Home
Search results “Big data options”
Big Data Career Options
 
07:09
The Big Data market is growing rapidly. Let's know which big data careers are suitable for you - Hadoop Administrator or Spark Developer?
Views: 757 Whizlabs
How is Big Data stored and processed
 
02:27
How is Big Data stored and processed? (2018) In a traditional approach, usually the data that is being generated out of the organizations, the financial institutions such as banks or stock markets and the hospitals is given as an input to the ETL System. An ETL System, would then Extract this data, and Transform this data, that is, it would convert this data into proper format and finally load this data onto the database. Now the end users can generate reports and perform analytics, by querying this data. But as this data grows, it becomes a very challenging task to manage and process this data, using the traditional approach, this is one of the fundamental drawbacks of using the Traditional Approach. Now let us try to understand some of the major drawbacks of using the Traditional Approach. The 1st drawback is, it an expensive system, as it requires a lot of investment for implementing, or upgrading the system, therefore it is, out of the reach of small and mid-sized companies. The 2nd drawback is, scalability. As the data grows, expanding the system is a challenging task. And the 3rd drawback is, it is time consuming, it takes lot of time to process and extract, valuable information from the data. I hope you might have understood the Traditional Approach of Storing and Processing Big Data and its associated drawbacks. Please don't forget to subscribe to our channel. https://www.youtube.com/user/itskillsindemand Enroll into this course at a deep discounted price: https://goo.gl/HsbEC8 If you like this video, please like and share it. Visit http://www.itskillsindemand.com to access the complete course. Follow Us On Facebook: https://www.facebook.com/itskillsindemand Twitter: https://twitter.com/itskillsdemand Google+: https://plus.google.com/+Itskillsindemand-com YouTube: http://www.youtube.com/user/itskillsindemand
Views: 18446 NetVersity
Big data storage  Options and recommendations
 
40:06
Hadoop clusters are often built around commodity storage, but architects now have a wide selection of Big Data storage choices, including solid-state or spinning disk for clusters and enterprise storage for compatibility layers and connectors. In this webinar, our CTO will review the storage options available to Hadoop architects and provide recommendations for each use case, including an active-active replication option that makes data available across multiple storage systems.
Views: 162 WANdisco
Big Data Storage  Options & Recommendations
 
49:37
Hadoop clusters are often built around commodity storage, but architects now have a wide selection of Big Data storage choices. Hadoop clusters can use a mix of solid-state and spinning disk storage, while Hadoop compatibility layers and connectors can use enterprise storage systems or share storage between Hadoop and legacy applications. In this webinar, 451 Research Director Matt Aslett will review the storage options available to Hadoop architects and provide recommendations for each use case. WANdisco's Randy DeFauw will then present an active-active replication option that makes data available across multiple storage systems.
Views: 150 WANdisco
All you should know about Big Data – Hadoop,Careers,Scope,Modules,Highpaid jobs
 
