# Best Time Series Databases

  *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





## Category Overview

**Total Products under this Category:** 52


## Trust & Credibility Stats

**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.


## Best Time Series Databases At A Glance

- **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**

### RaimaDB

RaimaDB: The High-Performance Embedded Database for Edge and IoT Systems RaimaDB is a high-performance, small-footprint database designed for edge computing, IoT, and embedded systems. Built on over three decades of database innovation, RaimaDB provides a powerful, reliable, and resource-efficient solution for developers building applications where data integrity, speed, and local storage are critical. Unlike large-scale enterprise databases, RaimaDB is purpose-built for environments with limited memory and processing power—such as industrial controllers, automotive systems, medical devices, and network appliances. Its lightweight architecture allows for fast transactions, deterministic performance, and minimal overhead, making it ideal for real-time applications. RaimaDB supports both SQL and C/C++ APIs, giving developers flexibility in how they access and manage data. It’s fully ACID-compliant, ensuring data reliability even in harsh or disconnected environments. With advanced features like in-memory performance, high availability, and flexible replication, RaimaDB enables secure local data processing while still integrating seamlessly with cloud or enterprise systems when connectivity is restored. The database can be deployed on a wide range of operating systems, including Linux, embedded Linux, Windows, QNX, and VxWorks, and can run on both ARM and x86 architectures. Its modular design allows for efficient scaling—from compact single-board computers to complex distributed networks. Trusted by global leaders in industries such as automotive, aerospace, energy, and telecommunications, RaimaDB powers mission-critical systems that demand reliability and speed. Developers choose RaimaDB for its ease of integration, low maintenance requirements, and proven performance under demanding conditions. Whether you’re building the next generation of connected devices or optimizing data handling at the edge, RaimaDB provides the robust foundation you need. Experience the efficiency of a database built for embedded and real-time systems—fast, reliable, and ready for the future of intelligent data management.



[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=6173&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&secure%5Btoken%5D=37fc89739dbe070877b8390f5230193761587b7948824115f00d9044e5ab2a7d&secure%5Burl%5D=https%3A%2F%2Fraima.com%2Fdownload-trial%2F%3Futm_content%3Dproducts-raimadb-reviews%26utm_medium%3Dreferral%26utm_source%3Dg2&secure%5Burl_type%5D=free_trial)

---

## Top-Rated Products (Ranked by G2 Score)
### 1. [CrateDB](https://www.g2.com/products/cratedb/reviews)
  The real-time database for analytics, search, and AI. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source, multi-model, distributed and containerized database that runs queries in milliseconds, regardless of data complexity, volume and velocity.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 82

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.3/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.9/10 (Category avg: 8.3/10)
- **Quality of Support:** 9.1/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [CrateDB](https://www.g2.com/sellers/cratedb)
- **Company Website:** https://cratedb.com/
- **Year Founded:** 2013
- **HQ Location:** Redwood City, CA
- **Twitter:** @cratedb (4,179 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/crateio/ (44 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 54% Small-Business, 31% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (12 reviews)
- SQL Usage (11 reviews)
- Easy Integrations (10 reviews)
- Flexibility (10 reviews)
- Features (9 reviews)

**Cons:**

- Lack of Features (5 reviews)
- Software Limitations (4 reviews)
- Limited Features (3 reviews)
- Poor Documentation (3 reviews)
- Complex Configuration (2 reviews)

### 2. [TDengine](https://www.g2.com/products/tdengine/reviews)
  TDengine is a time-series database designed to help traditional industries overcome the challenges of Industry 4.0 and Industrial IoT. It enables real-time ingestion, storage, analysis, and distribution of petabytes of data per day, generated by billions of sensors and data collectors. By making big data accessible and affordable, TDengine helps everyone — from independent developers and startups to industry stalwarts and multinationals — unlock the true value of their data. TDengine differentiates itself from typical time-series databases with the following four core competencies: - High Performance at Any Scale: With its distributed scalable architecture that grows together with your business, TDengine can store and process massive datasets up to 10.6x faster than other TSDBs — all while providing the split-second latency that your real-time visualization and reporting apps demand. - Efficient Data Storage: With its unique design and data model, TDengine provides the most cost-effective solution for storing your operations data, including tiered storage, S3, and 10:1 data compression, ensuring that you can get valuable business insights from your data without breaking the bank. - Data Consolidation Across Sites: With built-in connectors for a wide variety of industrial sources — MQTT, Kafka, OPC, PI System, and more — TDengine delivers zero-code data ingestion and extract, transform, and load (ETL) in a centralized platform that acts as a single source of truth for your business. - Comprehensive Solution for Industrial Data: With out-of-the-box data subscription, caching, and stream processing, TDengine is more than just a time-series database — it includes all key components needed for industrial data storage and processing built into a single product and accessible through familiar SQL statements.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 14

