Systems | Development | Analytics | API | Testing

Machine Learning Sandbox - Recommendation Engine

Talend’s Big Data and Machine Learning Sandbox is a virtual environment that utilizes Docker containers to combine the Talend Real-time Big Data Platform with some sample scenarios that are pre-built and ready-to-run. This example uses Talend's machine learning capabilities to implement a personalized recommendation model based on user input.

Machine Learning Sandbox - Data Warehouse

Talend’s Big Data and Machine Learning Sandbox is a virtual environment that utilizes Docker containers to combine the Talend Real-time Big Data Platform with some sample scenarios that are pre-built and ready-to-run. This example demonstrates a Data Warehouse Optimization approach that utilizes the power of Spark to perform analytics of a large dataset before loading it to the Data Warehouse.

Continuous Integration Best Practices - Part 1

In this blog, I want to highlight some of the best practices that I’ve come across as I've implemented continuous integration with Talend. For those of you who are new to CI/CD please go through the part 1 and part 2 of my previous blogs on ‘Continuous Integration and workflows with Talend and Jenkins’. This blog would also introduce you to some basic guidance on how to implement and maintain a CI/CD system. These recommendations will help in improving the effectiveness of CI/CD.

What Does It Mean To Be An Extension?

Throughout 2018, we have evolved our strategy and capabilities around extensions. Qlik's open platform has always allowed developers to build new functionality that takes advantage of our engine and extends the capabilities of our analytics products. Historically, extensions were typically created by third parties and largely unsupported. This is changing.

Introduction to the Agile Data Lake

Let’s be honest, the ‘Data Lake’ is one of the latest buzz-words everyone is talking about. Like many buzzwords, few really know how to explain what it is, what it is supposed to do, and/or how to design and build one. As pervasive as they appear to be, you may be surprised to learn that Gartner predicts that only 15% of Data Lake projects make it into production. Forrester predicts that 33% of Enterprises will take their attempted Data Lake projects off life-support.