05:05
Get Recruitment Notifications of all private and govt jobs , Mock test details ,Previous year question papers only at Freshersworld.com – the No.1 jobsite for entry level candidates in India. (To register : http://freshersworld.com?src=Youtube ) – This video is all about “Careers and Training courses for Big Data There are a handful of working definitions for big data, but to me it is most simply put as data sets so large and complex that it becomes difficult or impossible to process them using traditional database management applications. Granted, the term may apply differently to different organizations. For a smaller company, facing hundreds of gigabytes of data for the first time may trigger a need to explore new tools. Other companies that generate tons of transactional data, like UPS, wouldn't flinch with their existing toolsets until they hit tens or hundreds of terabytes. For freshers who wants to start learning big data here are a few tips: 1. Begin with the basics: If you are looking at building a career in big data, you can start with developing the core aptitudes such as curiosity, agility, statistical fluency, research, scientific rigor and skeptical nature. You have to decide which facet of data investigation (data wrangling, management, exploratory analysis, prediction) are you looking at acquiring. The first step to learning big data is to develop basic level of familiarity with programming languages. 2. Experience in programming languages: Begin with developing basic data literacy and an analytic mindset by building knowledge of programming languages such as Java, C++, Pig Latin and HiveQL. Figure out where you want to apply your data analytics skills to describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations. 3. Expertise in Hadoop: Developing knowledge about Hadoop Map-Reduce and Java is essential if you’re looking to be a high-performance data software engineer. 4. What are you looking for? If you are looking for a career switch to big data, begin with developing the skill sets required to work with Hadoop. A well-rounded understanding of Hadoop requires experience in large-scale distributed systems and knowledge of programming languages. 5. Data Analytics Skills: If you want to learn the fundamentals and want to get an indepth understanding of every aspect of Big Data, the resource material provided by Apache’s library is very useful. The Hadoop programme offered by Apache is an open-source software for reliable, scalable, distributed computing. Some of the other programmes offered are HBase Hive, Mahout, Pig ZooKeeper. 6. Online Courses: The big data universe is still very young, to get a well rounded expertise in big data it is important to learn and hone skills related to the subject. Decide on the course based on the skill set you're looking to get. Just by dedicating some time and energy, you can tackle learning big data with these free online classes. Applications of Big Data Big data includes problems that involve such large data sets and solutions that require a complex connecting the dots. You can see such things everywhere. 1. Quora and Facebook use Big data tools to understand more about you and provide you with a feed that you in theory should find it interesting. The fact that the feed is not interesting should show how hard the problem is. 2. Credit card companies analyze millions of transactions to find patterns of fraud. 3. There are similar problems in defense, retail, genomics, pharma, healthcare that requires a solution. The companies offering jobs on Big Data are : Qualcomm India Pvt Ltd, Accenture, Dev Solutions So let us summarize as Big Data is a group of problems and technologies related to the availability of extremely large volumes of data that businesses want to connect and understand. The reason why the sector is hot now is that the data and tools have reached a critical mass. This occurred in parallel with years of education effort that has convinced organizations that they must do something with their data treasure. Freshersworld.com is the No.1 job portal for freshers jobs in India. Check Out website for more Jobs & Careers. http://www.freshersworld.com?src=Youtube Download our app today to manage recruitment when ever and where ever you want : Link :https://play.google.com/store/apps/details?id=com.freshersworld.jobs&hl=en ***Disclaimer: This is just a training video for candidates and recruiters. The name, logo and properties mentioned in the video are proprietary property of the respective organizations. The Preparation tips and tricks are an indicative generalized information. In no way Freshersworld.com, indulges into direct or indirect promotion of the respective Groups or organizations.
Why Big Data Analytics is the Best Career Path? Become a big Data Engineer in 2018
 
