Talend provides a family of Machine Learning components which are available in the Palette of the Talend Studio if you have subscribed to any Talend Platform product with Big Data or Talend Data Fabric. These components provide a whole bunch of tools and technologies to help integrate Machine Learning concepts for your use cases. These out of the box components can perform various Machine Learning techniques such as Classification, Clustering, Recommendation and Regression.

Outra is a UK predictive data science business that helps companies increase the power and precision of data through a modern, science-led approach which delivers actionable insight at speed. Many of Outra’s clients have either limited or no data science capabilities in house. Outra matches its proprietary property data with client’s customer data and any relevant third-party data.

Monitor the number of prospects contacted daily, the number of replies your sales emails receive, how many prospects are engaging with your sales team, and more.

Should you pay for placement at the top of search results or work long and hard to get there organically? We asked 58 marketers; here’s what they recommend.

Continuous Integration (CI) allows you to continuously integrate code into a single shared and easy to access repository. Continuous Delivery (CD) allows you to take the code stored in the repository and continuously deliver it to production. CI/CD creates a fast and effective process of getting your product to market before your competition as well as releasing new features and bug fixes to keep your current customers happy.

The launch of Signals has been a complete game-changer for us this year. Yellowfin is doing something completely unique in the marketplace and we’re winning some great deals because of it. Signals is an automated data discovery product that delivers alerts to users about critical changes in their business. It’s not about dashboards - this is a completely different way of consuming analytics.

The flow of data can be perilous. Any number of problems can develop during the transport of data from one system to another. Data flows can hit bottlenecks resulting in latency; it can become corrupted, or datasets may conflict or have duplication. The more complex the environment and intricate the requirements, the more the potential for these problems increases. Volume also increases the potential for problems. Transporting data between systems often requires several steps.

Today I’m going to do something different and not talk about a specific visualization as I’ve done in my last blog posts, but rather a technique that we use within visualization. How to create multiple views that each show a different part of your dataset?

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.

A household name in global media analytics – let’s call them MTI – is using Unravel to support their data operations (DataOps) on Amazon EMR to establish and protect their internal service level agreements (SLAs) and get the most out of their Spark applications and pipelines. Amazon EMR was an easy choice for MTI as the platform to run all their analytics. To start with, getting up and running is simple. There is nothing to install, no configuration required etc.