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Some companies don’t always get the public credit they deserve for contributing to the open source community software and tools that can enable other developers to move their own projects along quicker and more efficiently. Companies such as Facebook, Google and others are famous for giving back to the software community valuable code, tools and hardware designs that they can use to solve thorny problems. But others, such as Yahoo and LinkedIn, aren’t as well known for what they are contributing. Besides the original development of Hadoop a dozen years ago, Yahoo has been an active participant in projects such as those listed on the company's Github page [1] , in various Apache Software Foundation Communities, and in niche communities, such as CPAN and others.   Last month it released a container-access control [2] to the community, a valuable tool in view of the current popularity of new-gen container/micro-services-based systems. Mountain View, Calif

After just five short years in the spotlight, tech experts are calling for the death of Hadoop [1] . For many, it seems like Hadoop is already going the way of legacy applications — it’s not going anytime soon, but many business leaders are seeing it as more of a necessary evil than the miracle it once was. The frustration is justified. The Hadoop distributed computing platform has been oversold as a solution for your entire business. But in reality, Hadoop is designed for data scientists who can code in MapReduce — not business analysts who need interactive business intelligence functionality. Sometimes, though, the grass isn’t greener on the other side. If you’re thinking of abandoning Hadoop for BI, you may want to reconsider. It’s True: Hadoop Isn’t Meant for Business Intelligence Despite what you may have thought when you dove headfirst into Hadoop, it isn’t built for visualization software that promises near real-time business intelligence. Consider

Recently Raj Verma (President & COO of Hortonworks) spoke to Jim McHugh from Nvidia at the DataWorks Summit keynote in San Jose ( video ). Jim began by talking about how parallel processing that is used in gaming is also essential to Deep Learning*. And the lifeblood of Deep Learning is data. With its insatiable desire for data, Deep Learning is best suited to customers that have lots and lots of data and, naturally, Hortonworks customers come to mind. The paradigm shift comes to how data at scale is the the new way to develop Artificial Intelligence applications – or how it is used to train new models that are the critical component of these applications. [1] What are the applications of Machine Learning? Jim McHugh gave two examples: healthcare and autonomous cars . In healthcare we are seeing models that are better at predicting patient outcomes than health practitioners. For example, Deep Learning models can be better at recognizing images than radiologist

by bigdata [1] 29 June 2017 Analytics , Big Data , Cassandra , Cloud Computing , Cloudera , Couchbase , Hadoop , Hadoop Tutorials , Hive , Impala , Internet of Things , Machine Learning , MapReduce , MongoDB , NoSQL , Predictive Analytics , SAS [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] Tags:  Analytics , Big Data , Big Data Analytics , Cloud Computing , Data Science , Data Warehousing , Hadoop , Hadoop 2.3.0 , Hadoop 2.x , Hadoop Cluster , Hadoop Developers , Hadoop ETL , Hadoop OpenStack , Hadoop Resources , Hadoop system , HBase , HDFS , Hive , machine learning , MapR , MapReduce , MongoDB , MySQL , NoSQL , Predictive Analytics , SQL on Hadoop , What is Hadoop , What is HDFS [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] No Comment [47] Hadoop greatly helps in storing and processing large data sets in a distributed computing environme

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I was working with a client recently and discussing how data is currently managed in their organization. During the course of the conversation, my client said “Too often here, data is an afterthought.” What he meant was that very often, programs, products, or services would be developed by one part of the organization or another, and when the program was ready to launch, only then was the department responsible for managing the data consulted. Or, rather than consulted, told “Here’s what’s happening.” In other words, the entire element of data collection and fulfillment was an afterthought, rather than being part of the discussion from the beginning. So think about how new programs and services are created at your organization. Is the management of the data that the program will create discussed at the beginning of the project, or at the end? Go to the article

REGISTER NOW AND SAVE OVER $400! Join us in Sydney on September 20-21 for DataWorks Summit/Hadoop Summit , the industry’s premier big data community event. Attend this year and learn from your peers and industry experts how open source technologies enable you to leverage all your data, on premise and in the cloud, to drive predictive analytics, distributed deep-learning, and artificial intelligence initiatives. [1] Planning is well underway and we can’t wait to deliver our biggest and best APAC Summit to-date! Here are just a few of the reasons you will want to attend: Over 60 sessions of cutting edge education designed by the community, for the community. From case studies to best practices to tips & tricks, there is something for everyone. [2] Eight dedicated business and technology tracks – hoodies and suits, we’ve got you covered! [3] Solve your big data challenges with the industry’s Who’s Who at the Community Showcase. Upskill at one of our Pre

