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Search results for dbms

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  • Interview: Thanigai Vellore, on Why Big Data vs RDBMS is the Wrong Question

    We discuss success factors with polyglot architectures, Big Data challenges, recommendations for using Big Data technologies, trends, advice, and more.

  • Interview: James Taylor, Salesforce on Apache Phoenix – RDBMS for Big Data

    We discuss the beginning of Phoenix project, decision of making it open source, relational database layer on HBase, and key reasons for the superior performance of Apache Phoenix.

  • Top KDnuggets tweets, Apr 14-20: Modern Methods for Sentiment Analysis; Basics of SQL, RDBMS – must have skills

    Great overview: Modern Methods for Sentiment Analysis #word2vec; Basics of SQL and RDBMS - must have skills for data science; The 7 Most Unusual Applications of Big Data; Extensive, but a little confusing site: Understanding Data Visualization.

  • 3 Key Trends in the DBMS Market

    The top 3 trends in DBMS include market consolidation, moving beyond OLTP, and distributed computing - we examine them in detail.

  • Pivotal HD ODBMS Interview with Scott Yara and Florian Waas

    ODBMS Editor Roberto Zicari talks to leaders of the new Pivotal about their new platform and Pivotal HD - their own Hadoop version.

  • How To Build A Database Using Python">Silver BlogHow To Build A Database Using Python

    Implement your database without handling the SQL using the Flask-SQLAlchemy library.

  • Gold BlogTop Programming Languages and Their Uses">Rewards BlogGold BlogTop Programming Languages and Their Uses

    The landscape of programming languages is rich and expanding, which can make it tricky to focus on just one or another for your career. We highlight some of the most popular languages that are modern, widely used, and come with loads of packages or libraries that will help you be more productive and efficient in your work.

  • Build an Effective Data Analytics Team and Project Ecosystem for Success

    Apply these techniques to create a data analytics program that delivers solutions that delight end-users and meet their needs.

  • MongoDB in the Cloud: Three Solutions for 2021

    An overview of pricing and compatibility for MongoDB Atlas, AWS DocumentDB, Azure Cosmos DB.

  • Feature Store as a Foundation for Machine Learning

    With so many organizations now taking the leap into building production-level machine learning models, many lessons learned are coming to light about the supporting infrastructure. For a variety of important types of use cases, maintaining a centralized feature store is essential for higher ROI and faster delivery to market. In this review, the current feature store landscape is described, and you can learn how to architect one into your MLOps pipeline.

  • Column-Oriented Databases, Explained

    NoSQL Databases have four distinct types. Key-value stores, document-stores, graph databases, and column-oriented databases. In this article, we’ll explore column-oriented databases, also known simply as “NoSQL columns”.

  • Cloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants">Gold BlogCloud Computing, Data Science and ML Trends in 2020–2022: The battle of giants

    Kaggle’s survey of ‘State of Data Science and Machine Learning 2020’ covers a lot of diverse topics. In this post, we are going to look at the popularity of cloud computing platforms and products among the data science and ML professionals participated in the survey.

  • Introduction to Data Engineering">Gold BlogIntroduction to Data Engineering

    The Q&A for the most frequently asked questions about Data Engineering: What does a data engineer do? What is a data pipeline? What is a data warehouse? How is a data engineer different from a data scientist? What skills and programming languages do you need to learn to become a data engineer?

  • AI and Automation meets BI">Silver BlogAI and Automation meets BI

    Organizations use a variety of BI tools to analyze structured data. These tools are used for ad-hoc analysis, and for dashboards and reports that are essential for decision making. In this post, we describe a new set of BI tools that continue this trend.

  • 5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use

    If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.

  • My Data Science Online Learning Journey on Coursera

    Check out the author's informative list of courses and specializations on Coursera taken to get started on their data science and machine learning journey.

  • Powerful CSV processing with kdb+

    This article provides a glimpse into the available tools to work with CSV files and describes how kdb+ and its query language q raise CSV processing to a new level of performance and simplicity.

  • Skills to Build for Data Engineering">Silver BlogSkills to Build for Data Engineering

    This article jumps into the latest skill set observations in the Data Engineering Job Market which could definitely add a boost to your existing career or assist you in starting off your Data Engineering journey.

