Simplifying Data Warehouse Optimization

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

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

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

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Real-time Risk Assessment Engine POC

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.

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IoT Predictive Maintenance POC

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.

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Recommendation Engine POC

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.

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

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

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

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

The Evolution of ETL and Continuous Integration

In the beginning of ETL…. When I started my IT career over 15 years ago I was nothing more than a “Fresh-out” with a college degree and an interest in computers and programming. At that time, I knew the theories behind the Software Development Life Cycle (SDLC) and had put it to some practice in a classroom setting but, I was still left questioning how it relates to the big, bad corporate world. And by the w READ ARTICLE