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 9.0/10)
- **Ease of Admin:** 10.0/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.6/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [TDengine](https://www.g2.com/sellers/tdengine)
- **Year Founded:** 2017
- **HQ Location:** Los Gatos, California
- **Twitter:** @TDengineDB (3,860 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/tdengine/ (132 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 93% Small-Business, 7% Mid-Market


### 3. [KX](https://www.g2.com/products/kx-kx/reviews)
  We power the time-aware data-driven decisions that enable fast-moving organizations to realize the full potential of their AI investments and outpace competitors. Our technology delivers transformational value by addressing data challenges around completeness, timeliness, and efficiency. We enable organizations to understand change over time and generate faster, more accurate insights — at any scale, and with cost efficiency. Our technology is essential to the operations of the world's top investment banks, aerospace and defense, high-tech manufacturing, healthcare and life sciences, automotive, and fleet telematics organizations. The primary audience for KX encompasses line-of-business leaders, developers, data scientists, and data engineers who require sophisticated analytics capabilities to create high-performance, data-driven applications. With its unmatched speed and scalability, KX allows users to efficiently process large volumes of data, whether in cloud environments, on-premises, or at the edge. This flexibility ensures that organizations can integrate KX technology into their existing workflows seamlessly, enhancing their analytical capabilities without causing disruptions to ongoing operations. KX distinguishes itself in the analytics landscape through its independently benchmarked performance, recognized as the fastest available on the market. This speed is vital for businesses that depend on real-time data insights to inform their decision-making processes. By enabling users to uncover richer, actionable insights quickly, KX facilitates faster and more informed choices, driving competitive advantage and transformative growth. Its ability to manage complex data sets and deliver insights promptly is particularly advantageous for industries that operate in fast-paced environments, where timely information is critical. Key features of KX include advanced time series and vector data analytics capabilities, which enable efficient management and analysis of extensive data volumes. Furthermore, KX integrates seamlessly with popular analytics tools, enhancing their performance and allowing users to maximize their existing investments. The platform's architecture is designed for high performance, ensuring that organizations can scale their analytics operations as needed without sacrificing speed or efficiency. With a global presence across North America, Europe, and Asia Pacific, KX is trusted by leading organizations to spearhead their data and AI initiatives. By providing a powerful analytics solution, KX not only enhances operational efficiency but also fosters a culture of innovation, empowering businesses to remain competitive in an increasingly data-driven world.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 50

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.9/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [KX](https://www.g2.com/sellers/kx-a145756d-91d3-463e-a51d-9e13b1ac577c)
- **Year Founded:** 1996
- **HQ Location:** NY, USA
- **Twitter:** @kxsystems (4,168 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/kx-systems (527 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Banking
  - **Company Size:** 57% Enterprise, 25% Small-Business


#### Pros & Cons

**Pros:**

- Speed (11 reviews)
- Performance (9 reviews)
- Tool Power (7 reviews)
- Efficiency (6 reviews)
- Fast Processing (6 reviews)

**Cons:**

- Learning Curve (12 reviews)
- Difficult Learning (7 reviews)
- Steep Learning Curve (7 reviews)
- Complexity (2 reviews)
- Expensive (2 reviews)

