Recent convocations of the Strata big data conference have seen a move away from sessions focused on data infrastructure and Hadoop and toward analytical applications and data science tools. Where is Strata going? Strata, it seems, cannot contain itself, when it comes to software containers.

News around Kubernetes and containers figured prominently in coverage at the recent Strata Data Conference in San Jose, Calif.

Containerized apps have significant benefits – so big data developers are discovering. Big data applications that sprouted up indiscriminately in organizations are now being folded into central data lakes and the like, and containers are increasingly seen as a flexible way to handle a procession of distinct and ever changing analytics jobs. Putting jobs in self-contained units seems a way to bring order to the workloads.

A key appears to be container orchestration, which, in the form of the Kubernetes open source standard, seems poised to usher in a new way of handling big data work. That is according to TechTarget Senior Executive Editor Craig Stedman, speaking with Senior New Writer Jack Vaughan.

Among vendors that showcased their container-related efforts at the conference are such as Blue Data, data Artisans, Intel, MapR Technologies, Pepperdata and others. They almost universally cite recommendation engines, machine learning, and AI as apt first targets.

Still, a message that arises in this podcast is that it is early. While improved speed of development and deployment seems in the offing, a lot of preliminary work must be done before pushing that magic button of container automation.

Containers may be one of those unique cases where software engineering takes significant inspiration from work in other industries. That is because the container metaphor comes straight from logistics advances that, beginning in World War II, transformed global shipping.

In the world today, 90% of non-bulk cargo is convey by containers – perhaps, someday, something similar will be said about software containers for persistent data. There is much that needs to be developed when it comes to persistent containers for big data, but important efforts are now underway, as discussed in this edition of the Talking Data podcast. Listen, and learn more. – Jack Vaughan

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