  • Top 20 ODSC 2020 Global Virtual Conference Sessions

    At ODSC 2020, we are unveiling our first ever 4-day Global Virtual Conference, an online and on-demand version of ODSC. Here are our picks for 20 talks that show how diverse and thorough the ODSC East Global Virtual Conference will be this April 14-17.

  • The Most Useful Machine Learning Tools of 2020

    This articles outlines 5 sets of tools every lazy full-stack data scientist should use.

  • Data Science Influencers and Keynotes Coming to ODSC East 2020

    ODSC is proud to announce its keynote speakers for ODSC East 2020, Apr 13-17 in Boston — ten preeminent researchers and visionaries who will kick off the already expert lineup set to speak at the community-based event for data science practitioners and AI engineers.

  • Platinum BlogEverything a Data Scientist Should Know About Data Management">Silver BlogPlatinum BlogEverything a Data Scientist Should Know About Data Management

    For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.

  • The Last SQL Guide for Data Analysis You’ll Ever Need">Gold BlogThe Last SQL Guide for Data Analysis You’ll Ever Need

    This is it: the last SQL guide for data analysis you'll ever need! OK, maybe it’s actually the first. But it’ll give you a solid head start.

  • Automate your Python Scripts with Task Scheduler: Windows Task Scheduler to Scrape Alternative Data

    In this tutorial, you will learn how to run task scheduler to web scrape data from Lazada (eCommerce) website and dump it into SQLite RDBMS Database.

  • Top Handy SQL Features for Data Scientists">Gold BlogTop Handy SQL Features for Data Scientists

    Whenever we hear "data," the first thing that comes to mind is SQL! SQL comes with easy and quick to learn features to organize and retrieve data, as well as perform actions on it in order to gain useful insights.

  • Can we trust AutoML to go on full autopilot?

    We put an AutoML tool to the test on a real-world problem, and the results are surprising. Even with automatic machine learning, you still need expert data scientists.

  • Is SQL needed to be a data scientist?

    As long as there is ‘data’ in data scientist, Structured Query Language (or see-quel as we call it) will remain an important part of it. In this blog, let us explore data science and its relationship with SQL.

  • Lead Data Scientist [London, UK]

    Seeking a Lead Data Scientists, an early member of the company who will play a key role in building out and defining Humn's data science capability, providing input from key technology choices to hiring decisions.

  • Understanding Cloud Data Services">Gold BlogUnderstanding Cloud Data Services

    Ready to move your systems to a cloud vendor or just learning more about big data services? This overview will help you understand big data system architectures, components, and offerings with an end-to-end taxonomy of what is available from the big three cloud providers.

  • Overview of Different Approaches to Deploying Machine Learning Models in Production

    Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.

  • Mongo DB Basics

    Mongo DB is a document oriented NO SQL database unlike HBASE which has a wide column store. The advantage of Document oriented over relation type is the columns can be changed as an when required for each case as opposed to the same column name for all the rows.

  • Gold Blog7 Steps to Mastering SQL for Data Science — 2019 Edition">Silver BlogGold Blog7 Steps to Mastering SQL for Data Science — 2019 Edition

    Follow these updated 7 steps to go from SQL data science newbie to practitioner in a hurry. We consider only the necessary concepts and skills, and provide quality resources for each.

  • What’s Going to Happen this Year in the Data World

    "If we wish to foresee the future of mathematics, our proper course is to study the history and present condition of the science." Henri Poncairé.

  • Machine Learning and Deep Link Graph Analytics: A Powerful Combination

    We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.

  • 7 “Gotchas” for Data Engineers New to Google BigQuery

    Here are some things that might take some getting used to when new to Google BigQuery, along with mitigation strategies where I’ve found them.

  • On Points Insights: Senior Python Developer with Big Data skills [Remote, US]

    Seeking a Senior Python Developer with Big Data skills (work remotely), to interpret internal or external business issues and recommend best practices, solve complex problems, and take a broad perspective to identify innovative solutions.

  • Data Science Strategy Safari: Aligning Data Science Strategy to Org Strategy

    The title of this post is derived by drawing inspiration from Mintzberg’s seminal work. In this post, I am attempting to take you on a safari through the data science strategy formulation process.

