Tag: Apache

Feed: SQL Server Customer Advisory Team. Author: Arvind Shyamsundar. Team members from Microsoft AzureCAT (a.k.a. DataCAT / SQLCAT) will be presenting a full-day pre-conference on “Building A Scalable Data Architecture In Azure” at the upcoming DPS 2018 conference in Bengaluru, India. We are very excited to be part of the 4th edition of Asia’s largest Microsoft data-focused conference, with over 1000 attendees expected overall. The motivation for this full-day pre-conference training is to share with attendees typical scenarios and considerations in architecting and implementing solutions on Microsoft Azure. Whether you are migrating an existing on-premises data pipeline, or implementing a ... Read More

Feed: Microsoft Azure Blog. Author: Rob Caron. Highlights New Azure innovation advances customer success for the cloud- and AI-powered future - In preparation for Microsoft Inspire in Las Vegas this week (where partners and Microsoft meet to connect, learn, and collaborate to accelerate the digital transformation and success of our customers), Jason Zander (Executive Vice President, Microsoft Azure) highlighted new offers and technologies that will help organizations navigate their own digital transformation and build the critical foundations for cloud and AI. He showcases momentum from businesses and partners who are investing in intelligent tech from the cloud to the edge ... Read More

Feed: Big Data Made Simple - One source. Many perspectives.. Author: Baiju NT. The most common and traditional data processing applications are becoming insufficient in handling a great volume of data sets so prominent in corporate computing today. Therefore, it is a little difficult to find a platform which can help the companies stay competitive in this Big Data race. Apache Spark is one such open source tool which is developed for Big Data processing. It is undoubtedly one of the most popular and highly used data processing software. Advantages of using Spark Even processing complex and highly difficult analytics ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Tetiana Boichenko. Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let’s see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. Definitions We can define machine learning (ML) as a subset of data science that uses ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Igor Bobriakov. Apache Spark is a powerful open-source processing engine built around speed, ease of use, and sophisticated analytics. It has originally been developed at UC Berkeley in 2009, while Databricks was founded later by the creators of Spark in 2013. The Spark engine runs in a variety of environments, from cloud services to Hadoop or Mesos clusters. It is used to perform ETL, interactive queries (SQL), advanced analytics (e.g., machine learning) and streaming over large datasets in a wide range of data stores (e.g., HDFS, Cassandra, HBase, S3). Spark supports ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Kateryna Lytvynova. The best trained soldiers can’t fulfill their mission empty-handed. Data scientists have their own weapons — machine learning (ML) software. There is already a cornucopia of articles listing reliable machine learning tools with in-depth descriptions of their functionality. Our goal, however, was to get the feedback of industry experts. And that’s why we interviewed data science practitioners — gurus, really —regarding the useful tools they choose for theirprojects. The specialists we contacted have various fields of expertise and are working in such companies as Facebook and Samsung. Some of them represent AI startups ... Read More

Feed: Hortonworks Blog – Hortonworks. Author: Saumitra Buragohain. We’re excited to announce the long-awaited release of Hortonworks Data Platform 3.0.0. HDP 3.0 is faster, smarter, hybrid, bigger, trusted and real-time database. We encourage you to read our blog that announced HDP 3.0. You can also view our keynote presentation from DataWorks Summit, where we demoed an autonomous car (1/10 scale), trained with HDP 3.0 technologies. We also want to thank  our current and prospective customers and partners for signing up for the HDP 3.0 Early Access and we are encouraged by the huge interest- now, you can use the HDP ... Read More

Feed: AWS Big Data Blog. Exploratory data analysis of genomic datasets using ADAM and Mango with Apache Spark on Amazon EMRAs the cost of genomic sequencing has rapidly decreased, the amount of publicly available genomic data has soared over the past couple of years. New cohorts and studies have produced massive datasets consisting of over 100,000 individuals. Simultaneously, these datasets have been processed to extract genetic variation across populations, producing mass amounts of variation data for each cohort. In this era of big data, tools like Apache Spark have provided a user-friendly platform for batch processing of large datasets. However, ... Read More

Feed: Planet MySQL; Author: HowToForge; Nextcloud is a free (Open Source) Dropbox-like software, a fork of the ownCloud project. Nextcloud is written in PHP and JavaScript, it supports many database systems such as MySQL/MariaDB, PostgreSQL, Oracle Database, and SQLite. In order to keep your files synchronized between Desktop and your own server, Nextcloud provides applications for Windows, Linux, and Mac desktops and a mobile app for Android and iOS. Nextcloud is not just a Dropbox clone, it provides additional features like Calendar, Contacts, Schedule tasks, and streaming media with Ampache etc. In this tutorial, we will show you how to ... Read More

Feed: Actian. Author: Nick Vancas. The usefulness of an analytic database is closely tied to its ability to ingest, store, and process vast quantities of data. Data is typically ingested from multiple sources such as operational databases, CSV files, and continuous data streams. In most cases, daily data loads are measured in tens or hundreds of millions of rows, so the traditional SQL INSERT mechanism is not well suited for these data volumes.In this blog post we’ll look at Actian Vector, our columnar database ideally suited for analytic applications, and evaluate a variety of CSV data ingestion options that operate ... Read More