09:31
Check out my latest Video: https://www.youtube.com/watch?v=VCrrLdxEXtA Why Big Data Analytics is the Best Career Path? Become a big Data Engineer in 2018. Why Big Data Analytics is the Best Career Path? Hello guys my name is Daniel and you are watching beginner tuts and in this video, we are going to talk about why big data analytics is the best career path? If you’re looking for an amazing career option in information technology and don’t know anything about this industry, then this video can help you become a big data analytics engineer. The Average salary of a big data engineer is 100,000 annually… That’s right guys. 100 grand a year. Now you must be wondering, what the heck is big data?? Big data is a term for data sets that are so large or complex that traditional data processing application software is too weak to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy. The Great White is considered to be the King of the Ocean. This is because the great White is on top of its game. Imagine if you could be on top of the game in the ocean of Big Data! Big Data is everywhere and there is almost an urgent need to collect and preserve whatever data is being generated, for the fear of missing out on something important. There is a huge amount of data floating around. What we do with it is all that matters right now. This is why Big Data Analytics is in the frontiers of IT. Big Data Analytics has become crucial as it aids in improving business, decision makings and providing the biggest edge over the competitors. This applies for organizations as well as professionals in the Analytics domain. For professionals, who are skilled in Big Data Analytics, there is an ocean of opportunities out there. . Why Big Data Analytics is the Best Career move? If you are still not convinced by the fact that Big Data Analytics is one of the hottest skills, here are 5 more reasons for you to see the big picture. 1. Soaring Demand for Analytics Professionals: Jeanne Harris, senior executive at Accenture Institute for High Performance, has stressed the significance of analytics professionals by saying, “…data is useless without the skill to analyze it.” There are more job opportunities in Big Data management and Analytics than there were last year and many IT professionals are prepared to invest time and money for the training. The job trend graph for Big Data Analytics, from Indeed.com, proves that there is a growing trend for it and as a result there is a steady increase in the number of job opportunities. The current demand for qualified data professionals is just the beginning. Srikanth, the Bangalore-based cofounder and CEO of CA headquartered Fractal Analytics states: “In the next few years, the size of the analytics market will evolve to at least one-thirds of the global IT market from the current one-tenths”. Technology professionals who are experienced in Analytics are in high demand as organizations are looking for ways to exploit the power of Big Data. The number of job postings related to Analytics in Indeed and Dice has increased substantially over the last 12 months. Other job sites are showing similar patterns as well. This apparent surge is due to the increased number of organizations implementing Analytics and thereby looking for Analytics professionals. In a study by QuinStreet Inc., it was found that the trend of implementing Big Data Analytics is zooming and is considered to be a high priority among U.S. businesses. A majority of the organizations are in the process of implementing it or actively planning to add this feature within the next two years. 2. Huge Job Opportunities & Meeting the Skill Gap: The demand for Analytics skill is going up steadily but there is a huge deficit on the supply side. This is happening globally and is not restricted to any part of geography. In spite of Big Data Analytics being a ‘Hot’ job, there is still a large number of unfilled jobs across the globe due to shortage of required skill. A McKinsey Global Institute study states that the US will face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using Big Data by 2018. According to Srikanth, co-founder and CEO of Fractal Analytics, there are two types of talent deficits: Data Scientists, who can perform analytics and Analytics Consultant, who can understand and use data. The talent supply for these job title, especially Data Scientists is extremely scarce and the demand is huge. 3. Salary Aspects: Strong demand for Data Analytics skills is boosting the wages for qualified professionals and making Big Data pay big bucks for the right skill. This phenomenon is being seen globally where countries like Australia and the U.K are witnessing this ‘Moolah Marathon’.
Views: 21898 Beginner Tuts
Big Data Analytics Using Python | Python Big Data Tutorial | Python And Big Data | Simplilearn
 
37:03
This Big Data Analytics using Python tutorial will explain what is Data Science, roles and responsibilities of a Data scientist, various applications of Data Science, how Data Science and Big Data work together and how andwhy Data Science if gaining importance. Every sector of business is being transformed by the modern deluge of data. This spells doom for some, and creates massive opportunity for others. Those who thrive in this environment will do so only by quickly converting data into meaningful business insights and competitive advantage. Business analysts and data scientists need to wield agile tools, instead of being enslaved by legacy information architectures. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=BigData-Python-cUw3DsDpQCE&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Data Science, check our Integrated Big Data and Data Science Certification Training Course: https://www.simplilearn.com/integrated-program-in-big-data-and-data-science?utm_campaign=BigData-Python-cUw3DsDpQCE&utm_medium=Tutorials&utm_source=youtube - - - - - - - - What are the course objectives of this Big Data and Data Science Course? Mastering the field of data science begins with understanding and working with the core technology frameworks used for analyzing big data. You’ll learn the developmental and programming frameworks Hadoop and Spark used to process massive amounts of data in a distributed computing environment, and develop expertise in complex data science algorithms and their implementation using , the preferred language for statistical processing. The insights you will glean from the data are presented as consumable reports using data visualization platforms such as Tableau. - - - - - - - - Why should you take this Big Data and Data Science Course? As an expert in this field, you will need to have a working knowledge of the three key pillars in the analytics ecosystem: data management, data science and reporting and visualization. This master’s program will hone your skills in: Big Data: Big data management is the ability to store and process voluminous amounts of unstructured data. Today with the overflow of online information, most companies are adopting big data practices to manage these huge volumes. Hadoop provides the distributed file system for storage, and MapReduce programming in Java is used for the processing. In the analytics lifecycle, it is critical to be able to store and query data to feed the necessary algorithms. Data Science: Data Science algorithms use data to create insights. Once you have an effective way to crunch data, you can use historical data for descriptive and predictive analytics. This is done using a programming language like R or Python, which utilize libraries for statistical analysis. Learning these languages are important to be able to design custom models for analytics, a key expectation for any data scientist. These skills range from basic probability to advanced machine learning. Reporting and Visualization: Once you have insights into data, it is important to make the insights available to the organization using visualization and reporting. This program also includes a number of electives to ensure you get broad knowledge of the entire ecosystem and complementary skills in these fields. The two-year period ensures you have enough time to ramp up, develop skills and apply them in real world scenarios. - - - - - - - - - Who can take this Big Data and Data Science Course? Many roles can benefit from this program and pursue new career opportunities with high salaries, including: 1. Software developers and testers 2. Software architects 3. Analytics professionals 4. Business analysts 5. Data analysts 6. Data management professionals 7. Data warehouse professionals 8. Project managers 9. Mainframe professionals 10. Graduates aspiring to build a career in analytics - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 14051 Simplilearn
Big Data in Society: OII MSc Option Course
 
