A software stack is emerging for streaming data applications that process high velocity flows of data in motion, or “fast data.” Similar to the LAMP stack for web servers, the Fast Data Stack describes what’s needed to meet the requirements for fast data applications: to grab real-time data and output recommendations, decisions and analyses in milliseconds. Listen as Ryan Betts describes the architecture of the Fast Data Stack which include ingestion, real-time analytics and decisions, and data export. He’ll explain how new applications can benefit from this architecture when processing real-time data, and providing decisions and analyses in milliseconds.
Presented by David Peters, CEO, Emagine and Peter Vescuso, CMO, VoltDB. The modern telecommunications data center environment must cater to billions of high frequency events daily. For Communications Service Providers, (CSPs), capturing the attention of customers by using this data for personalizing customer interactions, campaigns and loyalty programs is the difference between achieving a competitive advantage and increased profitability and slipping behind. Customers now expect more personalized content and offers at the right place, at the right time using the right channel. They will gladly go to competitors for better offers or a better customer experience.
Big Data is transforming the way enterprises interact with information, but that’s only half the story. The real innovations are happening at the intersection of Fast Data and Big Data. Why? Because data is fast before it’s big. Fast Data is generated by the explosion of data created by mobile devices, sensor networks, social media and connected devices – the Internet of Things (IoT).
Developers increasingly are building dynamic, interactive real-time applications on fast streaming data to extract maximum value from data in the moment. To do so requires a data pipeline, the ability to make transactional decisions against state, and an export functionality that pushes data at high speeds to long-term Hadoop analytics stores like Hortonworks Data Platform (HDPTM). This enables data to arrive in your analytic store sooner, and allows these analytics to be leveraged with radically lower latency.
Presented by Heikki Hämäläinen, Leader, Big Data, Eficode and Ryan Betts, CTO, VoltDB. Personalization is increasingly becoming the key differentiator between enhancing customer engagement, and losing that competitive advantage. Leveraging data instantly provides the opportunity to make real-time decisions, reduce risks, sense patterns, and identify customer behavior.
In this webcast, CMO Peter Vescuso and Dr. Michael Stonebraker discuss the new corporate data architecture and the necessary technology components for facing this data management challenge. Listen as they discuss the “one-size-never-fits-all” perspective for developing the ideal architecture for managing, and maximizing the value of fast, big data in your organization.
Writing applications on top of streaming data requires both scalable high throughput event processing, as well as efficient large volume storage. At scale, this requires combining best-in-class tools to create a complete solution. Real-time applications and the increasingly fast data streams created by personalized devices, IoT, and M2M, exceed the processing and storage capacity of legacy databases.
VoltDB CTO, Ryan Betts and Flytxt CTO, Prateek Kapadia discuss how the modern telecommunications data center environment must cater to billions of high frequency events daily. Technology and Business teams are faced with a challenge to architect and manage analytics platforms that extract optimum value from these event streams. Combining fast data with the advanced big data analytics through mining large volumes of historical data is increasingly becoming a key technology enabler for competitive differentiation and sustained economic value generation. Tapping into the value of data in real-time – the moment it arrives – is a significant opportunity but it requires the ability to track billions of events, generate real-time triggers from those billions of events reflecting the contextual usage and deviation in defined behavior, as well as to take right action at right time through the right channel instantaneously.
Building applications on streaming data has its challenges. If you’re a developer trying to use programs like Apache Spark or Storm to build apps, this webinar will explain the benefits and downfalls of each solution and how to choose the right tool for your next streaming data project.
Listen to Walt Maguire, Chief Field Technologist of HP Vertica and Ryan Betts, CTO of VoltDB discuss the Fast + Big Data challenge, and new approach required for both corporate data architectures and the data management systems that enable them.
Listen to CTO Ryan Betts as he explains how stream processing was not designed to serve the needs of modern Fast Data applications. Employing a solution that handles streaming data, provides state, ensures durability, and supports transactions and real-time decisions is key to benefitting from fast data.