# Best Time Series Databases - Page 4 *By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)* Time series databases allow businesses to store time-stamped data. A company may adopt a time series database if they need to monitor data in real time or if they are running applications that continuously produce data. Some examples of applications that product time series data include network or [application performance monitoring (APM) software](https://g2.com/categories/application-performance-monitoring-apm) tools, sensor data from IoT devices, financial market data, and a number of security applications, among many others. Time series databases are optimized for storing this data so that it can be easily pulled and analyzed. Time series data is often used when running predictive analytics or machine learning algorithms, enabling users to understand historical data to help predict future outcomes. Some [big data processing and distribution software](https://g2.com/categories/big-data-processing-and-distribution) may provide time series storage functionality. To qualify for inclusion in the Time Series Databases category, a product must: - Store data based on timestamps - Consume data in real time - Allow users to easily pull the data for time series analysis ## How Many Time Series Databases Products Does G2 Track? **Total Products under this Category:** 52 ### Category Stats (May 2026) - **Average Rating**: 4.46/5 - **New Reviews This Quarter**: 2 - **Buyer Segments**: Mid-Market 63% │ Small-Business 38% - **Top Trending Product**: InfluxDB (+0.006) *Last updated: May 19, 2026* ## How Does G2 Rank Time Series Databases Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 1,100+ Authentic Reviews - 52+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which Time Series Databases Is Best for Your Use Case? - **Leader:** [CrateDB](https://www.g2.com/products/cratedb/reviews) - **Highest Performer:** [dataPARC](https://www.g2.com/products/dataparc/reviews) - **Easiest to Use:** [dataPARC](https://www.g2.com/products/dataparc/reviews) - **Top Trending:** [Prometheus](https://www.g2.com/products/prometheus/reviews) - **Best Free Software:** [InfluxDB](https://www.g2.com/products/influxdata-influxdb/reviews) --- **Sponsored** ### QuestDB QuestDB is an open-source, SQL-first time-series database built for the most demanding workloads—from trading floors to mission control. A multi-tier design keeps hot data in native partitions and cold history in Parquet/object storage, queried via one SQL layer. Vectorized, columnar execution delivers high-throughput ingest and millisecond queries. Open formats (Parquet/Arrow) make it AI-ready and lock-in free. Deploy self-hosted or in your cloud (BYOC). [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1761&secure%5Bdisplayable_resource_id%5D=1761&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1761&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=131714&secure%5Bresource_id%5D=1761&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Ftime-series-databases%3Fpage%3D4&secure%5Btoken%5D=f4f6cbbf311d7f28c66487b5bfb03e30e4918e7cee506a8a293d8e82138fd6a3&secure%5Burl%5D=https%3A%2F%2Fquestdb.com&secure%5Burl_type%5D=company_website) --- ## What Is Time Series Databases? [Database Software](https://www.g2.com/categories/database-software) ## What Software Categories Are Similar to Time Series Databases? - [Relational Databases](https://www.g2.com/categories/relational-databases) - [Document Databases](https://www.g2.com/categories/document-databases) - [Database as a Service (DBaaS) Providers](https://www.g2.com/categories/database-as-a-service-dbaas) - [Time Series Intelligence Software](https://www.g2.com/categories/time-series-intelligence) - [Columnar Databases](https://www.g2.com/categories/columnar-databases) - [Vector Database Software](https://www.g2.com/categories/vector-database) - [Real-time Analytic Database Software](https://www.g2.com/categories/real-time-analytic-database) --- ## How Do You Choose the Right Time Series Databases? ### What You Should Know About Time Series Databases Software ### What are Time Series Databases Software? The growing number of different data types leads to the proliferation of different types of databases to facilitate its storage and analysis. Among the fast-growing data types is time series data—data which is timestamped and created over time—which is on the rise with the growth of the internet of things (IoT). Although it is frequently possible to store this data in other types of data stores, time series data has special properties—the data is append-only, making it worthwhile to consider a made-to-order database solution. The first challenge for selecting a database is finding the best structure for the data to be stored. In certain cases there is a natural fit—for example, airline flight information fits very well in a graph database as this mimics real-life patterns—while long-form web content usually slots into document databases. With time series databases software, users are able to store any data that has a timestamp, such as log data, sensor data, and industrial telemetry data. The use cases are manifold. For example, application developers use this software for the purpose of application monitoring to collect data points in real time and better understand application performance. In addition, IoT developers benefit from time series databases as they store and process sensor data, such as smart home devices, to determine how they are performing over time. Key Benefits of Time Series Databases Software - Provide scale and speed, with faster processing time than relational databases - Offer a tool which is specifically geared toward time series data - Allow for structured, organized data storage and management ### Why Use Time Series Databases Software? Like other databases, time series databases are primarily maintained by a database administrator or team. Owing to its wide range of coverage, time series databases are also accessible by several organizations within a company. Departments such as development, IT, billing, and others may also have access to time series databases, pending their assigned uses within the company. **Predict future —** Make informed predictions about future events, observe real-time changes, and capture historical anomalies. **Understand past —** Understand past data with a purpose-built database. ### Who Uses Time Series Databases Software? Time series databases software is highly flexible and is used by diverse teams throughout a company, making it particularly beneficial. For collecting extra large data sets in real time, big data processing and distribution systems are helpful. These tools are built to scale for businesses that are constantly collecting enormous amounts of data. Pulling data sets may be more challenging with big data processing and distribution systems, but the insights received is valuable due to