### 4. [Tiger Data](https://www.g2.com/products/tiger-data/reviews)
  Tiger Data, from the creators of TimescaleDB, is the #1 Postgres time-series database for developers, devices and agents. Keep sensor, on-chain, and customer data fresh while retaining years of history, all queryable in standard SQL. For IoT, Web3, and AI. Why teams choose Tiger Data: - Trusted by thousands of developers. 3M+ active databases, 2k+ customers - Up to 95% Compression. Keep years of history online at a fraction of the cost. - Production-ready without the operational pain. Multi-AZ HA, PITR, cross-region backups, SOC 2/HIPAA/GDPR, deep observability. - Scale effortlessly. Disaggregated compute & storage. Never pay for idle capacity. - Unified data architecture. Connect any data source and automatically sync it between your operational database, and data lake. - Hyperscaler procurement. Available on AWS Marketplace and Azure Marketplace. Key capabilities: - Automatic partitioning Ingest millions of data points per second without manual table management or sharding. - Incremental materialized views Precompute and cache rollups for instant dashboards and APIs. - Hybrid row/column storage Fast writes, compressed reads, optimized for real-time and historical queries. - Compression (up to 95%) Columnar encodings apply filters & aggregates directly on compressed data for faster queries and big savings. - Tiered Storage Automatically move older or less-frequently accessed data to low-cost object storage while keeping it fully queryable through the same SQL interface. - Fully managed Postgres Cloud Scale compute and storage independently, tier S3 storage to manage costs, deploy globally, and skip database ops. Industry verticals: Developers and platform teams in Industrial IoT, manufacturing, Crypto, SaaS/ML and DevOps tooling rely on Tiger to combine operational and historical data for real-time dashboards and mission-critical insights, queryable in standard-SQL. How to get started: Try Tiger Cloud for 1-month free with no credit card needed, or use us indefinitely as part of our free plan. Get started now - https://console.cloud.timescale.com/signup?utm\_source=g2&utm\_medium=referral&utm\_campaign=free-trial-g2


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 33

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.3/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.3/10)
- **Quality of Support:** 9.5/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Tiger Data (creators of TimescaleDB)](https://www.g2.com/sellers/tiger-data-creators-of-timescaledb)
- **Company Website:** https://www.tigerdata.com/
- **Year Founded:** 2015
- **HQ Location:** New York, New York
- **Twitter:** @TigerDatabase (1,311 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/timescaledb/ (43 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Financial Services
  - **Company Size:** 79% Small-Business, 18% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (8 reviews)
- Easy Setup (5 reviews)
- Setup Ease (5 reviews)
- Analytics (4 reviews)
- Performance (4 reviews)

**Cons:**

- Expensive (4 reviews)
- Expensive Licensing (3 reviews)
- Missing Features (3 reviews)
- Poor UI (3 reviews)
- Slow Performance (3 reviews)

### 5. [QuestDB](https://www.g2.com/products/questdb/reviews)
  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).


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 33

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 9.0/10)
- **Ease of Admin:** 9.0/10 (Category avg: 8.3/10)
- **Quality of Support:** 9.8/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [QuestDB](https://www.g2.com/sellers/questdb)
- **Year Founded:** 2019
- **HQ Location:** New York, US
- **Twitter:** @QuestDb (2,293 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/questdb/ (30 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Financial Services, Computer Software
  - **Company Size:** 69% Small-Business, 17% Mid-Market


### 6. [Amazon Timestream](https://www.g2.com/products/amazon-timestream/reviews)
  Amazon Timestream is a fast, scalable, fully managed time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day at 1/10th the cost of relational databases. Driven by the rise of IoT devices, IT systems, and smart industrial machines, time-series data, data that measures how things change over time, is one of the fastest growing data types.


  **Average Rating:** 3.7/5.0
  **Total Reviews:** 20

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 6.3/10 (Category avg: 9.0/10)
- **Ease of Admin:** 7.1/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.1/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,225,864 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 45% Mid-Market, 32% Small-Business


#### Pros & Cons

**Pros:**

- Reliability (1 reviews)


### 7. [InfluxDB](https://www.g2.com/products/influxdata-influxdb/reviews)
  InfluxDB is a purpose-built data platform designed to handle all time series data, from users, sensors, applications and infrastructure — seamlessly collecting, storing, visualizing, and turning insight into action. With a library of more than 300 open source Telegraf plugins, importing and monitoring data from any system is easy. InfluxDB empowers developers to build transformative IoT, monitoring and analytics services and applications. InfluxDB’s flexible architecture fits any implementation — whether in the cloud, at the edge or on premises — and its versatility, accessibility and supporting tools (client libraries, APIs, etc.) make it easy for developers at any level to quickly build applications and services with time series data. Optimized for developer efficiency and productivity, the InfluxDB platform gives builders time to focus on the features and functionalities that give their internal projects value and their applications a competitive edge. To get started, InfluxData offers free training through InfluxDB University at https://university.influxdata.com


