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Zoomdata Technology: Cloudera Impala, d3.js and Big Data Analytics

This video demonstrates Zoomdata's ability to analyze and visualize billions of rows of raw HDFS data via Cloudera Impala. Users can see big data visualized and analyzed instantly, at scale, in real-time and historically, without moving it out of HDFS. The visuals are also completely interactive and customizable. Any d3.js data visualization can be lit up by Zoomdata with real-time and historical data and instantly become interactive and collaborative.

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Recorded Mar 23 2016 4 mins
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Presentation preview: Zoomdata Technology: Cloudera Impala, d3.js and Big Data Analytics

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  • Trends in the Modern Data and Analytics Market and Industry Forecasts for 2018 Recorded: Feb 22 2018 61 mins
    CTO, Ruhollah Farchtchi & Jake Flomenberg Partner at Accel Partners
    Silicon valley venture capitalist Jake Flomenberg gets to see, track and make investments in the evolution of big picture technology trends across areas such as big data analytics, machine learning and artificial intelligence, and emerging modern data platforms and data types that are enabling organizations to be data and analytics-driven. Data driven companies make more effective information backed decisions and tend to significantly outcompete their peers operating on guesses and gut feel. Some companies are now even selling ‘data products’, for example aircraft engine manufacturers tracking and analyzing huge volumes of engine performance data to enable predictive maintenance, fixing things before failure to avoid costs and negative impact on customers.

    Jake will discuss trends he’s seeing related to the data analytics market, such as the growing need for self-service data discovery on very large volumes of data, analytics on streams of data, analytics on unstructured data, and contextual analytics embedded inside of software vendor and enterprise applications. He’ll discuss the characteristics of these markets, where he sees these markets going in the future, and why his firm chose to invest in a company like Zoomdata.

    Zoomdata CTO Ruhollah Farchtchi will then discuss industry trends he has forecast for 2018 including the eclipsing of relational databases by modern data platforms for doing analytics, how the cloud has changed the game for application development, and how working with streaming data is becoming the new normal. He’ll also discuss trends that are more specifically relevant to Zoomdata such as how to leverage the value of company’s investments in modern elastically scalable back-end data infrastructure and how to get corresponding value on the front-end, such as the ability to analyze and get insights from huge volumes of data, streaming data, and unstructured data types.
  • The Rise of Embedded Analytics, How To Do It Right Recorded: Feb 21 2018 50 mins
    Anurag Tandon, VP, Product Management
    Today's most successful companies are data-driven, making informed decisions instead of guesses. Traditionally data-driven insights have been delivered in the form of standalone business intelligence applications, but in the past few years we’ve seen a big shift towards delivering contextual analytics embedded directly into other software applications and business workflows. For software vendors this means meeting high end-user expectations by infusing compelling data visualizations and analytics into every application you create and sell. Modern data visualization and analytics applications, therefore, need to be designed from the ground-up to be quick and easy to embed for a web and mobile-first world. They also must capable of handling today's rapidly evolving modern data platforms so that your analytics technology does not lock you into using yesterday's
    data stores.

    If you're a CTO, product manager, or software engineering leader, you may be curious about whether to build or buy an integrated analytics solution, how to evaluate the available offerings for embedded analytics, and how to most efficiently embed data visualization and analytics into your application. In this webcast, we will cover the following topics:

    ● Assessing the current state of your application's integrated analytics
    ● Approaching the build vs. buy decision
    ● Styles of embedding, from light to deep
    ● Considerations for deployment flexibility
    ● Security integration considerations
    ● Avoiding data platform lock-in
  • Four Dominant Hadoop Distributions Recorded: Sep 13 2017 2 mins
    Howard Dresner, President, Dresner Advisory Services
    Watch this video for information about the importance of big data distributions.

    There are four dominant Hadoop distributions: Cloudera, Hortonworks, Amazon, and MapR. All four are gaining increased interest as the level of big data adoption grows. The market leader right now is Cloudera although high tech prefers Amazon as do smaller organizations -- up to 1,000 employees.

    Content and Images Source: Dresner Advisory Services Big Data Analytics Market Study; Copyright 2017 -- Dresner Advisory Services
  • What Are You Doing With Your Data? Recorded: Sep 11 2017 7 mins
    Wayne Eckerson
    In this video, we explore the best way to data about your products and services into a valuable product on its own.

    As Edd Wilder-James once said, “Data products are the reason data scientists are lately treated like rock stars.” Data products operationalize analytical insight. And that’s what monetizing data is all about. The automobile is a good example for the potential of monetizing data. It illustrates that data about a product can be just as valuable -- if not more valuable -- than the product itself for generating revenue and increasing customer loyalty.
  • Accessing Data Using Custom Connectors Recorded: Sep 11 2017 8 mins
    Ryan Haber
    This video explores how applications connect to data sources and what that means to an embedded application.

