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IoT’s Impact on Data Warehousing: Defining IoT in Terms of Its Data Requirements
The Internet of Things (IoT) is a computing paradigm where a widening range of physical devices—including smartphones, vehicles, shipping pallets, kitchen appliances, manufacturing robots, and anything fitted with a sensor—can transmit data about their location, state, activity, and surroundings. Depending on the device type, some may also receive data and instructions that control device behavior.
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Making Multiplatform Data Architectures Work for You: Common Use Cases and Reference Architectures
To leverage the new wave of advanced data sources available, users and architects are turning to a multiplatform data architecture (MDA), where numerous diverse data platforms and tools are integrated in a multiplatform, distributed architecture. An MDA is typified by an extreme diversity of platform types that may include multiple brands of relational databases, NoSQL platforms, in-memory functions, and tools for data integration, analytics, and stream processing. Any of these may be on premises, in the cloud, or in hybrid combinations of the two.
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Ask the Expert: Data Science
TDWI Members Only
It’s hard to find a topic out there hotter than Data Science right now; and can be equally hard to find one more confusing. Data Science techniques have revolutionized nearly any industry you can imagine, and in some cases created whole new ones from thin air. Despite this, much of Data Science remains couched in mystery--a magic black box that is supposed to solve all of our problems.
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Get More Business Value from a Data Lake via Data-as-a-Service (DaaS)
Data lakes are coming on strong as a modern and practical way of managing the large volumes and broad range of data types and sources that enterprises are facing today. TDWI sees data lakes managing diverse data successfully for business-driven use cases, such as omni-channel marketing, multi-module ERP, the digital supply chain, and data warehouses extended for business analytics. Yet, even in business-driven examples like these, user organizations still haven’t achieved full business value and return on investment from their data lakes.
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Defining a Multiplatform Data Architecture and What It Means to You
A revolution is occurring in modern analytics, driven by our ability to capture new sources of information at a detail previously too complex and costly to imagine. As more data comes from new sources (from machines to social media) and is applied to new applications, data is evolving into greater diversity, including every variation of data type from unstructured to multistructured. Even as new tools to analyze and manipulate this newly available resource come online, it is not enough to look at the data manipulation layer alone.
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Accelerating the Path to Value with Business Intelligence and Analytics: A TDWI Best Practices Research Report
Organizations of all sizes are in competition to realize value from data – and to realize it faster. To do so, they increasingly need flexible and agile business intelligence(BI), analytics, and data infrastructure, not systems that take too long to develop and do not give users the dynamic, iterative, and interactive access to data that they need. Fortunately, technology developments are trending in a positive direction for organizations seeking to accelerate their path to value with BI, analytics, and the critical supporting data infrastructure. These include self-service BI and visual analytics, self-service data preparation, cloud computing and software as a service(SaaS), and new data integration technologies.
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Architecting a Hybrid Data Ecosystem: Achieving Technical Cohesion and Business Value in a Multi-platform Environment
One of the strongest trends in data management today and into the future is the development of complex, multi-platform architectures that generate and integrate an eclectic mix of old and new data, in every structure imaginable, traveling in time frames from batch to real time. The data comes from legacy, mainstream enterprise, Web, and third-party systems, which may be home grown, vendor built, open source, or a mix of these. More sources are coming online from machines, social media, and the Internet of Things. These data environments are hybrid and diverse in the extreme, hence the name hybrid data ecosystems (HDEs).
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Metadata Discovery Doesn’t Have to be Painful
Today’s BI and analytics landscape is more complicated than it ever has been. Most organizations have repositories of data everywhere, many BI and analytics technologies, and multiple streams of data integration and/or data preparation – all with their own sets of metadata. No wonder BI implementers and business users are frustrated, confused, and lost when it comes to using these critical assets.
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Data Management for Big Data, Hadoop, and Data Lakes
A perfect storm of data management trends is converging.
First, organizations across many industries are experiencing the big data phenomenon, which forces them to capture and leverage data from new sources, in structures and velocities that are new to them, in unprecedented volumes. Second, technical users are scrambling to learn new data platforms like Hadoop and their evolving best practices. Third, the data lake arose suddenly in 2016 as the preferred approach to managing very large repositories of raw source data. Fourth, business managers have attained a new level of sophistication in their use big data for business value and organizational advantage.
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Between a Rock and a Hard Place: How to Modernize Legacy Middleware for an Evolving, Data-driven World
In support of daily operations, many organizations depend heavily on systems for enterprise application integration (EAI), enterprise service bus (ESB), and other approaches to middleware. Yet, these infrastructures are today legacy technologies that predate the rise of big data and unstructured data, as well as modern sources and targets for integration, such machines, devices, clouds, social media, and the Internet of Things (IoT). Furthermore, many middleware vendor tools are still optimized for the on-premises ERP-dominated applications world of twenty years ago; others are in legacy mode, with no future upgrades coming.
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Database Strategies for Modern BI and Analytics
The data universe has changed. Big data, cloud computing, and open source have dramatically expanded the number of data warehousing offerings available to today’s businesses. An increasing number of companies are implementing self-service business intelligence (BI) and visual analytics tools to access and make sense of all of the new and diverse sources of data their teams are consuming. Data literacy is changing equally fast as an increasing number of “data consumers” want to interact with data on their own rather than through IT.
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