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 97

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.6/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [InfluxData](https://www.g2.com/sellers/influxdata-c4358581-7be9-4eec-a0bc-bd083f9c5468)
- **Year Founded:** 2012
- **HQ Location:** San Francisco, California
- **Twitter:** @InfluxData (22 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/5159145/ (179 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 59% Small-Business, 23% Mid-Market


### 8. [dataPARC](https://www.g2.com/products/dataparc/reviews)
  dataPARC is a self-service industrial data visualization & analytics toolkit designed for process manufacturers seeking to improve quality, increase yield, & optimize their operations. Collect, connect, & analyze IoT data from across the plant with dataPARC’s process data analytics & visualization platform. Solve challenging process & product quality issues with simple, yet powerful trending & diagnostic analytics tools. Build sophisticated dashboards and displays to monitor processes & share production KPIs across your enterprise. Leverage artificial intelligence (AI) and machine learning to drive continuous improvement & increase margins via predictive modelling.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 39

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.7/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.8/10 (Category avg: 8.3/10)
- **Quality of Support:** 9.6/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [dataPARC](https://www.g2.com/sellers/dataparc)
- **Year Founded:** 1997
- **HQ Location:** Washougal, US
- **Twitter:** @dataPARCsolutio (26 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/dataparc-solutions/ (115 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Paper & Forest Products
  - **Company Size:** 62% Mid-Market, 31% Enterprise


#### Pros & Cons

**Pros:**

- Customizability (2 reviews)
- Data Analysis (2 reviews)
- Data Visualization (2 reviews)
- Ease of Use (2 reviews)
- Features (2 reviews)

**Cons:**

- Complex Usability (1 reviews)
- Data Management Issues (1 reviews)
- Difficult Learning (1 reviews)
- Learning Curve (1 reviews)
- Required Expertise (1 reviews)

### 9. [Prometheus](https://www.g2.com/products/prometheus/reviews)
  Prometheus is an open-source systems monitoring and alerting toolkit designed for reliability and scalability. It collects and stores metrics as time series data, enabling real-time monitoring of applications, systems, and services. With its powerful query language, PromQL, users can analyze and visualize data effectively. Prometheus operates independently, requiring no external dependencies, and integrates seamlessly with various service discovery mechanisms, making it ideal for dynamic environments. Key Features and Functionality: - Dimensional Data Model: Prometheus organizes time series data using a flexible dimensional model, identifying each series by a metric name and a set of key-value pairs. - Powerful Query Language (PromQL): PromQL allows users to query, correlate, and transform time series data for visualizations, alerts, and more. - Precise Alerting: Alerting rules based on PromQL leverage the dimensional data model, with a separate Alertmanager component handling notifications and silencing. - Simple Operation: Prometheus servers function independently, relying solely on local storage. Developed in Go, the statically linked binaries are easy to deploy across various environments. - Instrumentation Libraries: A wide range of official and community-contributed libraries are available for instrumenting applications in most major programming languages. - Ubiquitous Integrations: Prometheus offers numerous integrations, facilitating easy extraction of metrics from existing systems. Primary Value and Problem Solved: Prometheus addresses the need for a robust, scalable, and flexible monitoring solution in modern, dynamic environments. Its ability to collect, store, and query time series data empowers organizations to gain real-time insights into their systems' performance and health. By providing precise alerting and seamless integration with various service discovery mechanisms, Prometheus ensures that issues are detected and addressed promptly, enhancing system reliability and operational efficiency.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 61

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.1/10 (Category avg: 8.3/10)
- **Quality of Support:** 7.9/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Prometheus Authors](https://www.g2.com/sellers/prometheus-authors)
- **Year Founded:** 1998
- **HQ Location:** Raleigh, North Carolina, United States
- **Twitter:** @PrometheusIO (51,902 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/prometheusgroup/ (1,180 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, DevOps Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 40% Mid-Market, 32% Small-Business


#### Pros & Cons

**Pros:**

- Easy Integrations (7 reviews)
- Integrations (6 reviews)
- Alerting System (5 reviews)
- Monitoring (4 reviews)
- Real-time Monitoring (4 reviews)