  • Graphs Are The Next Frontier In Data Science">Gold BlogGraphs Are The Next Frontier In Data Science

    GraphConnect 2018, Neo4j’s bi-annual conference, was held in New York City in mid-September. Read about what happened, and why graphs are the next big thing in data science.

  • Charles River Analytics: Full Stack Software Engineer

    Seeking a Full Stack Software Engineer to work with a team of scientists and engineers to enhance our state-of-the-art user-centered enterprise system for members of the U.S. Armed Forces.

  • Modern Graph Query Language – GSQL

    This post introduces the prospect of fulfilling the need for a modern graph query language with GSQL

  • Choosing Between Modern Data Warehouses

    Most of the modern data warehouse solutions are designed to work with raw data. It allows to re-transform data on the fly without a need to re-ingest your data stored in a warehouse.

  • Introducing WSO2 Stream Processor

    WSO2 Stream Processor is an open source, lightweight, Streaming SQL based platform that enables you to do running aggregations, to detect patterns, and to generate alerts on data streams in real-time.

  • CVS: Advisor, Retail Data Strategy Analytics

    Seeking an Advisor, Retail Data Strategy Analytics, to lead requirements gathering, data analysis, end to end quality and user acceptance testing planning and coordination.

  • Event Processing: Three Important Open Problems

    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.

  • To SQL or not To SQL: that is the question!

    This article looks at the emergence of the NoSQL movement and compares it to a traditional relational database.

  • Presto for Data Scientists – SQL on anything

    Presto enables data scientists to run interactive SQL across multiple data sources. This open source engine supports querying anything, anywhere, and at large scale.

  • National Grid: Dev Ops – Operations Engineer / Sr Ops Engineer – Advanced Analytics

    Seeking an Analytics Operations Engineer you will package, optimize, operationalize/productionize cloud based advanced analytical and big data software solutions.

  • The Great Big Data Science Glossary

    To help those new to the field stay on top of industry jargon and terminology, we've put together this glossary of data science terms.

  • Graph Databases Burst into the Mainstream

    What do Amazon, Facebook, Google, IBM, Microsoft and Twitter have in common? They're all adopters of graph databases - a hot technology that continues to evolve.

  • Operational Best Practices for Enterprise Data Science

    Join Team Anaconda for a live webinar, Jan 30, 2pm CT, as we tackle the four main concerns we hear from our customers and show you best practices for managing enterprise data science: scalability, security, integration, and governance.

  • Elasticsearch for Dummies

    In this blog, you’ll get to know the basics of Elasticsearch, its advantages, how to install it and indexing the documents using Elasticsearch.

  • How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?">Gold BlogHow Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science?

    When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.

  • HelloFresh: Big Data Engineer

    As a Big Data Engineer at HelloFresh, you will develop distributed services that processes data in near-time and real-time, with focus on scalability, data quality and integration of machine learning models.

  • A Day in the Life of a Data Scientist">Silver BlogA Day in the Life of a Data Scientist

    Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.

  • Retina.AI: Sr. Data Engineer

    Seeking a Data Engineer to work together with data scientists and client technical contacts to implement scalable and robust data stores and pipelines for analysis.

  • Hellofresh: Big Data Engineer

    As a Big Data Engineer at HelloFresh, you will develop distributed services that processes data in near-time and real-time, with focus on scalability, data quality and integration of machine learning models.

  • eBay: Data Scientist

    Seeking a Data Scientist to be tasked with delivering end-to-end solutions starting from combing petabytes of data to find key insights, evaluating and improving state-of-the-art machine learning solutions, and building reliable and high performance production-quality systems.

  • Are Data Lakes Fake News?">Silver Blog, Sep 2017Are Data Lakes Fake News?

    The quick answer is yes, and the biggest problem is that the term “Data Lakes” has been overloaded by vendors and analysts with different meanings, resulting in an ill-defined and blurry concept.

  • How To Write Better SQL Queries: The Definitive Guide – Part 2

    Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.

  • How To Write Better SQL Queries: The Definitive Guide – Part 1

    Most forget that SQL isn’t just about writing queries, which is just the first step down the road. Ensuring that queries are performant or that they fit the context that you’re working in is a whole other thing. This SQL tutorial will provide you with a small peek at some steps that you can go through to evaluate your query.

