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  • TIBCO Analytics using AWS, Azure or GCP

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    Analytics has been one of the last enterprise systems to move to the cloud, but the situation has changed fundamentally in just the last year or two. There is suddenly a proliferation of cloud-based databases, and of open-source development frameworks like TensorFlow and PyTorch ? all of them now being heavily promoted by the major cloud vendors. This Wiki page is intended to help you find your way around all the available components and specificially TIBCO connectivity to the three main Cloud providers Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure (Azure). Because once you do, it can be easy to build even the most sophisticated machine learning models for everything from image recognition to fraud detection. This wiki pages gives examples and provides links to detailed tips & tricks on how you can make this work.


    In this on-demand webinar hosted by coriniumintelligence.com, members of the TIBCO data Science team review the array of technologies that are available for machine learning in the cloud, and answer the question of what to use and when and how. They then review a set of case studies that show how easy it can be to build applications based on sophisticated AI and machine learning

    On-demand Webinar available - click here 


    TIBCO connectivity with Cloud Providers 

    TIBCO's approach to our products connectivity to data and analytics services available from the three major cloud providers is to be cloud agnostic and multicloud. This will help with transitions to the cloud and reduces consequences if you choose to change cloud provider. We aim to support and anticipate current and future needs and allow you to use the best of these three main providers. As many TIBCO users already have multiple Cloud environments we can bring them together and also combine cloud and on-premise. 

    Examples of services being used frequently for Storage and Analytics purposes on Azure, AWS and GCP are listed in figure 1. Its important that we provide connectivity to these services as you are using these services to reduce costs of running a data center and because you are increasing efficiency by taking advantage of elastic cloud capacity. Using cloud services also improves agility as you can easily spin up new systems and access latest technologies. 


    Figure 1: Examples of cloud services frequently used in Analytics and Machine Learning applications

    Connectivity is achieved through a variety of options depending on the TIBCO products you are using and in which cloud. Some products have native connectors others use drivers such as from CData or JDBC, ODBC, Hive and mySQL. Or connections are created via Spark and solutions such as Hortonworks. These options are described and updated in TIBCO documentation. Your TIBCO technical contacts and partners will be able to help you or feel free to ask a question via Answers here on the TIBCO Community as new options to connect are added regularly as well as new services get launched to connect to!

    Machine Learning Demos

    In this section you will find examples and links to demos of Machine Learning/Artificial Intelligence (AI) and other Cloud services provided by AWS, Azure and GCP used with the TIBCO Analytics platform.

    The blog Image Recognition in TIBCO Spotfire® using Python and AWS by TIBCO Data Scientist Colin Gray describes how to use Spotfire® to produce interactive and highly visual tools for image recognition. This utilises Amazon Web Services (AWS) AI service called Rekognize. We have also recorded this demo in one of our TIBCO Analytics Meetups:

    This second blog by Colin Gray continues on this theme of using TIBCO Spotfire with Cloud AI services but expands its usage to perform Natural Language Processing, and sentiment and text analytics. Its a compare and contrast exercise of using Microsoft?s Azure services to that of Amazon?s. 

    In both examples, the Spotfire Python data function is used to work with the Cloud Machine Learning Libraries from Spotfire.

    Another blog by TIBCO Data Scientists Vinoth Manamala, Eric Hsu and Vaibhav Gedigeri describes in detail how to leverage Google infrastructure and machine learning offerings inside TIBCO Spotfire and TIBCO Data Science products to analyze massive amounts of data and perform NLP/Text Mining faster and more precisely than traditional methods. The use-case is an example of a Customer?s Journey tracked from booking until the customer?s end of the stay. Airbnb Support Analyst will analyze the reviews from NYC and gather insights which could help zero into the concerns from the customer reviews. 

    TIBCO is a proud member of Open Group's Open Subsurface Data Universe? Forum. The OSDU is developing a standard data platform for the oil and gas industry, which will reduce silos and put data at the center of the subsurface community. TIBCO Spotfire simplifies access to these datasets and APIs by providing a ready-to-use dashboard app for viewing and analyzing OSDU data. This Spotfire application was designed to access R1 OSDU data using the AWS APIs. Development of access using Azure is work in progress. See demo video below and more details here and in this blog.

    Next is a recording from AWS Re-invent where Michael O'Connell, Chief Analytics Officer and Steven Hillion, Sr Director Data Science show you how to develop cross-sectional and longitudinal analyses for anomaly detection and yield optimization using deep learning methods, as well as super-fast subsequence signature search on accumulated time-series data and methods for handling very wide data in Apache Spark on Amazon EMR. The trained models are applied to event streams using services such as Amazon Kinesis to identify hot paths to anomaly detection.

    Previously, we have also presented a demo at AWS Re:Invent 2018 on training anomaly detection AI models on big data using the cloud. This demo demonstrated TIBCO's ability to work with various AWS services such as S3, Sagemaker, Redshift and EMR. A video of this demo is available 

    . As you might expect, we recently also built an Azure and TIBCO Analytics Anomaly detection solution.

    And here is a customer example of usage of TIBCO Analytics platform in the Cloud from Leidos. Driven by new requirements continually emerging from private and public agencies across the research spectrum, including NASA, the Center for Disease Control (CDC), and the Centers for Medicare and Medicaid Services (CMS) ? to name a few ? Leidos is at the epicenter of innovation. By creating a web-based platform for data science, they?ve been able to leverage the flexibility of Amazon Web Services to spin up an entire big data infrastructure in an afternoon, ready-made for exploration and analysis.

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