**Cons:**

- Learning Curve (4 reviews)
- Difficult Learning (2 reviews)
- Difficult Installation (1 reviews)
- Graph Visualization (1 reviews)
- Ineffective Alerts (1 reviews)

### 10. [Epsilon3](https://www.g2.com/products/epsilon3/reviews)
  Epsilon3 is the first AI-powered procedure and resource management tool designed for teams that engineer, build, test, and operate advanced products and systems. ✔ Standardize & Optimize Processes Our interoperable procedure execution system replaces inefficient checklists managed with paper, spreadsheets, docs, and outdated planning tools. Automatically track every step to ensure quality, consistency, and traceability. ✔ Fuel Rapid Iteration & Innovation Built-in version control, conditional workflows, and real-time data synchronization keep stakeholders on the same page. Enable continuous improvement and quick, data-driven decisions to stay far ahead of the competition. ✔ Streamline & Scale Operations Securely integrate siloed systems and automate repetitive, error-prone tasks to boost productivity and prevent delays. Simplify training, reduce costs, and maintain efficiency as your operations expand to meet demand. Epsilon3 is trusted by industry leaders like NASA, Blue Origin, Firefly Aerospace, Sierra Space, Redwire, Shift4, AeroVironment, Commonwealth Fusion Systems, and other commercial and government organizations. The company and platform were built by engineering leaders from SpaceX, NASA, and Google. Learn how: https://www.epsilon3.io/


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 259

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.9/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Epsilon3](https://www.g2.com/sellers/epsilon3)
- **Company Website:** https://epsilon3.io
- **Year Founded:** 2021
- **HQ Location:** Los Angeles, California
- **Twitter:** @Epsilon3Inc (1,056 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/epsilon3inc (32 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Customer Service Representative
  - **Top Industries:** Aviation & Aerospace, Financial Services
  - **Company Size:** 43% Mid-Market, 40% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (80 reviews)
- Procedure Management (42 reviews)
- Features (30 reviews)
- Efficiency (27 reviews)
- Efficiency Improvement (24 reviews)

**Cons:**

- Learning Curve (35 reviews)
- Complexity (33 reviews)
- Confusing Procedures (29 reviews)
- Complex Procedures (27 reviews)
- Confusion (24 reviews)

### 11. [Aerospike](https://www.g2.com/products/aerospike/reviews)
  The Aerospike Real-time Data Platform enables organizations to act instantly across billions of transactions while reducing server footprint by up to 80 percent. The Aerospike multi-cloud platform powers real-time applications with predictable sub-millisecond performance up to petabyte-scale with five-nines uptime with globally distributed, strongly consistent data. Applications built on the Aerospike Real-time Data Platform fight fraud, provide recommendations that dramatically increase shopping cart size, enable global digital payments, and deliver hyper-personalized user experiences to tens of millions of customers. Customers such as Airtel, Experian, Nielsen, PayPal, Snap, Wayfair, and Yahoo rely on Aerospike as their data foundation for the future.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 80

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.9/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Aerospike](https://www.g2.com/sellers/aerospike)
- **Year Founded:** 2009
- **HQ Location:** Mountain View, CA
- **Twitter:** @aerospikedb (7,836 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2696852/ (306 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer
  - **Top Industries:** Marketing and Advertising, Information Technology and Services
  - **Company Size:** 45% Mid-Market, 34% Enterprise


### 12. [Warp 10](https://www.g2.com/products/warp-10/reviews)
  Warp 10 is a modular open source platform that collects, stores, and analyzes data from sensors. Shaped for the IoT with a flexible data model, Warp 10 provides a unique and powerful framework to simplify your processes from data collection to analysis and visualization, with the support of geolocated data in its core model (called Geo Time Series). Warp 10 is both a time series database, and a powerful analytics environment, allowing you to make: statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts. The Platform is GDPR compliant and secure by design using cryptographic tokens to manage authentication and authorization. The analysis environment can be implemented within a large ecosystem of software components such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. It can also access data stored in many existing solutions, relational or NoSQL databases, search engines and S3 type object storage system. Warp 10 fits your needs at any scale, from small devices to distributed clusters, and can be used in many verticals: industry, transportation, health, monitoring, finance, energy, etc. A collection of tools completes the Platform and ease your work on time series data: - WarpStudio, a web editor, to edit and execute your WarpScript code. - WarpFleet, the artifact repository, to share your plugins, extensions, and macros. - Sandbox, a hosted environment for test-driving Warp 10 without deploying it. - Discovery, a dynamic dashboarding solution with a unique dashboard as code approach. - HFiles, a high-density storage solution providing infinite storage scalability while retaining all analytics capabilities.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 35

