As organizations increase their interest in streaming data, near-real-time analytics and the use cases they support, they’re often frustrated by a lack of visualization tools that can handle this data.
Traditional business intelligence (BI) tools, many of which have been around for at least a decade, were not designed for the real-time world. Although the vendors who offer these tools are trying to retrofit them to accommodate near-real-time data, the results have been disappointing. And, not only do these tools lack real-time capabilities, they also can’t manage the huge volume of streaming data generated by IoT smart devices and event-driven software.
In addition, legacy BI tools were typically built to connect with data warehouses, which by definition means that data under analysis is not fresh. It’s been aggregated and loaded into a warehouse that might be refreshed overnight or, at most, a few times a day. Also in the aggregation process, some data is lost because to populate the data warehouse, decisions have to be made about which fields to include in the data model. Some detail is inevitably lost, which limits the scope of any analysis.
The Zoomdata platform provides enterprise-grade, near-real-time visualization of streaming data based on self-service, interactive, sub-second response to ad hoc queries of high-velocity data. It’s purpose-built for streaming data, with a scalable architecture that can push updates from the source through our stream processing engine to end users via a WebSockets connection.
Zoomdata features an interface that can integrate with any stream processing infrastructure. More than simply monitoring near-real-time data feeds, Zoomdata enables streaming analytics based on visual interaction with the stream. Users can dynamically filter the stream and change the aggregation level, as well as pause, rewind, replay, and fast-forward the stream.
Since working with streaming data demands a different approach than querying historical data, such as monitoring updates pushed to the user interface, Zoomdata developed the data DVR (digital video recorder). The data DVR combines the ability to visualize streaming data and historical data in a single user interface.
It works just like the DVR you use to watch and record live television. It has similar controls so rather than simply monitoring near-real-time data, you can pause it, rewind, replay, or fast-forward. In addition, Data DVR’s time controls function the same way for historical data replayed as a stream. Users can transition seamlessly from real-time monitoring to historical analysis, just by expanding the time bar.
The Data DVR Time Bar
The time controls let you choose the time period to display for a data set by using the time slider. Simply expand the time window from monitoring the live feed to a longer time horizon to analyze its full history. To see changes reflected in your chart, graph, or dashboard you simply scroll forward or backward in time. You can accelerate the speed at which a visualization progresses, so that each second represents a minute, an hour or a day. And you can apply time constraints using current, rolling and historical presets by the hour, day, week, month, quarter, year and more.
Data DVR Time Controls
Visualizations Quickly React to New Data
The streaming visualization below depicts taxi pickup and drop-off activity in New York City.
Streaming Data Visualization
In a recent O’Reilly survey, 15 percent of respondents indicated they needed to analyze data less than one hour old and another 10 percent indicated the data needed to be less than one minute old. The demand for streaming analytics is clearly on the rise, and Zoomdata has the capability to analyze and visualize streaming data, combined with historical data. To learn more about Streaming Analytics, check out the Business Analytics Master Class series on streaming.
What is data visualization?
Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized more easily with data visualization software.
Why is data visualization important?
Data visualization allows us to explore, process, interact with, and understand information more easily. Well executed visualizations -- charts, graphs, dashboards -- can be simple yet powerful. They often make it easier to separate the relevant information from the irrelevant -- the signal from the noise.
What is interactive data visualization?
According to Gartner, interactive visualization enables the exploration of data via the manipulation of charts and graphs, where the color, brightness, size, shape and motion of visual objects represents aspects of the data set. These image components change as the data streams change. They're not static. These visualizations often go beyond the standard pie, bar and line charts to include heat maps, object maps like trees or taxis, geographic maps, scatter plots, and other special-purpose visuals. Often the underlying data is generated by sensors operating continuously.