Modern Data Sources and Characteristics of a Modern BI Platform

  • video

    From Audio to Images to Text, Anything Can Become Data

    Mark Madsen President of Third Nature

    Watch this video to learn how we’ve arrived at the point where more data exists outside rather than in databases.

    Big data has made us rethink our very narrow view of data management, especially in the business intelligence, data warehousing field. We’re accustomed to thinking of data as records that are all the same type and form. But this is no longer true. The limits of analytical engines have actually limited what we consider data to be. Even in the early days of Hadoop a lot of projects dealt with data that could fit in a database.

  • video

    Modern Data Architecture: Production, Collection, Distribution, Consumption

    Mark Madsen President of Third Nature

    In this video, you learn about the various complexities involved in data architecture and why it should not be confused with data modeling.

    Data architecture is about how, where, and why you position data. Data architecture doesn't assume data is in a relational database although our past experience has led us to think that way. Data architecture involves solving the design problems that either support or impede an effective data supply chain. A data supply chain has four components: production, collection, distribution, and consumption. And for various use cases in data science and analytics, each stage has design problems that need to be solved.

  • video

    The Difference Between Business Intelligence and Data Science

    Mark Madsen President of Third Nature

    This video explains why data repeatability is one of the major differences between business intelligence and data science.

    With BI, we’re used to the repeatability of the data. We design data models that are used for many things. There are star schemas and normalized data models, query reports and dashboards. And all of these are forms of the same thing, and their purpose is data access and answering a set of questions that don’t change. But data science often can’t use repeatable data schema. The questions are constantly changing. And you can’t design a data model that can adapt to constantly changing questions

  • video

    Governed Self-Service

    Wayne Eckerson Founder & Principal Consultant at Eckerson Group

    Watch this video to learn why self-service and data governance have to go together -- and how it’s possible to balance the two.

    Users love self-service data discovery and analytics tools. And the terms self-service analytics are thrown around as if they represent a simple concept that’s easy to implement with no downside. But, unfortunately, for IT it’s not that simple.  Self-service tools remove one burden for IT, but replace it with another just as complex. Controlling and cleaning up the chaos that self-service tools can create. Likewise, tightly governed tools need to expand their self-service capabilities.

  • video

    Picking the Right Data Store: What is Spark?

    Mike Allen VP, Product

    In this video, you’ll learn about another modern data store -- Spark.

    With Spark’s in-memory processing, you can process workloads you couldn’t with traditional map bridge data access. For example, you can put machine learning algorithms in Spark. You can also use Spark as the query engine on top of Hadoop. That way you can have a very fast SQL layer that enables very fast queries. The power of Spark makes it suitable for many types of analytic tasks.

  • video

    Picking the Right Data Store: What is Hadoop?

    Mike Allen VP, Product

    Watch this video to learn, the democratizing effect Hadoop has had on big data and data analytics.

    Before Hadoop, querying large amounts data meant expensive proprietary hardware and software. Not anymore. Hadoop, an open-source project that can be deployed on commodity software, has evolved beyond MapReduce to solve a lot of different problems.

  • video

    Picking the Right Data Store: Data Storage Platforms

    Mike Allen VP, Product

    Watch this video to learn, how data storage platforms have evolved  to meet different analytic needs.

    In Darwin’s day knowledge was “stored” in books and books were kept in libraries. Today, while physical libraries still exist, a lot of data is stored digitally. Of course, cost is always a consideration when choosing a data store. But beyond cost, picking the right data store depends on the type of data you need to analyze and the questions you want to ask.

  • video

    Picking the Right Data Store: A History Lesson

    Mike Allen VP, Product

    In this video, you’ll find out how we got from the invention of magnetic storage and fast computers to today’s complex world of modern data platforms and streaming analytics.

    There were a lot of steps along the way starting with mainframes and hierarchical databases. Progress has occurred in every area: database design, query languages, scalable systems, and distributed architectures. Now open source projects like Hadoop and AI technologies like machine learning continue to advance the way organizations consume, store, and analyze data.

  • video

    Picking the Right Data Store: Generic or Specialized?

    Mike Allen VP, Product

    In this video, you’ll find out how we got from the invention of magnetic storage and fast computers to today’s complex world of modern data platforms and streaming analytics.

    There were a lot of steps along the way starting with mainframes and hierarchical databases. Progress has occurred in every area: database design, query languages, scalable systems, and distributed architectures. Now open source projects like Hadoop and AI technologies like machine learning continue to advance the way organizations consume, store, and analyze data.

  • video

    Picking the Right Data Store: Powerful Software

    Mike Allen VP, Product

    In this installment, you’ll find out the role of software in analyzing what you have in your data store.

    When it comes to query engines for non-relational data, you have quite a few choices. The current generation of query engines are powerful -- but they also take expertise to configure. So DIY may not be the way you want to go. There’s a lot to be said for pre-configured systems like AWS and Google BigQuery. And no matter what route you choose, the issues of security and scalability also figure into the equation.

