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?

I often get asked why Yellowfin is always the first to market with new products - it’s because of our innovation culture. Our innovation is something that we’ve even been recognized for by BARC and Gartner. Here are three things that we do to create a culture of innovation that enables us to live and breathe new ideas.

Earlier this year, Unravel released the results of a survey that looked at how organizations are using modern data apps and general trends in big data. There were many interesting findings, but I was most struck by what the survey revealed about security. First, respondents indicated that they get the most value from big data when leveraging it for use in security applications. Fraud detection was listed as the single most effective use case for big data, while cybersecurity intelligence was third.

I clip in and drive down on the pedals with all of my energy, legs turning and lungs burning as my heart rate soars during this cycling test. I continue to push my limits during this eight-minute, all-out effort. Why would anyone put themselves through such an exercise? The answer is testing. It is a great method to determine the current baseline, establish targeted areas for development and set milestone events.

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.

There's an incredible amount of hype about AI today. Consumer use cases like self-driving cars and robotics have captured people’s imaginations and shown them that they can make a huge leap with innovation rather than an incremental change. That shift in thinking is exciting, but there’s also a lot of FOMO around AI that means businesses are just trying to tick a box rather than thinking more deeply about how they can use automation to improve their business.

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.

There's been a huge amount of investment in the BI space recently. A recent article by Dave Kellogg talked about the hype in valuation and capital efficiency of many BI companies. It’s an important topic that people purchasing analytics platforms should think about because it says a lot about what priorities a business has.

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.