[unable to retrieve full-text content]The Big Data Toronto conference and expo is back for its 3rd edition on Jun 12-13, 2018 at the Metro Toronto Convention Centre. Big Data focuses on the skills, software and leadership needed to implement data insights & AI Toronto is dedicated to Toronto’s growing AI and deep learning communities.
Original Post: Big Data Toronto Brings Canada to the Centre Stage in Big Data and AI
[unable to retrieve full-text content]Coming soon: Mega-PAW Las Vegas, Spark + AI Summit SF, CogX London, Big Data Toronto Big Data Toronto Conference and Expo, ICDM/MLDM NYC, and many more.
Original Post: Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: June 2018 and Beyond
[unable to retrieve full-text content]This article summarizes the three most important problems to be solved in event processing. The facts in this article are supported by a recent survey and an analysis conducted on the industry trends.
Original Post: Event Processing: Three Important Open Problems
[unable to retrieve full-text content]Watch over 20 hours of YouTube videos on databases and database design, Physical Data Storage, Transaction Management and Database Access, and Data Warehousing, Data Governance and (Big) Data Analytics – all free.
Original Post: YouTube videos on database management, SQL, Datawarehousing, Business Intelligence, OLAP, Big Data, NoSQL databases, data quality, data governance and Analytics – free
[unable to retrieve full-text content]This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.
Original Post: The Executive Guide to Data Science and Machine Learning
[unable to retrieve full-text content]Why should be in Vegas? Network with other professionals, learn at 50+ technical sessions, talk to speakers and top experts, and enjoy the city!
Original Post: Las Vegas Data Innovation Festival, July 17-18
[unable to retrieve full-text content]We discuss 3Vs of Big Data; Infonomics and many aspects of monetizing information including promising analytics methods, successful companies, main challenges; Information marketplaces and why data ownership concept is misguided, and more.
Original Post: Exclusive Interview: Doug Laney on Big Data and Infonomics
[unable to retrieve full-text content]Curious about the future of Big Data and AI? Here’s what the trends have it in 2018 for innovations.
Original Post: Four Big Data Trends for 2018
[unable to retrieve full-text content]DSTI mission is simple: training executive students to become ready-to-go Data Scientists and Big Data Analysts. Check our small private online course programme.
Original Post: Online MSc in Applied Data Science, Big Data – part-time, small, private
The SparklyR package from RStudio provides a high-level interface to Spark from R. This means you can create R objects that point to data frames stored in the Spark cluster and apply some familiar R paradigms (like dplyr) to the data, all the while leveraging Spark’s distributed architecture without having to worry about memory limitations in R. You can also access the distributed machine-learning algorithms included in Spark directly from R functions. If you don’t happen to have a cluster of Spark-enabled machines set up in a nearby well-ventilated closet, you can easily set one up in your favorite cloud service. For Azure, one option is to launch a Spark cluster in HDInsight, which also includes the extensions of Microsoft ML Server. While this service recently had a significant price reduction, it’s still more expensive than running a “vanilla” Spark-and-R…
Original Post: A simple way to set up a SparklyR cluster on Azure