Systems | Development | Analytics | API | Testing

October 2018

Continuous Integration Best Practices - Part 2

This is the second part of my blog series on CI/CD best practices. For those of you who are new to this blog, please refer to Part 1 of the same series and for those who want to see the first 10 best practices. Also, I want to give a big thank you for all the support and feedback! In my last blog, we saw the first ten best practices when working with Continuous integration. In this blog, I want to touch on some more best practices. So, with that, let’s jump right in!

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.

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.

Talend Fall '18 Enables Enterprises to Deliver Insight-Ready Data at Scale

Redwood City, CA and London - October 16, 2018 - Talend (NASDAQ: TLND), a global leader in cloud data integration solutions, today announced a major update to Talend Data Fabric, the company’s unified data platform for data integration across complex, multi-cloud and on-premises environments.

Introducing Talend Data Catalog: Creating a Single Source of Trust

Talend Fall ’18 is here! We’ve released a big update to the Talend platform this time around including support for APIs, as well as new big data and serverless capabilities. You will see blogs from my colleagues to highlight those major new product and features introductions. On my side, I’ve been working passionately to introduce Talend Data Catalog, which I believe has the potential to change the way data is consumed and managed within our enterprise.

Astrazeneca: Building the Data Platform of the Future

AstraZeneca plc is a global, science-led biopharmaceutical company that is the world’s seventh-largest pharmaceutical business, with operations in more than 100 countries. The company focuses on the discovery, development, and commercialization of prescription medicines, which are used by millions of patients worldwide.

Machine Learning Sandbox - Building the Sandbox and Selecting a Platform

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 video shows you what to expect when you start the sandbox for the first time and how to select a Big Data Platform for evaluation.

Machine Learning Sandbox - IoT Predictive Maintenance

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 video demonstration 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.

Machine Learning Sandbox - Real-Time Risk Assessment

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 video demonstration shows how an online bank is trying to mitigate their exposure and risk by targeting credit offers to only those customers whom are deemed low risk and most likely to accept the credit offer.

Machine Learning Sandbox - Sign Up and Download

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 video shows you how to get signed up and download the Talend Big Data and Machine Learning Sandbox.

Cloudera 2.0: Cloudera and Hortonworks Merge to form a Big Data Super Power

We’ve all dreamed of going to bed one day and waking up the next with superpowers – stronger, faster and even perhaps with the ability to fly. Yesterday that is exactly what happened to Tom Reilly and the people at Cloudera and Hortonworks. On October 2nd they went to bed as two rivals vying for leadership in the big data space. In the morning they woke up as Cloudera 2.0, a $700M firm, with a clear leadership position. “From the edge to AI”…to infinity and beyond!

Best Practices for Building a Cloud Data Lake You Can Trust

Using Talend and Amazon Web Services (AWS), financial institutions are building cloud data lakes to consolidate customer data across hundreds of sources. By validating the quality of that data and correlating data sets with automated processes, you can deliver trusted reporting that meets regulatory requirements and uncover insights for new business.