  • The Internet of Things: An Introductory Tutorial Series

    In this series of post, the author will be presenting a set of Internet of Things technologies and applications in the form of tutorial in chapter form. Basic concepts are covered with an approachable style, not heaped in technical terms.

  • Celgene: Sr. Manager, Data Lake

    Seeking a Data Lake Manager. The role will work closely with the Data Science Teams, the Business Data Stewards and the team responsible for data ingestions/integration into the platform.

  • DuPont Pioneer: Data Engineer

    Seeking a Data Engineer/Software Developer to design, develop, and implement high quality data solutions and applications for our data science and analytics platform in AWS.

  • 42 Essential Quotes by Data Science Thought Leaders

    42 illuminating quotes you need to read if you’re a data scientist or considering a career in the field – insights from industry experts tackling the tough questions that every data scientist faces.

  • Hadoop is Not Failing, it is the Future of Data

    The author disagrees with a previous KDnuggets post on “Why Hadoop is Failing” and argues that the Darwinian Open Source Ecosystem ensures Hadoop is a robust and mature technology platform .

  • Help Define the Future of Open Source Data Management, Boston, June 26-28

    Postgres Vision, June 26-28, Boston, will be a forum for the sharpest minds in open source as organizations strive to harvest greater strategic value and actionable insight from their data. Use code KDPV17 to save.

  • What Is Data Science, and What Does a Data Scientist Do?">Gold Blog, Mar 2017What Is Data Science, and What Does a Data Scientist Do?

    This article is intended to help define the data scientist role, including typical skills, qualifications, education, experience, and responsibilities. This definition is somewhat loose, and given that the ideal experience and skill set is relatively rare to find in one individual.

  • LeapYear: Lead Data Scientist

    Seeking a Lead Data Scientist, responsible for conceptualizing, developing, testing, and deploying machine learning products on customer data sets. You and our data science team will use LeapYear's platform to create value from the world's most sensitive, siloed data sources.

  • Interviews with Data Scientists: Claudia Perlich

    In this wide-ranging interview, Roberto Zicari talks to a leading Data Scientist Claudia Perlich about what they must know about Machine Learning and evaluation, domain knowledge, data blending, and more.

  • Top KDnuggets tweets, Nov 16-22: Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning

    Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning; What is the Difference Between #DeepLearning and Regular #MachineLearning?; Questions To Ask When Moving #MachineLearning From Practice to Production; How to Choose the Right #Database System

  • How to Make Your Database 200x Faster Without Having to Pay More

    Waiting long for a BI query to execute? I know it’s annoyingly frustrating… It’s a major bottle neck in day-to-day life of a Data Analyst or BI expert. Let’s learn some of the easy to use solutions and a very good explanation of why to use them, along with other advanced technological solutions.

  • Data Science and Big Data, Explained">Silver BlogData Science and Big Data, Explained

    This article is meant to give the non-data scientist a solid overview of the many concepts and terms behind data science and big data. While related terms will be mentioned at a very high level, the reader is encouraged to explore the references and other resources for additional detail.

  • Corios: Junior Data Engineer

    Working with the team to implement data extraction, transformation, and load for clients in regulated industries such as financial services, insurance, retail, and energy sectors. Analyze the current state of the client's business and develop recommendations for improvement.

  • Learn Data Science in 8 (Easy) Steps

    Want to learn data science? Check out these 8 (easy) steps to set out in the right direction!

  • Mind of a Data Scientist – Part 1">Silver BlogMind of a Data Scientist – Part 1

    By now, many people are aware of which technical skills are required for a Data Scientist, but do you know what mindset or thinking is required to be a good data scientist? Let’s read this two parts series by an industry expert.

  • Embedded Analytics: The Future of Business Intelligence

    An overview of the evolution of Business Intelligence, and some insight into where its future lie: embedded analytics.

  • More answers, less theory from Big Guns at Big Data LDN, Nov 3-4

    This new free-to-attend conference seeks to help businesses debunk Big Data myths with real-life case studies, and is expected to attract 3000 attendees over two days.

  • Database Key Terms, Explained

    Interested in a survey of important database concepts and terminology? This post defines 16 essential database key terms concisely and accurately.