  • video

    The Changing BI Paradigm and What is Required of BI Vendors

    Anurag Tandon VP, Product Management

    Watch this video to learn how the entire BI paradigm is changing and what this means for BI vendors.

    For software vendors, the changing BI paradigm means that time to analytics becomes a metric that application developers should measure themselves against. How long does it take someone to get value from his or her data? Because the lines have blurred between producers and consumers, the lines between the exploratory and consumption models have blurred as well. Producers are data explorers and so are consumers, albeit with less technical background. Now there’s a partnership with one goal: to produce more value for the business.

  • video

    BI Personalities in Modern BI: Producers and Consumers

    Anurag Tandon VP, Product Management

    Watch this video to learn about the personalities or personas that make up the analytics ecosystem.

    There are a lot of personas involved in analytics, from people who facilitate data collection and its movement through the data supply chain to system administrators to analysts to users who make decisions based on data insights. And, broadly speaking, these individuals fall into two categories: analytics producers or consumers. They are present for any analytics application. This is especially in analytics that are not automated through machine learning or bots -- human interactive analytics that organizations use to conduct business.

  • video

    Consumption and Exploratory Analytic Models

    Anurag Tandon VP, Product Management

    In this video, you’ll see how human-driven interactive analytics divide into consumption-oriented models and exploratory models.

    The consumption-oriented model usually involves a small group of analysts producing analytics for a broad group of consumers who use them in the day-to-day operations of an organization. In the exploratory model, analysts or data scientists work with and combine data sets to discover what kind of analytics they can support. At some point, the exploratory model can feed a consumption model. The characteristics of analytics producers and consumers change as traditional BI evolved to modern BI.

  • video

    Producers and Consumers of Analytics in Traditional BI Consumption Models

    Anurag Tandon VP, Product Management

    This video explains the roles of analytics producers and consumers in the traditional business intelligence consumption model.

    Producers were authors proficient in building primarily static, fixed-format reports, which were distributed to their consumer counterparts. Producers spent a lot of time on formatting. And changes to reports were time consuming to implement. Often consumers needed to educate authors about data domains and how to query data to get the needed results. In the traditional consumption model, consumers were front-line executives who simply accessed reports to support their decisions. Ease of access and presentation quality were the top priorities.

  • video

    Producers and Consumers of Analytics in Traditional BI Exploratory Models

    Anurag Tandon VP, Product Management

    Watch this video to see how producer and consumer roles change in the exploratory model of traditional BI.

    In the exploratory -- or ad hoc -- model for traditional BI, producers would still be report authors. But they would work with database architects to fine tune databases and develop “prompted” reports that allowed consumers to answer a series of questions about what needed to be queried: timeframe, geography, department, etc. Consumers were domain experts with more experience using BI tools. They had the patience to build queries and wait for a response. They were more likely managers, not front-line decision makers.

  • video

    Modern BI Producers and Consumers of Analytics

    Anurag Tandon VP, Product Management

    In this video, you’ll learn how the producer and consumer personas have changed in modern BI consumption and exploratory models.

    In modern BI’s consumption model, interactivity is demanded by modern BI consumers. Static PDFs will not cut it. And data freshness is critical for making fast, well-informed decisions. Producers in the consumption model are data explorers. They focus on data freshness, data accuracy, and the ability to ask the right questions of their data, and even the ability to combine data from multiple places on-the-fly. In a modern exploratory model, the line between producers and consumers is more blurry. There are simply explorers and advanced explorers.

  • video

    Transform the Customer Experience with Big Data

    Watch and listen as a panel of experts including Cloudera co-founder and CTO Amr Awadallah and two joint Zoomdata and Cloudera customers, Bidtellect and Markerstudy, explain the opportunities and challenges of using big data to increase customer engagement.

  • video

    UX+Data Meetup Rise of the Data Artist

    What’s a Data Artist and Why Do You Need One?

  • video

    theCUBE Spark Summit East 2016

    During Spark Summit East, Zoomdata founder and CEO Justin Langseth told Jeff Frick and George Gilbert, cohosts of theCUBE, from the SiliconANGLE Media team, that the company took Hadoop into account when it built the product.

  • video

    Hadoop in Transition: From Proof of Concept to Production

    In this webinar we will summarize what was learned, as well as provide insights about skills development, open source vs. commercially-provided distributions, ease of use, integration, application development, and testing, challenges in implementation, and the perception of value of embracing the Hadoop paradigm.

  • whitepaper

    Hadoop in Transition: From Proof of Concept to Production

    DecisionWorx in collaboration with The Bloor Group recently conducted research to solicit information about the Hadoop adoption and productionalization process in order to provide insight into the current state of integration among a variety of organizations. The research spans across different industries and levels of both individual contributors as well as corporate experience. 

