Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
InfluxData has made good on a promise to deliver a next-generation time-series database that enables observability and monitoring tools used by DevOps teams to ingest high volumes of data more cost-efficiently.
In addition, for the first time, the latest iteration of the InfluxDB database supports the de facto standard SQL query language. Developers can run unlimited time-series workloads and contextualize data by any dimension without restrictions using SQL or existing tools such as the InfluxDB application programming interface (API), Flux scripting language or the InfluxQL query language
Brian Gilmore, director of internet-of-things (IoT) and emerging technologies for InfluxData, said InfluxDB is now based on a columnar engine, dubbed IOx, that leverages the open source Apache Arrow memory format and the Rust programming language accessed via a multi-tenant InfluxDB Cloud service that company manages on behalf of application developers. Use cases that require processing large volumes of data such as metrics, events, traces and other high cardinality data can now be queried in milliseconds, he noted.
The goal is to make this latest incarnation of InfluxDB available via a managed cloud service before providing IT teams with a version they can install themselves.
The arrival of a revamped InfluxDB architecture comes as the amount of data being generated across application environments continues to rapidly expand. Many DevOps teams are especially struggling with ways to process and store all the data being generated by cloud-native applications made up of microservices generating a steady stream of logs, metrics and traces.
InfluxData, of course, has ambitions for its database that go beyond application development and deployment environments. Edge computing platforms, for example, will increasingly process and analyze data in real-time at the point where it is generated and consumed. Similarly, applications infused with machine and deep learning algorithms will require databases that can process data at scale. Regardless of the use case, however, it’s apparent more IT teams will be embracing DataOps best practices alongside DevOps to manage data as applications are deployed and continuously updated.
It remains to be seen how many observability and monitoring tools might embrace this latest iteration of the InfluxDB database, but it’s clear DevOps teams should expect to see major advances as the underlying databases upon which these platforms depend continue to evolve. The goal is to ultimately eliminate the tradeoff that now often occurs between how much data is collected and analyzed versus the cost of processing and storing it.
In the meantime, DevOps teams will need to decide whether they want to manage databases themselves as more managed services become easier to invoke in the age of the cloud. Most database selections these days are made by development teams that aren’t especially eager to take on the tasks normally performed by a database administrator. A managed service essentially outsources responsibility for managing a database to a services provider instead of requiring IT teams to hire a DBA themselves.
One way or another, however, the speed at which data can now be processed in real-time should sharply reduce the need to rely on existing and cumbersome batch-oriented approaches to processing data.