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.1/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [SenX](https://www.g2.com/sellers/senx)
- **HQ Location:** Guipavas, Brittany
- **Twitter:** @SenXHQ (234 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/12632019 (13 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 50% Small-Business, 31% Mid-Market


### 13. [Druid](https://www.g2.com/products/druid/reviews)
  Apache Druid is an open source real-time analytics database. Druid combines ideas from OLAP/analytic databases, timeseries databases, and search systems to create a complete real-time analytics solution for real-time data. It includes stream and batch ingestion, column-oriented storage, time-optimized partitioning, native OLAP and search indexing, SQL and REST support, flexible schemas; all with true horizontal scalability on a shared nothing, cloud native architecture that makes it easy to deploy, monitor and manage at scale. It is downloadable for free for unlimited use from druid.apache.org and also hosted in the cloud by Imply Data.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 28

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 7.7/10 (Category avg: 9.0/10)
- **Ease of Admin:** 6.4/10 (Category avg: 8.3/10)
- **Quality of Support:** 7.9/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Druid](https://www.g2.com/sellers/druid)
- **Year Founded:** 1998
- **HQ Location:** Rio de Janeiro, Rio de Janeiro
- **Twitter:** @druid (4 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/druid_2/ (77 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 52% Enterprise, 29% Mid-Market


### 14. [Trendalyze](https://www.g2.com/products/trendalyze/reviews)
  Trendalyze is a platform for discovering, predicting, and monitoring patterns in granular time series data collected by sensors, IoT devices, machines, transactional and event log systems. Our self-service platform empowers all information workers to discover and monitor meaningful patterns as easily as it is to search and monitor for web content on Google. It also empower business users and analysts to discover, analyze and predict patterns in IoT and other transactional data as easily as it is for them to analyze business data in Excel. Trendalyze has pioneered patent-pending Logical Neural Networks that learn from small data sets, are 100% explainable, and can be configured by business professionals.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.1/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Trendalyze](https://www.g2.com/sellers/trendalyze)
- **Year Founded:** 2016
- **HQ Location:** USA, Newark
- **Twitter:** @trendalyze (115 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/9487986/ (6 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 37% Enterprise, 37% Small-Business


### 15. [GridDB](https://www.g2.com/products/griddb/reviews)
  GridDB is a database that offers both speed and scaling for mission critical big-data applications.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 27

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 9.0/10)
- **Ease of Admin:** 10.0/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Toshiba](https://www.g2.com/sellers/toshiba)
- **Year Founded:** 2016
- **HQ Location:** N/A
- **Twitter:** @griddb (9 Twitter followers)
- **LinkedIn® Page:** http://www.linkedin.com/company/griddb (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 74% Mid-Market, 15% Enterprise


### 16. [Bangdb](https://www.g2.com/products/bangdb/reviews)
  BangDB is a multiflavored, multimodel, embedded, distributed, high performance, analytical, timeseries NoSql database written in C/C++ and design from scratch for solving contemporary and future problems in simple and easy manner which otherwise requires huge amount of time and resources.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 12

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 9.0/10)
- **Ease of Admin:** 6.7/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [BangDB](https://www.g2.com/sellers/bangdb)
- **Year Founded:** 2015
- **HQ Location:** Bangalore, Karnataka
- **Twitter:** @IQLECT (453 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/bangdb/ (6 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Small-Business, 33% Enterprise


### 17. [Redis Cloud](https://www.g2.com/products/redis-cloud/reviews)
  Redis Cloud is our fully-managed Redis Enterprise service, delivering unmatched speed, simplicity, and scalability. It's perfect for cloud-native applications requiring real-time data processing, without the hassle of managing infrastructure. Redis Cloud surpasses Redis-compatible cloud services built on open source such as Amazon ElastiCache and Google Cloud Memorystore by offering enterprise-grade features like active-active geo-distribution, advanced query and search capabilities, seamless data synchronization, and multi-cloud support.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 42

