Tag: use case

Feed: Featured Blog Posts - Data Science Central. Author: Vimal N Suba. Depending on what news headline you have read, you may have perceived an Artificial Intelligence (AI) system as either an Alexa or Siri assistant that understands all your commands, a deep learning system that can recognize dog or a cat from image, a system that recommends personalized medicine, or an intelligent, overpowering machine that can overtake all human tasks and render humans useless. Few of these definitions can be termed as visionary, few fear mongering and rest of them being evolutionary. Last month, I was at Artificial Intelligence (AI) Summit 2018 in San ... Read More

Feed: Planet PostgreSQL. It would be rather nice if the exact SQL definition (well almost, see below) supplied with the “create view” statement could be held in the PostgreSQL System Catalog, rather than the re-written optimised version that is currently held. This has come up before, see: http://www.postgresql-archive.org/idea-storing-view-source-in-system-catalogs-td1987401.htmlhttp://www.postgresql-archive.org/Preserving-the-source-code-of-views-td5775163.html Back then this idea got stomped on for good reasons but I am not suggesting quite the same. We would prefer PostgreSQL to store view definitions in the system catalog *AFTER* column expansion (unlike in the discussion above) but *BEFORE* internal parser rewrites. So, currently for example: CREATE TABLE bar ( x ... Read More

Feed: Neo4j Graph Database Platform. Author: Jocelyn Hoppa. By Janos Szendi-Varga, Senior Consultant at GraphAware | October 18, 2018Reading time: 5 minutes Do you think there is no space for a graph database in your company? Or it would be a huge effort to integrate a graph database into your product? I have to tell you: You can use a graph database like Neo4j without touching your product, and you can use it for managing your company’s knowledge as well as to improve your software development process. So, even if your business problem is not inherently graphy (hard to believe ... Read More

Feed: AWS Compute Blog. Author: tiffany jernigan (@tiffanyfayj). → Part 2: Amazon EFS Post by: Jeremy Cowan, Ronnie Eichler, and Tiffany Jernigan Introduction Containers are emerging as the default compute primitive for building cloud-native applications.  They facilitate the adoption of continuous delivery, and help increase infrastructure use. However, deploying stateful application as containers has been challenging because containers have short life-spans, get re-deployed frequently, are scaled up and down dynamically, and often share the same host with other containers. All of these factors make it challenging for you to appropriately align the lifecycles of storage volumes and containers. Before Docker ... Read More

Feed: Neo4j Graph Database Platform. Author: Jocelyn Hoppa. We use Neo4j to design our next-generation Power chips, said Chuck Calio, Offering Manager IBM PowerAI at IBM.The future of graph technology is in AI, but it’s not the only use case. From designing hardware (naturally a graph) to dynamic pricing, IBM’s Chuck Calio sees use cases for graphs as myriad and the demand from customers strong.In this week’s five-minute interview (conducted at GraphTour San Francisco) we discuss how IBM designs next generation chips using Neo4j – and the way those chips accelerate graph algorithms. Tell us about how you use Neo4j ... Read More

Feed: R-bloggers. Author: Sean Lopp. We’re excited to announce the general availability of our newest RStudio professional product, RStudio Package Manager. RStudio Package Manager helps your team, department, or entire organization centralize and organize R packages. Get started with the 45 day evaluation today! With more than 13,000 packages in the R ecosystem, managing the packages you and your teams need can be challenging. R users naturally want the latest, but everyone benefits from reproducibility, stability, and security in production code. RStudio Package Manager is an on-premises server product that allows R users and IT to work together to create ... Read More

Feed: AWS Big Data Blog. Amazon QuickSight recently launched table calculations, which enable you to perform complex calculations on your data to derive meaningful insights. In this blog post, we go through examples of applying these calculations to a sample sales data set so that you can start using these for your own needs.You can find the sample data set used here. What are table calculations? By using a table calculation in Amazon QuickSight, you can derive metrics such as period-over-period trends. You can also create calculations within a specified window to compute metrics within that window, or benchmark against ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Saumitra Buragohain. What is a Data Science and Engineering Platform Apache Spark is one of our most popular workloads- both on-premises and cloud. As we recently announced HDP 3.0.0  (followed by a hardened HDP 3.0.1), we want to introduce the Data Science and Engineering Platform powered by Apache Spark. As noted in the marketecture above, our Data Science & Engineering Platform is powered by Apache Spark with Apache Zeppelin notebooks to enable Batch, Machine Learning, and Streaming use cases, by personas such as Data Engineers, Data Scientists and Business Analysts. We recently introduced Apache ... Read More

Feed: Microsoft Azure Blog. Author: Zeyad Rajabi. We’ve been very grateful for the feedback you’ve given us since we first introduced Azure Blockchain Workbench in public preview a few months ago. Your feedback continues to be an essential and impactful part of our work. For this release, we focused on making Workbench more accessible for everyone. Accessibility is a key pillar in our vision of empowering every person and every organization to achieve more. We are excited to share some of the improvements we’ve made with accessibility in mind. To use 1.4, you can either deploy a new instance of ... Read More

Feed: Planet PostgreSQL. After a recent chat with a DBA, making his first steps towards migrating a big database away from Oracle to Postgres, I thought there must be just too few articles on PostgreSQL features and “hacks”, aiding in achieving scalability to squeeze the last out of the hardware to safely accomodate some decent multi-terabyte size databases. Especially as there are quite a few options out there, I was very surprised that there was so much fear that Postgres is somehow very limited in the scaling matters. Well maybe it was indeed once so (I started with Postgres in ... Read More