  • 7 Steps to Understanding NoSQL Databases

    Are you a newcomer to NoSQL, interested in gaining a real understanding of the technologies and architectures it includes? This post is for you.

  • 7 Steps to Mastering SQL for Data Science

    Follow these 7 steps to go from SQL data science newbie to seasoned practitioner quickly. No nonsense, just the necessities.

  • Top NoSQL Database Engines

    An overview of the top 5 NoSQL database engines in use today, including examples of key-value, column-oriented, graph, and document paradigms.

  • 5 Reasons Machine Learning Applications Need a Better Lambda Architecture

    The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.

  • Hadoop Key Terms, Explained

    An straightforward overview of 16 core Hadoop ecosystem concepts. No Big Picture discussion, just the facts.

  • CRN Top Data Management Technologies Vendors 2016

    The CRN editorial team has released its annual Big Data 100 report for 2016. Check out which companies made the list of Data Management Vendors.

  • Corios: Junior Database Engineer

    Seeking a Junior Database Engineers, responsible for working with the team to implement data extraction, transformation, and load for clients in regulated industries such as financial services, insurance, retail, and energy sectors.

  • Big Data: Content and Technology

    A discussion of using Big Data to provide insight into the big economic questions, and the big expectations that come along.

  • Turn your company into a data science-driven business in 6 steps

    Transforming your business with (big) data analytics and data-driven insights is not a one-time event, but a journey. Here are 6 steps to help enterprises become data-science driven business and enjoy benefits along the way.

  • Corios: Data Engineer

    Seeking a data engineer, responsible for implementing data extraction, transformation, and load for clients in regulated industries such as financial services, insurance, retail, and energy sectors.

  • Embedding Open Cognitive Analytics at the IoT’s Edge

    Cognitive computing is penetrating more aspects of the IoT as algorithms enable edge devices and applications. Understand how unstructured data captured by IoT edge devices with the help of cognitive algorithms distilled into actionable insights.

  • Hadoop and Big Data: The Top 6 Questions Answered

    6 questions surrounding Hadoop and Big Data are posed and answered, including those related to implementation, management, and practical uses. Find out where Hadoop currently sits in the world of Big Data.

  • Tamr 2016 Data Management Predictions

    2016 predictions from Tamr team, which includes Turing Award winner Mike Stonebraker and some of the most forward-thinking experts from the world of Big Data.

  • 8 Myths about Virtualizing Hadoop on vSphere Explained

    This article takes some common misperceptions about virtualizing Hadoop and explains why they are errors in people’s understanding.

  • Geisinger: High Perf Computing Data Svc Admin

    Use Linux to deploy, manage, and maintain tools related to informatics and to assist with management of an HPC cluster. Responsible for managing REDCap, i2b2, and SAS deployments.

  • Analytics for Personal Fitness Devices

    Analytics in health care is yet an undiscovered territory, but due to IoT devices it is estimated to grow to $53 billion in the next three years. Here we explain the current status of industry, its future potential and key drivers.

  • Interview: Stefan Groschupf, Datameer on Why Domain Expertise is More Important than Algorithms

    We discuss large-scale data architectures in 2020, career path, open source involvement, advice, and more.

  • KDnuggets™ News 15:n24, Jul 29: Big Data to Big Profits; Mining Massive Datasets; Data for Humanity

    From Big Data to Big Profits: A Lesson from Google's Nest; Coursera/Stanford "Mining Massive Datasets", free online course; Data for Humanity: A Request for Support; To Code or Not to Code with KNIME.

  • Interview: Brian Kursar, Toyota on What You Need to be Truly Data-Driven

    We discuss Toyota’s Customer 360 Advanced Analytics and Insights platform, Product Quality Analytics system, Predictive Analytics use cases & performance assessment, and challenges in analyzing data from social media.

  • Interview: Thanigai Vellore, on Delivering Contextually Relevant Search Experience

    We discuss the role of Analytics at, the polyglot data architecture at, the use cases for Hadoop, vendor selection, supporting semantic search and experience with Avro.

  • KDnuggets Interview: Amr Awadallah, CTO & Co-founder, Cloudera on the Need for Self-Service Analytics

    We discuss the importance of enabling self-service analytics, partnership with Cask, Big Data vendor selection and competitive landscape.