  June 27, 2017 -- Herndon, VA (PRWEB) June 27, 2017 Search Technologies, the leading IT services firm specializing in the design and implementation of enterprise search [1] and big data applications [2] has recently fulfilled requirements to become a Silver Systems Integration Partner in the Cloudera Connect partner program. Cloudera is the provider of the modern platform for machine learning and advanced analytics built on the latest open source technologies. ?We are extremely pleased to have met requirements to now be recognized a Silver Systems Integrator partner in the Cloudera Connect partner program. And, we are proud to be their go-to partner for search enabled big data projects," said Kamran Khan, CEO of Search Technologies. "Since joining the program in 2013, we've built a strong relationship with Cloudera a

Portland, OR -- ( SBWIRE [1] ) -- 06/26/2017 -- Hadoop Market Report [2] , published by Allied Market Research, forecasts that the global market is expected to garner $84.6 billion by 2021, registering a CAGR of 63.4% during the period 2016-2021.North America accounted for around 52% share of the overall market revenue in 2015, owing to higher rate of adoption in industries such as IT, banking, and government sector. Europe is anticipated to witness the fastest CAGR of 65.7% during 2016 to 2021. For Sample – Click Here!!! [3] The Hadoop ecosystem comprises Hadoop software, hardware, and services. In 2015, the services segment dominated the market, accounting for approx. 49% share owing to growing demand for big data analysis across various organizations. The Hadoop services market is further segmented into consulting, training & outsourcing, integration & deployment, and middleware & support services. The consulting, training & outsourcing segment generated revenu

Get your free audio book: http://hlmp.us/k/b00a376bb2 Bill Karwin has helped thousands of people write better Sql and build stronger relational databases. Go to the article

Get your free audio book: http://hlmp.us/k/b01n8tq46a For many organizations, Hadoop is the first step for dealing with massive amounts of data. The next step? Go to the article

RP Analytics Webinar Series Presents; "Hadoop - Enabler of a Data Driven strategy" Featuring; Abed Ajraou - Analytics Director at First Utility, Data Leader ... Go to the article

JavaEE课程+HADOOP大数据课程黑马云12期\day01_html&css 源码与资料： http://pan.baidu.com/s/1bpu4rtd 教学视频总目录： ... Go to the article

Posted Wednesday, June 21, 2017 by RICHARD HARRIS, Executive Editor [1] The data delivery service is powered by a complex infrastructure originally built on a relational database. Growing data volumes and new requirements mandated a faster and more scalable solution. For many customers, the value of data is directly related to its timeliness so even the slightest delay risks placing them at a competitive disadvantage. To improve the customer experience and the quality of service, IHS Markit, a provider of information and analytics solutions, made the decision last year to migrate to MongoDB for its Data Delivery Service. Testing found that with MongoDB’s non-relational document database, data could be read 250x faster, written 10x faster and storage requirements were reduced by 65%. These massive performance gains mean data gets to customers quicker,

I’ve written many times about managing exceptions and being careful not to design rules around exceptions. As a general rule of thumb, if a given action occurs less than 10% of the time, it’s an exception, and should be treated as such. For example, one of the most common complaints I hear from my clients is that members and customers will create duplicate records. They go to the website, can’t remember their login, and rather than using the “forgot password” functionality, they create a new record. The first question I always ask is “How often does this happen?” Because the reality is, some percentage of customers are always going to ignore the forgot password, or create multiple records in order to “cheat,” or do something else that most customers don’t do. That’s the reality. What we need to ask is, how often does this happen? Because even though these things are remarkably irritating to staff, they are probably rare compared to the number of customers who actually DO remember th

Hadoop Big Data Analytics Report by Material, Application, and Geography – Global Forecast to 2021 is a professional and in-depth research report on the world’s major regional market conditions, focusing on the main regions (North America, Europe and Asia-Pacific) and the main countries (United States, Germany, united Kingdom, Japan, South Korea and China). The report firstly introduced the Hadoop Big Data Analytics basics: definitions, classifications, applications and market overview; product specifications; manufacturing processes; cost structures, raw materials and so on. Then it analyzed the world’s main region market conditions, including the product price, profit, capacity, production, supply, demand and market growth rate and forecast etc. In the end, the report introduced new project SWOT analysis, investment feasibility analysis, and investment return analysis. For sample PDF pages click here at:  https://www.researchnreports.com/request_sample.php?id=77568 [1] Hadoop is re