05:06
‘Big data’ is increasingly being touted as the next big breakthrough in research, business, and government. This option course for the OII MSc in "Social Science of the Internet" moves beyond the hype to critically examine the unprecedented opportunities and serious challenges inherent in big data approaches to advancing knowledge. We start by defining ‘data’ and introducing the social implications of big data research. We then undertake an in depth survey of the empirical literature where big data has been used to address previously unanswerable questions, focusing in particular on social media such as Twitter, mobile phone data and collaboration platforms like Wikipedia. We also compare the approaches taken in a number of different disciplines including economics, literature, and development studies. Finally, we examine the methods used, claims made, and limitations of various approaches in terms of their representativeness and reliability. More about the MSc in Social Science of the Internet: http://www.oii.ox.ac.uk/graduatestudy/msc/
Week 5 Unit 2: Handling Big Data with New Data Provisioning Options into SAP BW
 
19:57
SAP Business Warehouse powered by SAP HANA Upload by Mohamed Elbagoury (Sr.ERP Consultant & Certified Trainer ) www.facebook.com/Elbagoury www.youtube.com/c/Elbagoury
Views: 109 Mohamed Elbagoury
Big Data Analytics: OII MSc Methods Option Course
 
02:16
Big data, the real time streams of transactional records of our daily activities, hold major promise for (computational) social science. However, to be able to collect, clean, analyse, model, and interpret these data, a high level of technical skills is required. In many cases the analytical techniques can be adopted from natural sciences, and in others, have to be invented within the framework of “Data Science” in order to “make sense” of the data under study. In this methods option course for the OII MSc in “Social Science of the Internet” some of these techniques are introduced and applied to real world datasets. The main focus of the course however is not on data collection and data cleaning but the statistical analysis, manipulating, and making sense of the already prepared data. More about the MSc in Social Science of the Internet: https://www.oii.ox.ac.uk/study/msc/
Big Data for Training Models in the Cloud (AI Adventures)
 
03:19
What do you do when your data is too big to fit on your machine? In this episode of Cloud AI Adventures, Yufeng walks through some of the options and rationale for putting your big data in the cloud. Associated Medium post "Big Data for Training Models in the Cloud": https://goo.gl/XYL9uV Google Cloud Storage: https://goo.gl/MbeKRy gsutil: https://goo.gl/6v9Tac Google Transfer Appliance: https://goo.gl/cfZynF Data transfer guide: https://goo.gl/m37jQN Cloud machine learning: https://goo.gl/Ds9q2F Watch more episodes of AI Adventures: https://goo.gl/UC5usG Subscribe to get all the episodes as they come out: https://goo.gl/S0AS51
Views: 13872 Google Cloud Platform
Understanding Big Data Using System Dynamics and XMILE
 
