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### Time Series Data Management
Reviewers generally praise InfluxDB's capabilities in handling time series data efficiently and effectively. Users appreciate its high performance in managing large volumes of time series data with ease. The platform's ability to handle high write and query loads, coupled with its real-time analytics features, makes it a popular choice for users dealing with time series data. Additionally, the flexibility and scalability of InfluxDB in storing and querying time series data are highlighted as key strengths by reviewers. Despite some minor concerns about data retention policies and pricing, users find InfluxDB to be a reliable and efficient solution for time series data management.

### Query Optimization
Reviewers generally express mixed opinions about InfluxDB's query language. Some users find the Flux query language challenging to learn, citing a steep learning curve as a drawback. However, others appreciate the powerful capabilities and extensibility it offers for crafting complex queries. While some users highlight the efficiency and speed of query execution, others mention the need for more user-friendly documentation and tutorials to aid in mastering the query language. InfluxDB's query optimization seems to be a point of contention among users, with some finding it a strength for real-time analytics and quick query results, while others struggle with the learning curve and complexity of the Flux language.

### Advanced Analytics and Machine Learning
User sentiment regarding InfluxDB's advanced analytics and machine learning capabilities is mixed. While some users appreciate its ability to support machine learning models and provide valuable historical data for training and prediction purposes, others find the learning curve associated with the custom programming language and query language to be a significant challenge. The consensus seems to be that while InfluxDB offers powerful features for real-time analytics and time-series data storage, the complexity of certain aspects, such as the Flux language, can be a hindrance to some users.

### Data Monitoring and Alerting
Users generally praise InfluxDB's robust data monitoring and alerting capabilities. Reviewers highlight the ease of integrating InfluxDB with other monitoring tools like Prometheus and Grafana, enabling real-time monitoring and alert generation for various events. The ability to handle high volumes of time-series data efficiently and the flexibility to customize alert rules and visualize data in real-time are key strengths of InfluxDB's monitoring and alerting features. Despite some users experiencing latency issues with Grafana integration, the overall consensus leans towards the effectiveness and versatility of InfluxDB in monitoring time-series data and generating alerts based on threshold changes.

### Visualization and Dashboards
Users generally appreciate InfluxDB's strong integration with Grafana for visualizing data and creating dashboards. The ease of connecting data sources and the ability to customize dashboards with pre-built templates are highlighted as key strengths. While some users find the dashboards somewhat restrictive in terms of sharing options, the overall sentiment leans towards the effectiveness and efficiency of visualizing data using InfluxDB in conjunction with Grafana. The platform's ability to handle time series data and provide real-time analytics is a notable advantage, making it a popular choice for various use cases requiring robust visualization capabilities.

### Real-Time Analytics
Users across various industries consistently praise InfluxDB for its real-time analytics capabilities, highlighting its ability to handle high write and query loads efficiently. The software's real-time performance, fast query execution times, and low latency are particularly commended, making it a preferred choice for applications requiring instant data analysis and monitoring. Additionally, users appreciate the scalability, ease of use, and the built-in clustering functionality that ensures high availability even with large datasets. InfluxDB's real-time analytics feature enables businesses to make quick, data-driven decisions based on up-to-date information, enhancing operational efficiency and performance monitoring.

### IoT Data Handling
Users consistently highlight InfluxDB's prowess in efficiently managing and analyzing IoT data from a multitude of sensors and devices. The platform's ability to handle large volumes of real-time data and provide valuable insights for IoT projects is widely appreciated. Despite some past concerns about documentation and community support, users acknowledge the significant impact of InfluxDB's IoT data handling capabilities in enhancing the efficiency and effectiveness of their projects.

### Data Collection and Storage
Users generally praise InfluxDB for its efficient data collection and storage capabilities, particularly for time series data. They appreciate the ease of storing and accessing data from various sources, such as IoT devices, sensors, and network devices. While some users find the platform initially complex to understand, they acknowledge its flexibility in handling large volumes of data and its quick query results. The community support and scalability of InfluxDB are highlighted as key advantages for users dealing with real-time data storage and analysis. Additionally, users value the platform's ability to integrate with other tools and services seamlessly for comprehensive data storage solutions.

