that revolutionizes InfluxDB into a columnar real-time data platform. The new engine boasts high-speed data ingestion with unbounded cardinality and is built to handle the full extent of time-series data while providing support for SQL language queries.

Time-Series Databases Enhancing Security Monitoring to Propel Market Growth

Time-series databases can enhance security monitoring by providing a way to store and analyze vast amounts of time-stamped data from various sources, such as network devices, servers, and applications. With a traditional relational database, querying and analyzing large datasets can be slow and resource-intensive, resulting in delays in identifying security threats and risks. However, time-series databases are optimized for handling time-stamped data and can scale to handle large volumes of data with high write and query rates. This makes them well-suited for use in security monitoring where real-time analysis is crucial for quickly detecting and responding to security incidents. Additionally, time-series databases can be used to detect and analyze patterns in data that may indicate security threats, making them an important tool in enhancing security monitoring.

Technological Advancements in Time Series Databases To Create New Opportunities for Market Development

Time-series databases can be used in conjunction with machine learning algorithms to analyze and predict patterns in time-stamped data. Machine learning techniques, such as regression analysis, clustering, and neural networks, can be used to analyze the data stored in time-series databases to identify trends, anomalies, and patterns that may help predict future outcomes. For example, time-series databases can be used in the context of predictive maintenance to identify patterns in sensor data that may highlight potential equipment failures. Additionally, machine learning models can be trained on time-series data stored in a time-series database to predict future trends and draw forecasts.

Asia Pacific to Observe the Fastest Growth at a CAGR of 11.9% Over the Forecast Period

The usage of these databases in China can be seen across various industries, such as finance, manufacturing, and healthcare, where it is used for data monitoring, reporting, and analysis purposes. The Indian IT/software sector has seen significant growth in the adoption and usage of time series databases, with companies in industries such as finance, manufacturing, and healthcare relying on these databases for critical data monitoring, reporting, and analysis.

CEIC Databases is one of the major providers of macroeconomic and industry-specific time series data for India, offering access to over 163,000 time series. For instance, in April 2020, InfluxData expanded its geographical presence in the Asia Pacific region, partnering with Digital China and other companies to provide access to its platform. Additionally, the increasing demand for time series database monitoring solutions is likely to propel market growth.

Market Players Concentrating on Product Development & Innovation to Drive Market Development

The global time series databases software market is fragmented with key players such as InfluxData Inc., Prometheus Authors, Microsoft, MongoDB, Inc., Timescale Inc., InfluxDB, KX Systems, Inc., Amazon Web Services, Inc., DataStax, Trendalyze, Inc., QuasarDB SAS, QuestDB, IBM Corporation. Companies employ a variety of strategic measures such as acquisitions, mergers, partnerships, product introductions, and collaborations to expand their business globally and enhance their competitive position.

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