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 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.
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.
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.
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.