### Integration and APIs
Reviewers consistently praise InfluxDB's integration capabilities and APIs for their ease of use and versatility. Users appreciate the simplicity of setting up instances and integrating them with various projects, such as IoT applications and data control services. The ability to handle time series data efficiently and merge multiple data sources for analysis is a standout feature for many users. Despite some minor complexities in integrating libraries during the development phase, the overall consensus leans towards the positive side, highlighting the convenience of the built-in REST API for quick integration with other solutions. Some users have noted minor drawbacks, such as high latency rates when using Grafana for monitoring and the need for better error logs integration. However, the general sentiment remains favorable towards InfluxDB's integration and APIs, especially for time series data processing and real-time analytics.

### Learning and Documentation
User opinions vary regarding InfluxDB's learning curve and documentation quality. Some users find the Flux query language challenging to grasp initially, while others appreciate the structured and detailed documentation provided. There are suggestions for improvement in making the documentation more user-friendly and offering tutorials for different use cases. Despite differing views, the consensus is that InfluxDB's learning and documentation play a significant role in users' experiences with the platform.

### Open Source Contributions
User sentiment regarding InfluxDB's open source contributions capabilities varies widely. Some users appreciate the fact that it is open source, allowing for experimentation and testing of time series projects without cost. However, there are concerns about limitations in the open source version, such as the lack of certain features and scalability options. Additionally, users have highlighted the need for better documentation and more integrations to enhance the performance testing ecosystem. Despite these mixed opinions, the open source nature of InfluxDB remains a notable aspect for many users.

### Application Development
Users generally praise InfluxDB for its robust application development capabilities, citing its ease of installation, configuration, and development as major strengths. The platform's HTTP native API, support for Continuous Queries, and built-in tools for down-sampling and summarizing data are highlighted as valuable features that enhance the development process. Despite some challenges with the complexity of integrating libraries and the learning curve associated with the Flux language, users appreciate the scalability, flexibility, and real-time performance that InfluxDB offers for application development.

### Cloud Solutions and Infrastructure
Users generally appreciate InfluxDB's cloud solutions and infrastructure capabilities, highlighting the ease of setup, intuitive user interface, and the flexibility in storing and querying data. Some users find the pricing model challenging to estimate, while others mention that the cloud hosting costs could be high at scale. Despite some concerns, users find the cloud service to be efficient for managing large datasets and real-time streaming data, especially for applications like IoT sensor data aggregation and electricity time series analysis. The transition to InfluxDB Cloud version 2.0 has been noted as a significant shift, with some users feeling that older applications may become outdated quickly. However, the availability of open-source options for testing time series projects and the smooth integration with cloud services like GCP are seen as valuable features by users.

### Performance and Scalability
Users consistently highlight InfluxDB's performance and scalability as standout features, praising its ability to handle high write and query loads efficiently. The database's real-time performance, fast query execution times, and low latency are particularly commended, making it a preferred choice for applications requiring real-time data analysis. Additionally, users appreciate the database's ease of use, optimized performance, and built-in clustering functionality that ensures high availability even with large datasets. The community's supportiveness and the software's scalability for large-scale applications further contribute to its positive reputation in terms of performance and scalability.

Users have recommended using InfluxDB for a variety of use cases due to its remarkable data storage and querying abilities. It is also suggested for fast and efficient data visualization, especially for enterprise organizations. Additionally, users advise starting with a small on-premise setup when implementing InfluxDB and then deciding between on-premise and cloud-based solutions as usage grows to achieve the lowest cost implementation. These recommendations highlight the versatility, performance, and cost-effectiveness of InfluxDB as a time series database solution.

High Availability and Scalability: Many users appreciate the high availability and scalability of InfluxDB, making it suitable for handling large amounts of data. Several reviewers have mentioned that they can rely on InfluxDB to efficiently handle their data needs, allowing them to scale their applications without worrying about performance issues.

Real-time Performance: The real-time performance of InfluxDB is impressive, with fast query execution times and low latency. Numerous users have praised its ability to provide quick insights and analysis on real-time data. This makes it an ideal choice for tasks such as real-time monitoring and data analysis.

Ease of Use in Querying and Processing Data: Users find one of the key strengths of InfluxDB lies in its ease of use when querying, filtering, and processing data. They appreciate the built-in data explorer, dashboard building capabilities, and alert mechanisms that simplify these tasks. Several reviewers have expressed their satisfaction with how intuitive it is to work with InfluxDB's interface for data manipulation.

Difficult Learning Curve: Some users have mentioned a slight learning curve when using the TICK stack components, particularly with understanding the query language used in InfluxDB. They acknowledged that it could be their own problem but still found it challenging to grasp the concepts and syntax of the language.

