Overview of Influx Data and Time Series Data
Influx Data
- Influx Data is the creator of InfluxDB, the most widely used time series database with over 1 million active instances.
- They are also the creators of Telegraf, a widely used tool for ingesting and transmitting data into various databases.
- Influx Data is the category leader in time series databases.
Time Series Data
- Time series data is data with the purpose of knowing what happened or is currently happening at specific points in time.
- It can come in the form of metrics (consistent data points) or events (sudden changes in the data).
- Time series data has many use cases, such as financial analysis, health monitoring, and weather forecasting, as it provides clarity of the past, present, and enables better prediction of the future.
InfluxDB 3.0
- InfluxDB 3.0 is the newest version, featuring a real-time columnar database with sub-100 millisecond response times.
- It offers lower storage costs by leveraging Apache Parquet and object storage, as well as unlimited cardinality to handle large-scale analytical queries.
Real-Time Analytical Processing
- InfluxDB 3.0 enables real-time analytical processing (OLAP) by allowing direct querying of data during the ingestion process, before it's persisted to long-term storage.
- This reduces latency and enables faster decision-making and proactive measures.
Getting Started with InfluxDB
- InfluxDB is open-source and has a vibrant community.
- For smaller workloads, InfluxDB Cloud Serverless provides an easy way to get started with built-in visualizations.
- InfluxDB also integrates with AWS Time Stream, a managed service for InfluxDB on AWS.
- For larger scale workloads, InfluxDB offers Cloud Dedicated and Clustered solutions to handle high-ingestion and high-scale requirements.
Key Use Cases
- DevOps monitoring
- Real-time IoT analytics
- High-cardinality custom analytics applications
- Tracing and observability
Conclusion
Influx Data is the leading provider of time series databases, with InfluxDB 3.0 offering real-time analytical processing capabilities, reduced storage costs, and the ability to handle high-scale, high-cardinality workloads. The company provides a range of deployment options to cater to different use cases and scales, making it easier for users to leverage the power of time series data.
