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A Real-Time Data Aggregation Solution

Improved time-to-value of event and batched data for accurate, usable insights through immediate and schematic data aggregation of numerical and non-numerical data points.

Data Aggregation Case Background

As data systems grow in complexity, taking into account numerous systems and processes, the amounts and types of data being collected are steadily increasing. However, no matter how much data is collected, without the ability to organize it into an easy-to-use, simple summary it cannot be analyzed and turned into actionable insights. Data aggregation is the process of taking large data sets, compiling them together, and utilizing different events, sums as well as other statistical calculations to derive answers from the data itself. This process makes data usable for dashboards and reports to be used by employees at every level across a company.

The Data Aggregation Issues Faced

The value creation needed to stay competitive in marketing plans, pricing schedules, and so forth requires consistent findings, continuous evolution, and ultimately data usability. Today companies are ingesting data at unprecedented rates, but proper data aggregation is required to reveal the insights needed for strategic planning.  Aggregating real-time and batch data is a challenge in itself, but to do so in a cost-effective fashion is a feat that is difficult to accomplish.

Processing Times Saved with Datorios' Data Aggregation Solution

Utilizing the process of identifying unique keys and conducting data aggregation on-the-fly, as part of the data pipeline, Datorios’ schematic approach takes into account defined data collection intervals including granularity, reporting, and polling periods. These intervals can then be summarized into easy-to-use, simplified results. Insights from large data pools are revealed by applying mathematical functions for numerical and non-numerical data sets, such as averages, sums, max and min data. The result is the creation of fresh, adjustable data for dashboards while saving processing time and the cost of doing multiple database iterations.

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