Lack of Direct Plugins/Integrations: Several users have expressed dissatisfaction with the lack of direct plugins or integrations available for InfluxDB. They stated that they had to develop most of the out-of-box solutions themselves, which added extra effort and time to their implementation process.

Documentation and Tutorial Difficulties: Users have found the documentation of the database difficult to understand and suggested the need for better tutorials. They also mentioned that InfluxDB's documentation could be improved overall, as it was not always clear and comprehensive enough to provide effective guidance for beginners.

Users have found the integrated Dashboards in Influx highly beneficial for monitoring and visualizing critical data. They appreciate the software's effectiveness in time series data analysis and alerting on threshold changes. Influx has successfully been utilized by users to collect and visualize real-time data series from sensors and applications. The holistic view provided by the Influx user interface allows users to effectively monitor the performance of their applications during testing. Leveraging the Infux DB dashboard, users can easily monitor various utilization metrics across their instances, such as response time, throughput, CPU, memory, disk, and network. Additionally, the software provides convenient alerting capabilities to notify relevant parties for necessary actions.

Users have seamlessly integrated Influx with their applications and are impressed with the one-stop web page for monitoring upgrades. The software proves to be effective for collecting and monitoring data from batches, enabling users to easily track running time and compare results. Furthermore, Influx has successfully addressed challenges related to data availability and robust data transfers with minimal lag. Users have leveraged the software for bandwidth consumption optimization, data modeling using time series forecasting, and real-time data processing or monitoring. In terms of query performance, Influx excels particularly when time is the main parameter. Users have harnessed the power of InfluxDB to store telemetry information from thousands of machines and utilize this valuable information for monitoring and reporting purposes.

The InfluxDB is a time series database from InfluxData headquartered in San Francisco. As an observability solution, it is designed to provide real-time visibility into stacks, sensors and systems. It is available open source, via the Cloud as a DBaaS option, or through an Enterprise subscription.

InfluxDB

Time Series Databases (TSDB) are designed to store and analyze event data, time series, or time-stamped data, often streamed from IoT devices, and enables graphing, monitoring and analyzing changes over time.

Time Series Databases

InfuxDB: Overview, Key Concepts and Demo | Getting Started

Intro to TICK Stack Components and InfluxDB Enterprise | Getting Started [2 of 7]

Introducing InfluxDB 2.0

Quick Demo of Telegraf 1.13.0 Receiving SNMP Traps

Grafana Template and Repeat Demo (with InfluxDB)

[Training] How to Create User-Defined Functions in Kapacitor for InfluxDB

InfluxDB CLI Demo

InfluxDB Clustering, Monitoring and Data Exploration Demo

InfluxDB Clustering, Monitoring and Data Exploration Demo - June 2016

Performance Testing Solution K6 & Integration with InfluxDB, Grafana and Jenkins.

Demo Ansible Playbook | InfluxDB + Telegraf + Grafana

Node.js metrics demo in Play with Docker (using InfluxDB & Grafana)

Likelihood to Recommend

Likelihood to Renew

Support Rating

InfluxData

Monitor Your System Metrics With InfluxDB Cloud in Under One Minute

Learn how to monitor your system metrics by automatically configuring Telegraf in InfluxDB Cloud. Be sure to sign up from InfluxDB Cloud and download Telegraf prior to following this demonstration. 

Speaker: Samantha Wang, Product Manager

Sign up for InfluxDB Cloud: https://cloud2.influxdata.com/signup
Download Telegraf: https://portal.influxdata.com/downloads/
More documentation on how to automatically configure Telegraf: https://docs.influxdata.com/influxdb/v2.0/write-data/no-code/use-telegraf/auto-config/

Tivoli

IBM Netcool Network Management

IBM Tivoli NetCool/OMNIbus, part of Netcool Network Mgmt

Dynatrace

Enterprise Systems Manager Specialist

InfluxDB 2020-04-21 10:29:16

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New Relic

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Gusto

Intercom

Mirantis Kubernetes Engine

DigitalOcean

Slack

Cloudflare

Retrace

Stripe Payments

Sentry

CEO & Co-Founder

InfluxDB 2018-10-03 09:57:05

HashiCorp Consul

Apache Kafka

Senior Consultant IT Infrastructure

InfluxDB 2018-01-21 16:38:23

Google Cloud BigTable

Google Cloud SQL

Google Cloud Spanner

Google Cloud Pub/Sub

Google Cloud DNS

InfluxDB 2021-01-28 17:17:38

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