Watch this video to learn about analyzing and taking action on data in motion -- streaming analytics.
Streaming data often has a short half life. It loses value quickly. This is in contrast to conventional analytics, which takes a more historical approach. Conventional and streaming analytics are complementary. Conventional analytics can provide context for streaming analytics, helping analysts know the right questions to ask in real time.
In this video, you’ll find out how streaming analytics moved beyond complex event processing for a few large organizations -- mostly on Wall Street. As bandwidth has gotten cheaper and new software technologies entered the market, streaming analytics applications have exploded.
This video explains why streaming data and streaming analytics have become such hot topics.
A lot of factors have converged. Cheaper bandwidth. More wireless and smart device traffic. Smarter software. Practical use cases. In other words, the Internet of Things -- IoT. Ovum believes smart devices will account for most of the data on IP networks by 2019.
From this video, you’ll get an idea of who is going to use streaming analytics and for what.
The short answer is: almost everyone for almost everything. There are plenty of uses cases -- online and physical -- in nearly every industry. Physical retailers can use it for location based marketing. Manufacturers can use streaming analytics to fine tune maintenance procedures and prevent breakdowns. Of course, the automotive industry is already embedding sensors in virtually every system that runs a car. And weather predictions are getting increasingly accurate with streaming analytics.
Watch this video to get an idea of what potential streaming analytics may be able to do for your organization. And the challenges you may encounter.
First, you’ll have choices to make depending on the use case you have in mind. One of the first will be the kind of streaming engine you need. For example, some streaming engines like Spark Streaming actually use a form of very fast batch processing with micro batches. Others engines, like Twitter’s Heron, offer pure, one-event-at-a-time streaming.
Watch this video, to learn why it’s important that your analytics tool supports batch and streaming data.
Part of any big data strategy includes determining the role of batch and streaming data. To succeed with big data, sometimes you need to dig deep into massive historical data sets. For that, you need a tool that can analyze batch data. Other times, you need to analyze what's happening as it happens. That's a job for streaming data analysis. That’s why you need a tool that can do both. Both are valuable depending on the particular application or solution you're building.
Watch this recording of Chief Technologist, Ruhollah Farchtchi's, presentation at Spark Summit San Francisco.
IoT data is growing at such an enormous rate that it’s simply overwhelming traditional data storage and analytics technology.
Watch Zoomdata CTO, Ruhollah Farchtchi, present at Spark Summit East 2017.
Take a seat as DM Radio host Eric Kavanagh interviews five industry experts about the impact of real-time data on organizations. and how yours can benefit.
Pause. Rewind. Fast-Forward Your Data. Now you can stream your data just like a Netflix™ movie.
Zoomdata Real-Time Analytics Powers
CellOS Revenue Assurance
We all know that the value of data decreases over time. So the faster you can gather and analyze it, the more value you can extract from it. When you can literally measure the half-life of data in days, hours, or even minutes, batch-oriented data warehouses just clog your data supply chain.
Zoomdata enables visualization and analysis of streaming data at the speed of thought. Seamlessly move between real time data and history - pause, rewind, fast-forward - it's easy. Try it now. Users simply love it, IT finally sees its investment in streaming data shine.
Zoomdata enables speed-of-thought visualization and quantitative analysis of unstructured and structured data stored in a search engine such as Elasticsearch, Solr, or Cloudera Search. In this interactive demo, analyze business operations by enhancing business transactions with millions of free text product reviews. Try it now.
In this video, we explore the limitations of current geospatial visualization technologies.
Most telcos have the capacity to effectively display operational data such as available bandwidth and signal strength. But significant latency plagues efforts to transfer data collected from antennas in batches to regional and central data centers. In addition, legacy geospatial visualization tools do not pass through enterprise firewalls. Nor are they available on mobile devices such as tablets and phones. These constraints hamper data analysts who must connect to these data sources from inside the enterprise firewall and analyze data in huge spreadsheets on their desktops.
This video offers shows how Zoomdata partner, Aktiun, built a solution for geospatial data visualization.
The solution allows for data discovery across traditional relational databases, big data SQL, NoSQL, and search databases. It also supports high-throughput processing of geographically distributed events in real time using streaming technologies. XML5-based web technologies make the solution mobile-friendly, pass data through firewalls, and enable real-time and historical analysis of massive data sets. Plus, the interface can mimic legacy systems that allow users to visualize sites, sectors, and layers.
Watch this video to gain more knowledge about how Aktiun built a geospatial data visualization solution for telcos.
For telcos, the solution provides timely operational insights about their network from any device. Its features include:
In this video, you’ll see a demo of geospatial data visualization for telecommunication companies.
For example, with the solution’s metric selector you can choose between any metric you’ve configured and render the metrics with different markers. You’ll also see how a range slider enables filtering KPI values using color ranges. Mapping options are extensive and include OpenStreetMap, MapQuest, and Mapbox. You’ll also see the solution’s ability choose from complex markers and represent their values using color scales and text fields. One of the most impressive features is the ability to see 3D elements using WebGL technologies.
In this webinar, Streaming Analytics and the Immediacy of Modern Business, Third Nature’s Mark Madsen and Zoomdata’s Ian Fyfe explain that to keep up with and profit from rapid changes in the marketplace, businesses need to harness streaming data.
Enterprises need new methods for ingesting, analyzing and acting on data quickly and efficiently. To detect threats, they require tools that enable rapid-fire discovery of data as it’s streaming, throughout their organization and beyond. This IM Live webcast explores the power of micro-queries for diving into big data sets of all shapes and sizes.
You’re not going to believe this, and it’s a bit of a spoiler, but streaming is not a new thing. It all started in the Stone Age. If a caveperson saw a T-Rex charging them, they had to process the data in real time, which is pretty much what streaming analytics is. Mind blown?
This colorful, enchanting eBook was created to help you understand how streaming analytics started and why it’s so important in business today.
Watch this video to learn about the challenges facing telecommunication companies that want to apply real-time data visualization to geospatial data.
The ability to visualize geospatial is especially important to telcos. Yet most data visualization solutions offer only two dimensional forms of display such as tables, line charts, and bar charts. They lack the ability to apply concepts of direction, sectors, layers, and cells in geospatial systems, which can provide very useful information for decision-making. Plus, telcos may have thousands of sites producing data for voice, SMS, and other data communications.
Traditional methods of data visualization & analysis have been outpaced by the data they analyze. Learn how streaming differs from traditional analytics.