InfluxDB and Kafka aren’t competitors – they’re complimentary. Streaming data, and more specifically time series data, travels in high volumes and velocities. Adding InfluxDB to your Kafka cluster provides specialized handling for your time series data. This specialized handling includes real-time queries and analytics, and integration with cutting edge machine learning and artificial intelligence technologies. Companies like as Hulu paired their InfluxDB instances with Kafka.

The challenge for every organization is gathering actionable observability information from all your systems, in a timely manner, without creating a substantial operational burden for the teams managing the collection tooling. While each observability solution has its unique benefits and challenges, the one common burden expressed by teams is the management of the metadata of the metrics, traces, and logs.

Data growth has significantly out-pacing budgets; the products we use, have to do more. This is where optimization comes into play. Generally, optimization is associated with reduction which may be intimidating…what if something important is reduced? How can you identify what should be reduced? Reduction isn’t about removing context, but about removing repetitive data, meaningless fields, or even flattening JSON.

So many businesses today are playing “Hungry, Hungry, (Data) Hippo,” devouring every marble of information they can get their hands on. While it seems like every company has a robust data aggregation system, what most companies don’t have is an efficient way to control what data they store and where that data goes. We all want to make data-driven business decisions, but sorting through tons of data to find useful business insights can be like finding a needle in a whole farm.

Any existing InfluxDB user will notice that InfluxDB underwent a transformation with the release of InfluxDB 3.0. InfluxDB v3 provides 45x better write throughput and has 5-25x faster queries compared to previous versions of InfluxDB (see this post for more performance benchmarks). We also deprioritized several features that existed in 2.x to focus on interoperability with existing tools. One of the deprioritized features that existed in InfluxDB v2 is the task engine.

The commercial version of InfluxDB 3.0 is a distributed, scalable time series database built for real-time analytic workloads. It supports infinite cardinality, SQL and InfluxQL as native query languages, and manages data efficiently in object storage as Apache Parquet files. It delivers significant gains in ingest efficiency, scalability, data compression, storage costs, and query performance on higher cardinality data.