A mysterious Israeli-based Big Data startup has just announced $15 million in Series A funding, even though it’s yet to reveal exactly what kind of technology it’s building.

Iguaz.io, which is still effectively in stealth, hinted at doing something about “rebuilding the framework for dealing with Big Data in a way that integrates information from numerous storage and processing platforms”. The new round of funding was led by Magma Venture Partners, with participation from Jerusalem Venture Partners and other anonymous investors.

The problem Iguaz.io is attemping to tackle is a valid one. Big Data handling has often been criticized for its inflexibility, due to the need to carry out repeated reads and select/extract/load cycles of large data sets to push additional data into HDFS systems. It’s slow and inefficient, and alternatives like in-memory processing with Spark can be extremely resource intensive.

Iguaz.io has yet to say exactly how it plans to tackle this problem, but the fact that it’s aiming to fix some of the problems with Spark suggests that whatever it does come up with, could be very big indeed.

“The challenge with Spark is the need to store the entire dataset in memory, and run over all the data, as opposed to read and process only relevant data,” wrote Iguaz.io CTO Yaron Haviv in a March 2015 blog post. “This is a challenge since memory and additional servers are quite more expensive than disk or even flash. Spark also lacks key database semantics like record updates, indexing, and transactions, so it is mostly applicable to analyzing medium sized datasets as a whole, or iterative machine learning, not for incremental updates to stored data records or for processing data in the tens of terabytes or more.”

Haviv continues by saying there are four elements to building a system that can efficiently handle real-time Big Data analytics, namely: Scalable and high-speed messaging layer for ingestion (e.g. Kafka); Stream or In-memory processing layer (e.g. Spark); Batch processing for crunching and digesting large datasets (e.g. MapReduce); and Interactive Real-Time Analytics and SQL tools for presenting the data.

Notably, these four elements all exist, but Haviv’s solution also requires a shared High-Volume and High-Velocity data repository for storing data records, files, messages and objects, and providing transaction semantics. This suggests to us that Iguaz.io could well be planning to build such a data repository.

No doubt Iguaz.io’s new financial backers are privy to its plans, and the $15 million they’ve stumped up shows that they believe the company’s co-founder Yaron Segev, former co-founder and CEO of XtremIO, is capable of pulling it off.

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