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.8/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Redis](https://www.g2.com/sellers/redis)
- **Year Founded:** 2011
- **HQ Location:** San Francisco, CA
- **Twitter:** @Redisinc (43,961 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2014725/ (1,510 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 50% Small-Business, 41% Mid-Market


### 18. [Google Cloud BigTable](https://www.g2.com/products/google-cloud-bigtable/reviews)
  Cloud Bigtable is Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail. Bigtable is designed to handle massive workloads at consistent low latency and high throughput, so it's a great choice for both operational and analytical applications, including IoT, user analytics, and financial data analysis.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 37

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.9/10 (Category avg: 9.0/10)
- **Ease of Admin:** 6.7/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.2/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,910,461 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Company Size:** 41% Mid-Market, 32% Enterprise


#### Pros & Cons

**Pros:**

- Cloud Storage (8 reviews)
- Ease of Use (4 reviews)
- Integrations (4 reviews)
- Application Development (3 reviews)
- Data Analytics (3 reviews)

**Cons:**

- Cost Issues (5 reviews)
- Expensive (4 reviews)
- Billing Issues (2 reviews)
- Complexity (2 reviews)
- Learning Difficulty (2 reviews)

### 19. [Tinybird](https://www.g2.com/products/tinybird/reviews)
  Tinybird is a fully managed ClickHouse® service designed for software developers and AI-native product teams by enabling them to create large-scale real-time analytics projects with minimal effort. Tinybird makes integrating the open source ClickHouse database into applications simpler, faster, and more reliable, allowing engineers to focus on feature development rather than infrastructure management. Tinybird eliminates the complexities associated with traditional database management, making it an ideal choice for teams looking to leverage the power of ClickHouse without the overhead of server maintenance and scaling concerns. The target audience for Tinybird includes software developers, data engineers, technical founders, and AI-native product teams building real-time analytics capabilities in their applications. With the increasing demand for real-time data processing, Tinybird caters to teams that need to deliver insights quickly and efficiently. Use cases for Tinybird span various industries, including SaaS, e-commerce, finance, crypto, AI, and IoT, where real-time data analysis is crucial for decision-making and operational efficiency. By providing a managed service, Tinybird allows software engineers to deploy analytics features in days rather than months, significantly accelerating project timelines. Key features of Tinybird include a hosted ClickHouse database plus managed data ingestion and API layers, which simplify the process of integrating analytics into applications. The built-in authentication tools enhance security and data privacy, with support for row-level access policies using JWTs. Free observability logs storage and querying allow users to keep tabs on usage and performance. AI-native features, including Tinybird Code - a CLI agent with deep ClickHouse expertise - plus the Tinybird MCP Server, make integrating analytics features into LLM apps simpler and more robust. Additionally, Tinybird's architecture is designed to handle scaling automatically, allowing teams to focus on their core development tasks without worrying about understanding a new database or worrying about infrastructure details. For those who desire infrastructure control, Tinybird offers self-managed deployment, for free. This unique combination of features enables users to ship data-driven features rapidly while maintaining high performance and reliability. Tinybird stands out in the real-time analytics database landscape by providing the performance of one of the world's fastest OLAP databases without the associated complexity. By abstracting the technical challenges of managing clusters and provisioning resources, Tinybird empowers teams to innovate and iterate on their products more quickly. The service's emphasis on ease of use and rapid deployment makes it an attractive option for organizations looking to harness the power of real-time analytics without the burden of extensive operational overhead. With Tinybird, users can unlock the potential of their data and drive impactful insights, all while enjoying a seamless and efficient development experience.