  • podcast

    DM Radio: A Spark of Genius? How the Hadoop Ecosystem Evolves

    First, there was Hadoop. Then, there was YARN. And then? Spark took the industry by storm! What is this Big Data processing engine all about, and how does it change the game? The big distro vendors are all speaking the language these days, even if they seemed reluctant at first.

  • podcast

    DM Radio: Method to the Madness -- New Roles for the Information Architect

    The constraints of yesteryear continue to fade away, opening up a whole new class of information solutions. From big data discovery to real-time analytics, from highly scalable infrastructure to lightning fast apps, the possibilities today are nearly endless. Learn how can your company take advantage.

  • whitepaper

    Rise of the Data-Driven App

    In its report on data-driven apps, 451 Research notes that Zoomdata makes “it easy to embed visual analysis so that the user experience is exactly what the data-driven app needs.”

  • whitepaper

    Big Data Analytic Tools Checklist

    Written by Wayne Eckerson and Phil Bowermaster from Eckerson Group, this report defines the 10 most important characteristics of a big data analytics tool. Companies should evaluate their current or proposed reporting and analysis solutions against these 10 characteristics to ensure they are getting a product designed from the ground up to handle big data at speed that emanates from a variety of systems and sources, including the internet of things.

  • ebook

    TDWI Best Practices Report

    Accelerating the Path to Value with Business Intelligence and Analytics.  When It Comes to Analytics, Patience Doesn’t Pay.

  • ebook

    TDWI eBook: Data-Driven Analytics: Knowing What You Don't Know

    This eBook discusses the importance of a data platform that can reduce “time to analytics” for business users.

  • ebook

    AWS Data Lake Users Need Zoomdata Modern BI

    Learn why the proper combination of infrastructure and analytics tool is so important in this ebook. 

  • ebook

    How to Deliver Optimized Analytics for Redshift with Etleap and Zoomdata

    Etleap and Zoomdata have optimized the way analysts extract, transform, load, and analyze data using Amazon Redshift data warehouses. Etleap’s cloud-based ETL tool guides even the inexperienced user through combining virtually any data source into Redshift. And Zoomdata connects directly to data warehouses of any size to visually interact with data in ways that were previously not possible.

  • podcast

    DM Radio: The Analytical Enterprise: Fostering a Data-Driven Culture

    What defines the data-driven business? Analytics! But how does an organization foster a culture of insight? Register for this episode of DM Radio to learn more.

  • whitepaper

    Total Data Analytics 2016

    Understand total data reporting and its evolution.

  • video

    Zoomdata Dashboard Creation

    Watch the creation of a dashboard using a sample set of real-time sales data. It's easy to combine charts and graphs that display sales by region, customer segment, and other filters.

  • infographic

    Play With the Analyze Search Engine Data Interactive Demo

    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.

  • infographic

    Play With the Blend Multiple Data Sources Interactive Demo

    Zoomdata Fusion blends data from multiple sources without replicating or moving the data and without extract-transform-load (ETL) processes. Visualize and analyze data blended across multiple data sources. Try it now. Users simply love it. IT loves the simplicity.

  • whitepaper

    'OLAP on Hadoop' And Visual-Based Analytics Open the Door to Total Data Analytics

    What’s New in Total Data Analytics?

  • whitepaper

    NoSQL Analysis Tools: A New M and A Frontier for 2016

    With NoSQL stores increasingly deployed in enterprise settings, and the growing need to gain insight from data in these databases, NoSQL analytics could shape M&A activity in 2016.

  • whitepaper

    Mighty Guides Data Disruption

    To gain a fuller understanding of how modern analytical methods are being used in visible and not-so-visible ways, Mighty Guides approached data analytics experts from many fields and industries. We asked them to contribute essays about their experiences applying big data analytics. This e-book is a compilation of those essays. In it you will find discussions about new analytics technologies, how organizations can more effectively use their data assets, and many interesting use cases.

  • ebook

    What Are the Elements of Modern Data Sources?

    I Want to Understand What’s Different About Modern Data Sources!

    How do you imagine data? If you’re thinking about it in terms of uniform records and databases, it’s time to make a brain update. Back in the day, analytical engines were limited, so our perception of what could be considered data was, too. Today, the big data renaissance has begun, and actually, more of the data exists outside of databases than inside them, plus, EVERYTHING is data.

    We’re going to help you discover how business intelligence and data sources of today have changed, and as a result, so has our approach to analyzing data. An eBook on this very topic is waiting for you—all it takes is the click of a button.

  • ebook

    Darwin Goes to the Library: Selecting the Right Data Store

    Find out how data storage platforms have evolved to meet different analytic needs, and scalability, performance, and security are all considerations.

Modern Data Sources and Characteristics of a Modern BI Platform

Learn how modern data platforms make it possible to store and analyze billions of transactions and events streaming in real time.

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