    Database query languages like Structured Query Language (SQL) and the Open Database Connectivity Protocol (ODBC) have been around a long time. SQL since the early 1970s and ODBC since 1986. And for as long as people have been querying data, reducing the length of time it took to get answers back -- query latency -- has been a problem. As databases have changed and new types emerged, solving the problem has become even more complex. Custom data source connectors are a solution.
  • Big Data Drives Businesses of All Sizes Recorded: Sep 11 2017 4 mins
    Wayne Eckerson
    In this video, you’ll learn why businesses of all sizes are investing so heavily in big data.

    Big data and big data analytics are big business. IDC projects the market to grow to $50 billion by 2019. So what do organizations that invest in big data expect to achieve. Is there still a role for intuition in decision-making? Essentially, businesses pursue three objectives with big data: understanding the past, improving the present, and predicting the future. We’re also increasingly surrounded by data-driven smart systems that are reshaping the way we work and the economy we work in.
  • The Data Monetization Maturity Model Recorded: Sep 11 2017 6 mins
    Wayne Eckerson
    Watch this video to find out where most organizations are right now in terms of data monetization maturity -- and how to move past that.

    A five-stage model describes the path to data monetization maturity. But did you know that although many organizations have invested heavily in data and analytics since the 1990s, most have not moved beyond the first stage of the model -- distributing analytics internally? The next four stages chart the development of analytics from a cost center to a profit center. And each stage has its own requirements.
  • Embedding Analytics with iFrames Recorded: Sep 11 2017 5 mins
    Ryan Haber
    In this video, you’ll find out how iFrames extends the customization of embedded analytics beyond what’s possible with white labeling.

    There are a lot of cases when white labeling isn't sufficient,especially when you want to have data analytics alongside other functionality. A good example would be a customer portal where you have several columns with different widgets. You might have a news feed, a weather map, and other features plus analytics tools.

    One way to do this would be embed an analytics web application into the portal via an iFrame. Of course, like white labeling, iFrames have limits.
  • Embedding Analytics with a Software Development Kit Recorded: Sep 11 2017 5 mins
    Ryan Haber
    This video goes beyond customization via white labeling and iFrames to the use of a software development kit (SDK).

    Using iFrames and white labeling for customization, there's always a tradeoff between security, interconnectivity, and the integrity of the user experience. You have more control and more connectivity with an SDK. You can build a truly custom application without starting from scratch. A robust SDK should include the ability to embed out-of-the-box charts and new charts as well as their accompanying data and metadata. It should also allow you to embed just data that can feed pre-built visualizations and, very important, integrate using REST APIs.
  • Building Your Own Data Platform with REST APIs Recorded: Sep 11 2017 6 mins
    Ryan Haber
    Watch this video to learn, how using REST APIs alongside of an SDK can multiply the power of both.

    An API is to software code what a UI is to users. It helps different bits of software interact with each other. And can help you turn a data application into a data platform. If you have access to them, you can do a lot with REST APIs that weren’t built into a particular SDK. For example, you could accept user inputs and pass them to the platform to be stored. Or acquire data and metadata from the application or from users. This can be very powerful when building portals.
  • Embedded Applications: Administration and Automation Recorded: Sep 11 2017 2 mins
    Olivier Meyer, Zoomdata
    Watch this video to learn how administration and automation via REST APIs makes life easier when a SaaS company or large enterprise is embedding a BI platform for use by tens or hundreds of thousands of users.

    In that scenario, you want to look for ways to automate the provisioning of new users and groups. So, it's important for a BI platform to offer administrative APIs that can be scripted from your application. These are usually REST APIs, and they really help ease the administrative load when users want to sign up for the embedded service from the parent application.
  • Embedded Applications are Like Icebergs Recorded: Sep 11 2017 2 mins
    Olivier Meyer, Zoomdata
    This video recaps why embedded applications are like icebergs -- a lot happens under the surface.

    Visualizations are what we see from an embedded application. That’s the part of the iceberg that’s above water. But under the hood, you have to make sure that the embedded BI platform will work with the parent application's platform and its development environment. You have to be able to deploy it on the same kind of infrastructure. And it has to work with the parent application’s security model.
  • Embedding Analytics By White Labeling Recorded: Sep 11 2017 4 mins
    Ryan Haber
    Watch this video to learn the uses and limits of customizing embedded analytics through white labeling.

    When a third-party software application is integral to the way a business delivers products or services, many organizations want that software to look like its home grown. White labeling is a way to do that relatively simply; and it’s sufficient in many situations. A lot of cosmetic fine tuning can be done with logos, color palettes, fonts, icons, and background images. In combination, these changes can make an embedded analytics blend well with its parent application.
  • Security for Embedded Applications: Authorization Recorded: Sep 11 2017 3 mins
    Olivier Meyer, Zoomdata
    In this video, we cover the second “A”: authorization, which refers to defining and enforcing privileges and permissions for a user.