  **Average Rating:** 4.1/5.0
  **Total Reviews:** 14

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 9.0/10)
- **Ease of Admin:** 10.0/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.6/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Tinybird](https://www.g2.com/sellers/tinybird)
- **Company Website:** https://tinybird.co
- **Year Founded:** 2019
- **HQ Location:** New York, US
- **LinkedIn® Page:** https://www.linkedin.com/company/35704741 (52 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 50% Mid-Market, 36% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (6 reviews)
- Analytics (4 reviews)
- Easy Integrations (4 reviews)
- Features (4 reviews)
- Integrations (4 reviews)

**Cons:**

- Poor Customer Support (3 reviews)
- Lack of Features (2 reviews)
- Learning Curve (2 reviews)
- Learning Difficulty (2 reviews)
- Limited Customization (2 reviews)

### 20. [The PI System](https://www.g2.com/products/the-pi-system/reviews)
  The PI System is an enterprise infrastructure for management of real-time data and events with tools and features to help you manage your data and more.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 19

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 9.0/10)
- **Ease of Admin:** 9.7/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.2/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [AVEVA](https://www.g2.com/sellers/aveva)
- **Year Founded:** 1967
- **HQ Location:** Cambridge, GB
- **Twitter:** @AVEVAGroup (15,400 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/14547/ (7,622 employees on LinkedIn®)
- **Ownership:** LSE:AVV

**Reviewer Demographics:**
  - **Company Size:** 43% Mid-Market, 38% Enterprise


#### Pros & Cons


**Cons:**

- Complex Usability (1 reviews)
- UX Design (1 reviews)
- UX Improvement (1 reviews)

### 21. [CortexDB](https://www.g2.com/products/weaveworks-cortexdb/reviews)
  Cortex offers the same powerful query language, data model and configurable alerts as Prometheus, but we added horizontal scalability and cloud-native storage for virtually infinite data retention.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 6

**User Satisfaction Scores:**

- **Quality of Support:** 10.0/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Weaveworks](https://www.g2.com/sellers/weaveworks)
- **Year Founded:** 2014
- **HQ Location:** London , GB
- **Twitter:** @weaveworks (11,221 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/9420084 (12 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 67% Small-Business, 33% Mid-Market


### 22. [Heroic](https://www.g2.com/products/heroic/reviews)
  Heroic is an open-source monitoring system originally built at Spotify to address the problems that were facing with large scale gathering and near real-time analysis of metrics.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 6

**User Satisfaction Scores:**

- **Quality of Support:** 8.9/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Heroic](https://www.g2.com/sellers/heroic)
- **Year Founded:** 2017
- **HQ Location:** N/A
- **Twitter:** @spotify (20,028,593 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 67% Small-Business, 33% Mid-Market


### 23. [Axibase Time Series Database](https://www.g2.com/products/axibase-time-series-database/reviews)
  ATSD is a distributed NoSQL database designed from the ground up to store and analyze time-series data at scale. Unlike most other databases, ATSD comes with a robust set of built-in features including Rule Engine, Visualization, Data Forecasting, Data Mining and more.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 4

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 9.0/10)
- **Ease of Admin:** 10.0/10 (Category avg: 8.3/10)
- **Quality of Support:** 9.6/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Axibase](https://www.g2.com/sellers/axibase)
- **Year Founded:** 2004
- **HQ Location:** Cupertino, US
- **Twitter:** @axibase (55 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/axibase (3 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Mid-Market, 25% Enterprise


### 24. [Aiven for InfluxDB](https://www.g2.com/products/aiven-for-influxdb/reviews)
  Fully managed InfluxDB – the popular, lightweight, high-ingest time series database that you can snap into your workflow in minutes.


  **Average Rating:** 3.8/5.0
  **Total Reviews:** 3

**User Satisfaction Scores:**

- **Quality of Support:** 7.2/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Aiven](https://www.g2.com/sellers/aiven)
- **Year Founded:** 2016
- **HQ Location:** Helsinki, Southern Finland
- **Twitter:** @aiven_io (4,096 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10294984/ (439 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 33% Mid-Market, 33% Small-Business


### 25. [ExtremeDB](https://www.g2.com/products/extremedb/reviews)
  The eXtremeDB database combines exceptional performance, reliability and developer efficiency in a proven real-time embedded database engine.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 10

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 9.0/10)
- **Ease of Admin:** 8.8/10 (Category avg: 8.3/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [McObject](https://www.g2.com/sellers/mcobject)
- **Year Founded:** 2001
- **HQ Location:** Federal Way, WA
- **Twitter:** @LowLatencyDB (4,838 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/mcobject/ (17 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Mid-Market, 30% Enterprise




## Parent Category

[Database Software](https://www.g2.com/categories/database-software)



## Related Categories

- [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)



---

## Buyer Guide

### 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.