A technique for ionospheric data ingestion is discussed.
Big data increases companies' data ingestion by many orders of magnitude, adding to the complexity.
Data ingestion updates the model with local level of ionization, enabling it to follow the observed changes of ionization levels.
On the other hand, data structures that can sustain high data ingestion rates tend to only support very simplistic queries.
"What you don't realize is that it is the front to this huge data ingestion machine that is taking all of those statements by that child and then using them for a whole range of purposes".
The throughput and end-to-end latency of the system can have complicated relationships with batch sizes, data ingestion rates, variations in available resources, workload characteristics, etc.
Enabling these applications require real-time monitoring and analysis tools that can support high throughput data ingestion, low-latency online queries, and low-overhead offline queries.
Unfortunately, data structures for supporting these tend to have high update overheads, and are unable to sustain the data ingestion rates required for most applications.
It is important for such frameworks to be robust against variable operating conditions such as server failures, changes in data ingestion rates, and workload characteristics.
While data structures exist for high throughput data ingestion, and for supporting expressive online and offline queries, the two have so far remained mutually exclusive.
Atomic MultiLog exploits the application workload assumptions outlined above to facilitate both high throughput data ingestion, as well as rich online and offline queries.