  • Open Source Enabled Interactive Analytics: An Overview

    Explaining the aspects of creating an interactive data driven dashboard using open source technologies i.e. MongoDB, D3.Js, DC.JS and Node JS.

  • KDnuggets™ News 15:n19, Jun 17: Which Big Data, Data Mining Tools go together? Best Data Science Podcasts

    Which Big Data, Data Mining, and Data Science Tools go together? Best Big Data, Data Science, Data Mining, and Machine Learning podcasts; Not So Fast: Questioning Deep Learning IQ Results; Love, Sex and Predictive Analytics.

  • Top stories for May 31 – Jun 6: Top 20 Python Machine Learning Open Source Projects

    Top 20 Python Machine Learning Open Source Projects; Top 30 Social Network Analysis and Visualization Tools; R vs Python for Data Science; Love, Sex and Predictive Analytics.

  • 150 Most Influential People in Big Data & Hadoop

    A list of 150 Most Influential People on Twitter in Big Data & Hadoop includes Merv Adrian @merv, Alistair Croll @acroll, Ben Lorica @bigdata, Paul Zikopoulos @BigData_paulz, Mathias Herberts @herberts, and Gregory Piatetsky @kdnuggets.

  • In-Memory Computing Summit, San Francisco, June 29-30

    The In-Memory Computing Summit 2015 is the first and only industry-wide event of its kind, where Fast Data meets Big Data. Get KDnuggets discount if you register by may 31.

  • Interview: Michael Stonebraker, greatest living contributor to database technology

    Michael Stonebraker, described as the greatest living contributor to database technology, on how he adjusts to the award and what trends he foresees in database management systems and big data.

  • Big Data Bootcamp, Austin: Day 2 Highlights

    Highlights from the presentations by Big Data and Analytics leaders/consultants on day 2 of Big Data Bootcamp in Austin.

  • Big Data Bootcamp, Austin: Day 1 Highlights

    Highlights from the presentations by Big Data and Analytics leaders/consultants on day 1 of Big Data Bootcamp 2015 in Austin.

  • KDnuggets™ News 15:n12, Apr 22: Predictive Analytics Future? Top LinkedIn Groups; Preventing Overfitting

    New Poll: Future of Predictive Analytics? Top LinkedIn Groups for Analytics, Big Data, Data Mining - "Big Bang" to Now; Preventing Overfitting in Neural Networks; Cloud Machine Learning Wars: Amazon vs IBM Watson vs Microsoft Azure.

  • KDnuggets™ News 15:n11, Apr 15: Big Data Predictive Analytics Gainers & Losers; Awesome Public Datasets

    Awesome Public Datasets on GitHub; Gold Mine or Blind Alley? Functional Programming for Machine Learning; Inside Deep Learning - Convolutional networks; KDnuggets Free Pass to Strata Hadoop World London.

  • Big Data for the Common Good “Collider”, at Frankfurt / Berkeley

    The Frankfurt Big Data Lab and cooperate with the Center for Entrepreneurship & Technology (CET) at UC Berkeley to enable the creation of project proposals for Big Data for the Common Good.

  • Interview: Dave McCrory, Basho on Why Data Gravity Cannot be Ignored in Architecture Design

    We discuss data gravity and its implications, Riak Enterprise 2.0, Riak CS 1.5, competitive landscape, challenges and more.

  • KDnuggets™ News 15:n07, Mar 4: Analytics/Data Science Salaries; Machine Learning Flaws; Strata Highlights

    Analytics, Data Mining, Data Science salary survey by region and role; Strata + Hadoop World 2015 San Jose - Highlights; All Machine Learning Models Have Flaws; Interview: Ted Dunning, MapR on The Real Meaning of Real-Time in Big Data, and more.

  • Top KDnuggets tweets, Feb 23-25: Microsoft is building fast, low-power Deep Learning networks; Lucrative tech careers: Data Scientist, Data Engineer

    5 lucrative tech careers in 2015: Data Scientist ($150K), Data Engineer ($148K); Which SQL on Hadoop? Gartner Poll Still Says "Whatever" But DBMS Providers Gain; 10 Most-Funded #BigData #Startups; DataRPM 8 runs in #Hadoop, uses #MachineLearning to find insights.