the granularity of the data. **Database administrators —** Time series databases have grown in popularity since they are easier to implement, have greater flexibility, and tend to have faster data retrieval times. Database administrators use these tools to maintain and manage their time series data, ensuring it is properly stored. **Data scientists —** As data science, including artificial intelligence, is fueled by data, it is key that this data is stored in the most effective and efficient manner. This ensures that the data can be queried and analyzed properly. ### Kinds of Time Series Database Software Although all time series databases store timestamped data, they differ in the manner in which this data is stored, the relation between the various data points, and the method in which the data is queried. **Relational databases —** Relational databases are traditional database tools used to align information into rows and columns. The structure allows for easy querying using SQL. Relational databases are used to store both simple information, such as identities and contact information, and complex information critical to business operations. They are highly scalable and can be stored on-premises, in the cloud, or through hybrid systems. **NoSQL databases —** NoSQL databases such as graph databases are a great option for unstructured data. If the user needs to render a value that is easily found by its key, then a key-value store is the fastest and most scalable. The drawback is a much more limited querying ability, implying its limitations for analytic data. Conversely, rendering a user’s email address based on the username or caching web data is a simple and fast solution in a key-value store. ### Time Series Databases Software Features Time series databases, designed specifically for time series data, provide the user with the features they need to successfully store, process, and analyze this data. **Querying using time—** Time series databases allow users to query data using time, allowing them to search or analyze the data across a given time period, even by a fraction of a second. **Data security —** Time series database solutions include data security features to protect the data stored by a business in its databases. **Database creation and maintenance —** Time series databases software allows users to quickly create brand-new relational databases and modify them with ease. **Scalability —** Times series database solutions grow with the data and is hence scalable, with the only pain point being physical or cloud storage capacity. **Operating system (OS) compatibility —** Relational database solutions are compatible with numerous OS. **Recovery —** Whether a database needs to be rolled back or outrightly recovered, some time series database solutions offer recovery features in the event any errors occur. ### Trends Related to Time Series Databases Software **Databases and data aggregation —** Debate continues on the use of relational databases versus NoSQL databases, as data aggregation continues to rise among businesses. Organizations need to determine the best way to store their data as data-driven products and services require immense data backing. In reality, the two database types should be used together. While relational databases excel in structured data storage, NoSQL databases (non-relational databases) shine when there’s no real structure to how data should be collected and stored. Both relational and non-relational databases scale quite easily, given the right software supporting them. This shouldn’t be a "this versus that" debate, but a "this and that" collaboration. **Big data —** Data has become the backbone of conducting business in the information age. As data drives business decisions and trends, it’s important that the data be digestible, easy to follow, and easy to reference. That’s why big data software mostly falls back on relational database solutions. Designed with strict organization, referencing, and referral in mind, relational databases absorb and store massive amounts of data to be later digested in the decision-making process. ### Potential Issues with Time Series Databases Software **Unstructured data —** Time series databases struggle when handling unstructured data. Time series databases hinge on data being structured to properly create relationships between data points and data tables. If a company uses mostly unstructured data, they should consider a NoSQL database solution or data quality software to clean and structure unstructured data. **Query lag —** Time series databases store massive quantities of data, but with that advantage, such database tools run queries slowly on larger data sets. This is mainly due to the sheer volume of data being queried. In situations where queries might traverse significant quantities of data, they should be based on specific values whenever possible. Also, querying strings takes significantly longer than querying numerics, so focusing on the latter may help improve search times. ### Software and Services Related to Time Series Databases Software Finding the right database solution involves finding a tool that best fits a particular use case, including the type of data involved and the type of analysis that needs to be done with that data. The format of the data also determines the right database solution for a given company. **Time series intelligence software —** Users focusing on analyzing, as opposed to just storing time series data, may leverage [time series intelligence software](https://www.g2.com/categories/time-series-intelligence). By utilizing embedded machine learning, time series intelligence tools pull out previously hidden insights—such as microtrends and anomalies—without requiring a human to dig through the data manually, saving a business time and resources. **NoSQL databases —** While relational databases solutions excel with structured data, [NoSQL databases](https://www.g2.com/categories/nosql-databases) more effectively store loosely structured and unstructured data. NoSQL databases solutions pair well with [relational databases](https://www.g2.com/categories/relational-databases) if a company deals with diverse data that can be collected by both structured and unstructured means. **Relational databases —** [Relational databases](https://www.g2.com/categories/relational-databases) are helpful in creating scalable repositories for business information. They are also quality tools for back-end application support. They may be synced to applications to make data available to end users. **Data quality software —** Relational databases struggle with handling unstructured data, and duplicate or incorrect data may throw off the accuracy of results once data becomes structured. [Data quality software](https://www.g2.com/categories/data-quality) helps clean and structure data, which makes it easier to create a formal relational database for that data.