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    David Wilmer

      Contributions by David Wilmer

      Have you checked out Talend’s 2019 summer release yet?

      September 12 2019 David Wilmer
      Have you had a chance to take a look at Talend’s summer 2019 product release? Our 2019 release has some exciting features that not only will help improve your productivity but will help you scale data projects across your organization. We are all about helping you do your work faster, and we think you’ll find the new features in this latest product release pretty great. Changes to Pipeline Designer First of all, some big changes are coming to our next generation cloud da
      READ ARTICLE

      Simplifying Data Warehouse Optimization

      November 10 2018 David Wilmer
      When I hear the phrase “Data Warehouse Optimization”, shivers go down my spine.  It sounds like such a complicated undertaking.  After all, data warehouses are big, cumbersome and complex systems that can store terabytes and even petabytes of data that people depend on to make important decisions on the way their business is run.  The thought of any type of tinkering with such an integral part of a modern business w
      READ ARTICLE

      [Step-by-Step] How to Load Salesforce Data Into Snowflake in Minutes

      September 06 2018 David Wilmer
      As cloud technologies move into the mainstream at an unprecedented rate, organizations are augmenting existing, on-premise data centers with cloud technologies and solutions—or even replacing on-premise data storage and applications altogether. But in almost any modern environment, data will be gathered from multiple sources at a variety of physical locations (public cloud, private cloud, or on-premise). Talend Cloud is Talend
      READ ARTICLE

      Data Warehouse Optimization POC

      September 06 2018 David Wilmer

      In this example we explore a data warehouse optimization approach that utilizes the power of Spark to perform analytics of a large dataset.

      WATCH

      Real-time Risk Assessment Engine POC

      September 06 2018 David Wilmer

      Using Web APIs and machine learning, this job will use a decision tree model to determine, at login, whether to display a specific credit offer, or no offer at all.

      WATCH

      IoT Predictive Maintenance POC

      September 05 2018 David Wilmer

      This example shows how a large company with over 50,000 vending machines can use the power of IoT (Internet of Things) and Machine Learning to determine an individual machine’s likelihood to break down.

      WATCH

      Recommendation Engine POC

      September 05 2018 David Wilmer

      This example provides users with movie recommendations from our Talend Movie Database website. Using Talend’s machine learning capabilities we can recommend movies based on the individual visitor’s ratings.

      WATCH

      Using Talend and MapR to Create a Real-time Recommendation Model

      June 01 2017 David Wilmer
      Talend was recently recognized as a certified partner on the MapR Converged Data Platform.  This is exciting news not only for Talend and MapR, but also for current and future customers who are looking at Talend and MapR as the solution to their big data challenges.  Today we are going to look at how you can implement a real-time recommendation model usi
      READ ARTICLE

      Setting Up an Apache Spark Powered Recommendation Engine

      November 01 2016 David Wilmer
      One of the easiest ways for retailers to increase their customers’ average shopping cart size is to suggest items to prospective buyers at the point of sale.  We see every day in the physical world when we go to the grocery store or hardware store, the packs of gum, beef jerky, ba
      READ ARTICLE

      Applying Big Data Analytics to Clickstream Data

      October 20 2016 David Wilmer
      If you are a retailer, how well do you know your products and how well do you know your customers?  You may know which products are most popular based on their purchase history because you keep records of those transactions.  But do you know which of your products are the most and least viewed?  Do you know what is driving traffic to certain products and what type of customers are most interested in those products?  Are you able to provide intelligent and meaningful product recommendatio
      READ ARTICLE
      12>>

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