52:51
This webinar provides information on OASIS XML Interchange Language (XMILE) for System Dynamics. The standard will enable organizations to generate complex cause and effect models for Big Data Analytics. XMILE has the potential to revolutionize the way organizations consume Big Data, analyze options, and evaluate decisions. Speakers include Steve Adler, IBM Information Strategist, and Karim Chichakly, isee systems Chief Architect. This recording was made 24 June 2013.
Views: 1809 OASIS Open Standards
Smart Data Summit 2017 Dubai: Big Data Fabric and Data Virtualization Unlock Big Data Value
 
20:05
In this presentation, Ravi Shankar, Chief Marketing Officer at Denodo discusses proven ways to modernize your data architectures. He will demonstrate business value with proven customer case studies about best practices in building modern data architectures using big data fabric and data virtualization. This video was filmed at Smart Data Summit 2017 in Dubai.
Views: 1099 Denodo
Maria Nattestad: How Big Data is transforming biology and how we are using Python to make sense
 
39:44
PyData NYC 2015 Biology is experiencing a Big Data revolution brought on by advances in genome sequencing technologies, leading to new challenges and opportunities in computational biology. To address one of these challenges, we built a Python library named SplitThreader to represent complex genomes as graphs, which we are using to untangle hundreds of mutations in a cancer genome. The field of biology is in the midst of a sequencing revolution. The amount of data collected is growing exponentially, fueled by a cost of sequencing that is dropping at a rate outpacing Moore's Law. In Python terms, the human genome is a "list" containing 46 "strings" (chromosomes) for a total of 6 billion characters. Every single character can be the site of a mutation that brings you one step closer to cancer. My research is in cancer genomics, and I have been working to reconstruct the history of rearrangements that brought one patient's cancer genome from 46 chromosomes to 86. In an effort to untangle hundreds of large, overlapping mutations, we built a genomic graph library in Python named SplitThreader. I will motivate why a special graph library is needed to represent genomes and how this same library can be used to understand human genetic variation. I will also discuss some of the major challenges we are facing in genomics, how big data is introducing a new way of doing science, and how we ourselves have used Python to quickly iterate on new ideas and algorithms. This will serve as an overview of some of the challenges in computational biology. Slides available here: http://www.slideshare.net/MariaNattestad/data-and-python-in-biology-at-pydata-nyc-2015 GitHub repo here: : https://github.com/marianattestad/splitthreader
Views: 11227 PyData
BIOINFORMATICS: Decoding big data
 
04:27
In a collaborative internship program with the University of Oregon aimed at producing highly trained bioinformaticians, four master’s level students have spent the better part of a year embedded in Stowers labs learning and refining biological, statistical, and computational skills and applying them to biological questions posed by our scientists.
CSCI E63: Event Streaming Processing Options for Big Data
 
14:45
Final project for E-63 Spring 2015. Gives summary data on Apache Storm, Apache Spark Streaming and Amazon Kinesis
Views: 25 Anne Racel
What is Big Data? Introduction to Big Data | Big Data on Hadoop [Part 1] | Tutorial | Great Learning
 
15:06
#BigData | What is Big Data Hadoop? How does it helps in processing and analyzing Big Data? In this course, you will learn the basic concepts in Big Data Analytics, what are the skills required for it, how Hadoop helps in solving the problems associated with the traditional system and more. About the Speaker: Raghu Raman A V Raghu is a Big Data and AWS expert with over a decade of training and consulting experience in AWS, Apache Hadoop Ecosystem including Apache Spark. He has worked with global customers like IBM, Capgemini, HCL, Wipro to name a few as well as Bay Area startups in the US. #BigData #BigDataHadoop #GreatLakes #GreatLearning About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM - What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ - Follow our Blog: https://www.greatlearning.in/blog/?utm_source=Youtube
Views: 847 Great Learning
Exploiting Big Data Analytics in Trading
 
