We live in a digital age which is increasingly being driven by algorithms and data. All of us, whether at home or work, increasingly relate to one another via data. It’s a systemic restructuring of society, our economy and institutions the like of which we haven’t seen since the industrial revolution. In the business world we commonly refer to it as digital transformation. In this algorithmic world, data governance is becoming a major challenge.

For me, the most exciting thing about Pipeline Designer is the way that it makes working with streaming data easy. Traditionally this has required a completely different way of thinking if you have come from a "batch" world. So when Pipeline Designer was released, the first thing I wanted to do was to find a good streaming data source and do something fun and interesting with the data. Twitter was my first choice of streaming data.

Many SAP customers have been running SAP on premise for decades and have struggled to harness the full potential of their business processes data running inside of SAP along with other enterprise and external data to gain augmented insight and become more agile in this digital era where everything keeps on moving at an exponential pace with no sign of slowing down.

Before going to the world of integration, machine learning, etc., I would like to discuss with all of you about a scenario many of you might experience when you live in a mega city. I lived in the London suburbs for almost 2 years (and it's a city quite close to my heart too), so let me use London as this story's background. When I moved to London, one question which came to my mind was whether I should buy a car or not. The public transport system in London is quite dense and amazing (Oh!!!

I have had the privilege of playing with and following the progress of Pipeline Designer for a while now. I am really excited about this new tool. If you haven’t seen it yet, then don’t delay and get your free trial now…..actually, maybe read this blog first ;-) Pipeline Designer is an incredibly intuitive, web-based, batch and stream processing integration tool.

The main purpose of machine learning is to perform learning tasks on unseen data sets, having previously built up experience using training and testing data. Often those tasks can include looking for patterns and relationships between variables within the data.

We all know that data is important and that becoming a data-driven enterprise is critical to future enterprise success. But recent events threw into sharp relief just how critical data is to business. Google announced its intention to buy Looker for $2.6 billion dollars. Several days later, Salesforce announced that it would be purchasing Tableau for $15 billion dollars. What can we make of these acquisitions?

Data integration is one of the hardest things developers have to do, but the talented members of the Talend User Group have lots of ideas on how to make it easier. Stitch, now part of Talend, recently hosted the inaugural meeting of the Philadelphia Talend User Group in their office. It was a great time characterized by interesting presentations, tons of food, and a chance to meet and talk with peers who use Talend products.

Talend Data Catalog provides intelligent data discovery that delivers a single source of trusted data into a centralized data catalog. Talend Data Catalog provides the capability for doing impact analysis and/or tracing lineage by harvesting Talend data integration Jobs. For example, you can find the use of a specific attribute or column from the source to the destination of the data flow within the scope of a Talend data integration Job.

Talend Cloud enables the use of several prebuilt connectors and components for different services running on cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. This article explores a use case of querying an Amazon Redshift table as part of a Talend Job developed using Talend Studio and executed as a task on Talend Cloud.