  • HomeUnion: Big Data Technical Project Manager

    Big Data TPM plays a key role in delivering all Big Data related product releases, and is familiar with Big Data technologies and Agile methodologies.

  • Megaputer: Data Analysis Consultant

    Create data analysis and reporting solutions for Megaputer customers with the help of PolyAnalyst(tm) platform: experimental, proof-of-concept, implementation, and production projects. Develop successful long-term relationships with customers.

  • How Big Data Pieces, Technology, and Animals fit together

    How Big Data Pieces and animals fit together: MapReduce, HDFS, Apache Spark,, Pregel, Zookeeper, Flume, Hive, Pig, and more, explained by a Quora (and past Facebook) Data Scientist.

  • Interview: Nandu Jayakumar, Yahoo on How Yahoo is Harnessing Big Data

    We discuss the major Big Data uses cases at Yahoo, major challenges, trends in enterprise Big Data implementations, and advantages of using Spark.

  • 16 NoSQL, NewSQL Databases To Watch

    NoSQL and NewSQL databases have become much more important with the proliferation of big, mobile, and networked data, and these sixteen database solutions are some of the biggest up-and-comers.

  • KDnuggets Interview: Paul Zikopoulos, IBM on Big Data Opportunities and Challenges

    We discuss the value of Big Data for SMBs, how Cognitive will impact Big Data, IBM’s distinction from competition, significant trends and more.

  • KDnuggets Interview: Paul Zikopoulos, IBM on Why Big Data needs Polyglots

    We discuss why not to focus on a single technology in Big Data, prevalent myths, what IBM & Twitter partnership means for the world, and current state of data governance.

  • 2015 Predictions – What’s Next for Data Scientists?

    What’s next for data scientists in 2015 - new areas they will focus on - cyber threats to fraud detection - and how the expectations for this profession will change.

  • Zicari: Big Data: A Data-Driven Society

    Roberto Zicari talk at Stanford reviews how Big Data is enabling a data-driven economy, examines 3 Big Data research challenges, and makes a case for Big Data for Social Good.

  • SlamData Open Source Analytics Tool for MongoDB

    SlamData is an open source SQL-based tool designed to make accessing data in MongoDB easy for developers and non-developers alike with the goal of making application intelligence easier.

  • Data Analytics Week for Government, Feb 23-25, Washington, DC

    Download the brochure to see the full line-up of speakers and sessions that help you unravel the key issues in federal data analytics - special KDnuggets discount. Also get free report on Big Data Analytics Market 2013-2023.

  • IRI: Digitial Media Consultant, Analytics

    Join Media Center of Excellence, analyze the impact of digital and other media on CPG shopping and product purchasing, analyze and interpret media and digital data trends/patterns and translate them into meaningful solutions for clients.

  • Megaputer Intelligence: Data Analysis Consultant

    Create data analysis and reporting solutions for Megaputer customers with the help of PolyAnalyst platform: experimental, proof-of-concept, implementation, and production projects. Develop successful long-term relationships with customers.

  • U. Luxembourg: Professor of Computer Science – Big Data

    Complement and strengthen the existing research expertise in the CS and Communication Research Unit, reinforce and expand the activities in the field of Big Data.

  • Apple: Data Scientist

    The Maps Supply Chain Management team is looking for a skilled, experienced, and motivated Data Scientist to help us discover patterns and quality issues with our local data sets.

  • September 2014 Analytics, Big Data, Data Mining Acquisitions and Startups Activity

    September 2014 acquisitions, startups, and company activity in Analytics, Big Data, Data Mining, and Data Science: Hootsuite, eBay - Paypal, MemSQL/In-Q-Tel, Qualtrics, SingTel, Radius, Numerify, DataStax, Nielsen/Indicus,, Teradata / Think Big Analytics.

  • Top KDnuggets tweets, Oct 6-7: Great TED talk by @KnCukier “Big Data is better data”; Top 10 One-Person Startups

    Great TED talk by @KnCukier "Big Data is better data"; Top 10 One-Person Startups; 7 critical elements of effective dashboards and visualizations; Making Sense of Public Data - Wrangling Jeopardy.

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