In Architecting for the IoT, VoltDB CTO Ryan Betts explores the IoT from the convergence of three powerful trends: Big Data, Cloud, and Fast Data. The Internet of Things is bridging machine-to-machine communications, big data, cloud computing, distributed systems, networking, mobile and telco, apps, and smart devices. Ryan explains the emergence of a reference architecture for the IoT and the critical role that operational databases play in that convergence.
Fast data is data in motion, streaming into systems from hundreds of thousands to millions of endpoints – mobile devices, sensor networks, financial transactions, stock tick feeds, logs, retail systems, telco call routing and authorization systems, and more. This 5-step Guide to Fast Data Success will introduce you to fast data strategies at a practical level and provide you with a roadmap to rapid execution and implementation.
Fast data is data in motion, streaming into systems from hundreds of thousands to millions of endpoints – mobile devices, sensor networks, financial transactions, stock tick feeds, logs, retail systems, telco call routing and authorization systems, and more. Check out this guide to get the five steps to make the right moves with fast data.
The Gartner ODBMS Magic Quadrant (MQ) is considered the definitive source for competitive comparisons in the information technology industry. An MQ offers visual summaries and in-depth analyses of the direction and maturity of markets and the key vendors.
Building streaming data applications that can manage the massive quantities of data generated from mobile devices, M2M, sensors, and other IoT devices is a big challenge many organizations face today. Data ingestion is a pressing problem for any large-scale system. Several architecture options are available for cleaning and preprocessing data for efficient and fast storage. In this report, we will discuss the advantages and disadvantages of various fast data front ends for Hadoop.
451 Research analyst, Jason Stamper discusses the latest VoltDB release featuring geospatial support. With the addition of geospatial support, VoltDB says its database better meets the needs of modern applications, particularly in areas such as mobile, media and e-commerce.
The in-memory database platform represents a new space within the broader data management market. Enterprise architecture (EA) professionals invest in in-memory database platforms to support real-time analytics and extreme transactions in the face of unpredictable mobile, Internet of Things (IoT), and web workloads. This report details how well each vendor fulfills Forrester’s criteria and where the vendors stand in relation to each other to meet next-generation real-time data requirements.
The need for fast data applications is growing rapidly, driven by the IoT, the surge in machine-to-machine (M2M) data, global mobile device proliferation, and the monetization of SaaS platforms. So how do you combine real-time, streaming analytics with real-time decisions in an architecture that’s reliable, scalable, and simple? Ryan Betts and John Hugg from VoltDB examine ways to develop apps for fast data, using pre-defined patterns. These patterns are general enough to suit both the do-it-yourself, hybrid batch/streaming approach, as well as the simpler, proven in-memory approach available with certain fast database offerings.
VoltDB was engineered to handle Fast Data. We’re so excited by its capabilities that we worked with O’Reilly Media to write the eBook, Fast Data and the New Enterprise Architecture. Download the eBook today to learn more about Fast Data and the new enterprise data architecture—a unified data pipeline for working with Fast Data (data in motion) and historical Big Data - together. Written by VoltDB Co-Founder & Chief Strategy Officer, Scott Jarr.
In this analyst report, Ovum Analyst Clare McCarthy discusses the pressing need for telcos to operate at Internet speed, but struggle to do so: They need to handle big data – and, increasingly, fast data – from a variety of sources and to take action in real time. VoltDB’s technology platform taps into live or in-session data flowing into the organization (such as clickstream and user-experience data), and analyzes it in real time. This provides telcos with a range of monetizable use cases that would not otherwise be possible.
VoltDB Technical Overview is a white paper that covers VoltDB architectural concepts, identifies popular use cases and provides information about how to get started using VoltDB.
Municipal officials worldwide are adopting technologies to enhance services, meet sustainability goals, reduce operating costs, and improve the local economy and the quality of urban life. Cities face competition to attract new residents, businesses, and visitors, and local political leaders face the challenges of balancing budgets while seeking new ways of dramatically improving public services. Read more about how VoltDB and Fast Data enable the future of connected cities in this whitepaper.
Successfully writing applications to manage fast streams of data generated by mobile, smart devices, social interactions, and the Internet is development’s next big challenge. This VoltDB white paper explores three different approaches – fast online transaction processing (OLTP) solutions; streaming solutions, e.g. complex event processing (CEP) systems and newer open-source tools; and fast online analytical processing (OLAP) solutions, assessing the strengths and weaknesses of the three contenders’ underlying architectures and providing guidance in the selection of an approach to solving the fast data challenge.
The availability and abundance of fast, new data presents an enormous opportunity for businesses to extract intelligence, gain insight, and personalize interactions of all types. As applications with new analytics capabilities are created, what were two separate functions – the application and the analytics – are beginning to merge. CTOs and development managers now realize they need a unifying architecture to support the development of data-heavy applications that rely on a fast-data, big-data workflow. This white paper looks at the requirements of the fast data workflow and proposes solution patterns for the most common problems software development organizations must resolve to build applications – and apps – capable of managing fast and big data.
Software designers and architects build software stack reference architectures to solve common, repeating problems. A great example is the LAMP stack, which provides a framework for building web applications. Big Data is data at rest; Fast Data is streaming data, data in motion. A stack is emerging across both verticals and industries alike for building applications that process these high velocity streams of data that quickly accumulate into the ‘Big Data lake.’ This new stack, the Fast Data Stack, has a unique purpose: to grab real-time data and output recommendations, decisions and analyses in milliseconds.
In this report by 451 Research Analyst, Jason Stamper, he will discuss how VoltDB’s in-memory relational database is aimed at the analysis of what the firm calls fast data: enabling companies to make real-time decisions on data as it arrives.
Computer architectures are moving towards an era dominated by many-core machines with dozens or even hundreds of cores on a single chip. This unprecedented level of on-chip parallelism introduces a new dimension to scalability that current database management systems (DBMSs) were not designed for. In particular, as the number of cores increases, the problem of concurrency control becomes extremely challenging. With hundreds of threads running in parallel, the complexity of coordinating competing accesses to data will likely diminish the gains from increased core counts.
In Q1, 2014, VoltDB polled database managers, analysts, administrators and other IT professionals about the databases they use, the results of their Big Data projects, and opinions about Big Data technology advancements. The 2014 Big Data Survey reveals that most organizations cannot access, let alone utilize, the vast majority of the data they collect, and exposes a major Big Data divide: the ability to successfully capture and store huge amounts of data is not translating to improved bottom-line business benefits. What’s to blame? Deficiencies in database performance.