Sometimes I write a blog because I've learned something new that I'm eager to share. And sometimes I write a blog because we've just done something enlightening in one of my classes or client exercises. But sometimes I write a blog because I don't know anything about a topic, and writing a blog is the best way to force myself to learn what I don't know. The latter is the driving factor behind this blog: What's the Difference between data integration and data engineering? Why is this distinction important?

Recently, I had an enlightening conversation with Gemini Data about their approach to creating what I'm calling "semantic connectors." These connectors will help immensely in making graph ETL work better. In my view, graph ETL, and the ability to master graph queries, algorithms, and analytics, are the key to unlocking the vast potential of graphs.

Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQL Server eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your ETL processes or to any application. You can install and run any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server.

Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQLServer eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your ETL processes or to any application. You can install and run any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server.

Boone will be contributing the insights and experience of her position to the board's role in defining and assisting the strategy of the Energistics Consortium in the coming years. "We are very pleased to welcome Kim Boone to the Energistics board," said Ross Philo, president and CEO of Energistics, "Her unique cross-discipline view of Exploration & Production computing will be of great support to the organization as we accelerate the deployment of the new integrated data exchange standards first made available in 2016." Her biography as well as those of the other board members can be found at www.energistics.org/board-of-directors. About Energistics Energistics is the leading upstream oil and gas industry's data standards body. We are a global, non-profit consortium established over 25 years ago to bring together industry professionals in a neutral and collaborative environment to develop and deploy open data exchange standards and to address oil and gas information sharing challenges.

JERSEY CITY, N.J., June 5, 2018 - Verisk (Nasdaq:VRSK), a leading data analytics provider, today announced a strategic alliance with Omnitracs, LLC, a global pioneer of fleet management solutions to transportation and logistics companies. As the first large-fleet commercial telematics service provider to align with Verisk, Omnitracs will now offer its subscribers the ability to contribute data to the Verisk Data Exchange(TM) and apply analytics to the operational safety of their connected fleets. Through the alliance, fleet owners have the option to transfer driving data collected by their Omnitracs telematics platform, the company's Software-as-a-Service (SaaS) fleet management solution, into the Verisk Data Exchange. By contributing their driving data, Omnitracs subscribers will be able to take advantage of fleet insurance services from participating insurance providers and receive operational safety feedback through focused analytics while keeping fleet managers in complete control of their data. "We're excited to expand our telematics-based insurance services to Omnitracs customers and help the insurance industry address the challenging commercial auto risk segment through innovative tools and solutions," said Saurabh Khemka, senior vice president and general manager, Verisk IoT/Telematics.

Bots are breaking into the most human industry on earth: health care. Just look at Microsoft -- the company recently launched a new division to address the intersection between health care and AI. That's not to say AI will be replacing doctors anytime soon. But the integration of AI into regular health care tasks is upon us. From surgical assistance to patient education to imaging analysis, robots are already part of the process -- and their roles will only increase over time. Of course, not everything is smooth sailing -- AI faces challenges in health care just as it does in other fields.

Multi-sensor data fusion technology plays an important role in real applications. Because of the flexibility and effectiveness in modelling and processing the uncertain information regardless of prior probabilities, Dempster-Shafer evidence theory is widely applied in a variety of fields of information fusion. However, counter-intuitive results may come out when fusing the highly conflicting evidences. In order to deal with this problem, a novel method for multi-sensor data fusion based on a new generalised belief divergence measure of evidences is proposed. Firstly, the reliability weights of evidences are determined by considering the sufficiency and importance of the evidences. After that, on account of the reliability weights of evidences, a new Generalised Belief Jensen-Shannon divergence (GBJS) is designed to measure the discrepancy and conflict degree among multiple evidences, which can be utilised to measure the support degrees of evidences. Afterwards, the support degrees of evidences are used to adjust the bodies of the evidences before using the Dempster's combination rule. Finally, an application in fault diagnosis demonstrates the validity of the proposed method.

An attacker just needs to play ultrasonic sounds through a built-in speaker of a target computer or by using a speaker in its proximity. The principle is simple, the technique leverages specially crafted acoustic signals to cause significant vibrations in the HDDs components that could cause severe damage. Modern HDDs use shock sensors to prevent the head crash, but the team of researchers has demonstrated that sonic and ultrasonic sounds could cause false positives in the shock sensor, causing a drive to park the head in a wrong position. "We created and modeled a new feedback controller that could be deployed as a firmware update to attenuate the intentional acoustic interference. Our sensor fusion method prevents unnecessary head parking by detecting ultrasonic triggering of the shock sensor" reads the paper published by the experts.

ETL stands for Extract, Transform, Load. It has been a traditional way to manage analytics pipelines for decades. With the advent of modern cloud-based data warehouses, such as BigQuery or Redshift, the traditional concept of ETL is changing towards ELT – when you're running transformations right in the data warehouse. Let's see why it's happening, what it means to have ETL vs ELT, and what we can expect in the future. ETL arose to solve a problem of providing businesses with clean and ready-to-analyze data.