    There are two common methods for authorizing users: role-based access control (RBAC) and the access control list (ACL). In the first, a user is defined as a member of group -- say finance administration -- and the group as a whole is assigned permissions. Another group in finance -- finance accounts payable -- could be assigned a different level of permissions. ACLs provide a finer-grained level of control. For embedding purposes, users, groups, and roles should be defined by the parent application.
  • Security for Embedded Applications: Authorization for SaaS Companies Recorded: Sep 11 2017 3 mins
    Olivier Meyer, Zoomdata
    Watch this video to learn how the two models of SaaS multi-tenancy affect the deployment of embedded platforms.

    Multi-tenancy models typically fall within one of two approaches: isolated and shared. The isolated model is arguably safer, but the shared model is more efficient for the SaaS company. An embedded analytics platform needs to support both. Moreover, to support the co-mingled or shared model, the embedded platform needs to support row-level security. Row-level security ensures that tenants only see the data relevant to that customer. Row-level security can be implemented via the embedded platform or it can be enforced by the data source.
  • Security for Embedded Applications: Auditing Recorded: Sep 11 2017 2 mins
    Olivier Meyer, Zoomdata
    This video explains the importance of auditing to the security environment for embedded applications.

    Especially in highly regulated industries like healthcare and financial services, it’s not enough to correctly configure authentication and authorization. You have to be able to prove it that you’ve done it. External regulators and internal auditors will want to review a record -- an audit trail -- of user activities within the application. To provide that, the parent application logs user activities. The embedded application should support centralized logging so it can use the parent application’s logging environment.
  • Embedding is More than Showing Charts in an Application Recorded: Sep 11 2017 2 mins
    Olivier Meyer, Zoomdata
    Watch this video to learn why there’s a lot more to embedding applications than what you see -- the charts, graphs, and dashboards.

    Of course, visualizations like charts or dashboards are part of it. But for a platform to be embeddable, it needs to align with the parent application’s architecture and development technology. It needs to be compatible with that application’s infrastructure. And, it must integrate with its security model. In addition, SaaS or cloud-native companies also put special demands on the embedding platform, including the need to support automated user provisioning. Supporting multi-tenancy also means isolating metadata and providing row-level security.
  • Security for Embedded Applications: Authentication Recorded: Sep 11 2017 3 mins
    Olivier Meyer, Zoomdata
    In this video, you’ll find out about the three “A”s of security and get an overview of the first “A” -- authentication.

    Authentication confirms that users are who they say they are. But in embedding, the embedded application has to support the authentication standard of the parent application. And there are multiple standards. The most common is form-based, which prompts the user for a username and password. So users don’t have to login more than once, embedding platforms should also support single sign-on, for which there are also multiple standards including SAML, Kerberos, and the X.509 client certificate.
  • What Are The Big Data Access Methods for Spark and Hive? Recorded: Sep 2 2017 3 mins
    Howard Dresner, President, Dresner Advisory Services
    Check out this video for insight into big data access methods.

    Spark SQL, Hive, and Hive QL offer different approaches to accessing data stored in Hadoop. The Hive alternatives are preferred by those accustomed to typical query languages. Amazon Redshift and Google BigQuery also have adherents in this corner of the big data space. Organization’s show data access preferences based on size and industry. Tech and financial services often adopt Spark SQL, but size plays into this dynamic. Smaller organizations tend to lean more towards Spark SQL, while larger organizations lean towards Hive and Hive QL even though they also show use of Spark SQL.

    Content and Images Source: Dresner Advisory Services Big Data Analytics Market Study; Copyright 2017 -- Dresner Advisory Services
  • Big Data Search: The Importance of Elasticsearch, Apache Solr, & Cloudera Search Recorded: Sep 2 2017 2 mins
    Howard Dresner, President, Dresner Advisory Services
    This video looks at the role of search in big data.

    As text analytics and natural language processing gain importance in business intelligence efforts, big data search will gain more of the spotlight. Elasticsearch, Apache Solr, and Cloudera Search are the main players right now. Cloudera Search is Apache Solr implemented in the Hadoop environment. Sentiment analysis of social media data from sources like Facebook and Twitter is one use case where big data search will play an important role.

    Content and Images Source: Dresner Advisory Services Big Data Analytics Market Study; Copyright 2017 -- Dresner Advisory Services
Changing the way people see and interact with data
Zoomdata develops the world’s fastest visual analytics solution for big data. Using patented Data SharpeningTM and micro-query technologies, Zoomdata empowers business users to visually consume data in seconds, even across billions of rows of data. Zoomdata Fusion enables interactive analysis across disparate data sources, bridging modern and legacy data architectures, blending real-time streams and historical data, and unifying enterprise data with data in the cloud. Delivered in a micro-services architecture for elastic scalability, Zoomdata runs on premises, in the cloud or embedded in an application.

Subscribe to this channel to learn best practices and emerging trends about data data visual analytics.

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