26:00
Presentation held by Jose A, Guerrero-Colon, Senior Data Scientist at the QuanTech Conference in London, April 22, 2016. Jose highlights the key Capabilities of RavenPack Data and a couple of use-cases. RavenPack's white papers are available on ►https://www.ravenpack.com/research/browse/ Visit us at ►https://www.ravenpack.com/ Follow RavenPack on Twitter ► https://twitter.com/RavenPack #RavenPack #finance #sentiment #newsanalytics #bigdata #quant
Views: 1328 RavenPack
Big Data Analytics with HDInsight: Hadoop on Azure
 
05:09:11
Aiodex’s Referral Program  will give you 20% -80% commission from their transaction fee for 7 years. The value will be calculated starting from the date the member you invite sign up ☞ http://vrl.to/c4099b4d9f Next Generation Shorten Platform. Optimal choice to make a profit and analyze traffic sources on the network. Shorten URLs and earn big money ☞ https://viralroll.com/ Get Free 15 Geek ☞ https://geekcash.org/ CodeGeek's Discuss ☞ https://discord.gg/KAe3AnN Playlists Video Tutorial ☞ http://vrl.to/d5fc7d45 Learn to code for free and get a developer job ☞ http://vrl.to/ee8f135b The Ultimate Hands-On Hadoop - Tame your Big Data! ☞ http://deal.codetrick.net/p/SJ0_BYGKZ Apache Spark 2.0 with Scala - Hands On with Big Data! ☞ http://deal.codetrick.net/p/B1MIbtMK- Taming Big Data with Apache Spark and Python - Hands On! ☞ http://deal.codetrick.net/p/SkvnpBSXx Spark and Python for Big Data with PySpark ☞ http://deal.codetrick.net/p/Sk-YpaVYyb Elasticsearch 5 and Elastic Stack - In Depth and Hands On! ☞ http://deal.codetrick.net/p/BJHxJRkYzFZ Apache Spark with Scala - Learn Spark from a Big Data Guru ☞ http://deal.codetrick.net/p/rJfZjdrR- Would you like to make sense of the volumes of data, and to use that data to streamline your workload? You can visualize your data and make it work for you. Find out how with leading experts on big data analytics with Hadoop and Microsoft Azure HDInsight. HDInsight is a managed service deployment of Hadoop, which makes it easier to query, ingest, and publish data and to create workflows. Get insight into Hadoop fundamentals and dev tools like Hive, Pig, and Sqoop, and see how to use them on HDInsight. Leave our HDInsight and Azure training session with a greater understanding of the Hadoop ecosystem and with tips and tricks to get your Hadoop analytics started right away. Instructors | Nishant Thacker - Product Marketing Manager, Microsoft; Matt Winkler - Principal Program Manager, Microsoft; Ashit Gosalia - Principal Software Engineer Manager, Microsoft; Asad Khan - Principal Lead Manager, Microsoft; Adnan Ijaz - Senior Program Manager, Microsoft Microsoft Big Data Fundamentals Get an introduction to the structure of this Hadoop big data training course, including Microsoft’s play in the Hadoop ecosystem. Hear why we need big data and how it helps the overall solutions. HDInsight Makes Hadoop Easy Explore the various enhancements Microsoft Azure HDInsight brings to the table so that developers and businesses can make the most of their existing skill sets and work with Hadoop. Take a look at the various interfaces, Visual Studio templates, and the customization options in HDInsight. Hive and Tez: Querying Big Data Get an in-depth look at one of the most important workloads on HDInsight (and maybe even on Hadoop): Hive. See what it achieves, how queries are processed, and how it is similar to the T-SQL environment, plus find out about recent enhancements with the Tez engine. Pig, Sqoop, Oozie, and Mahout Take a look at these common tools which are familiar to Hadoop Developers. See how they sit proficiently in HDInsight, and hear about the many ways that developers can use them within the Microsoft ecosystem. A New Paradigm Explore the ever-growing NoSQL capabilities in the Hadoop environment. See what HBase is, which canonical scenarios it can be used for, and how it sits as part of HDInsight. Storm Essentials Knowing that streaming is becoming essential, as more and more solutions have started demanding real-time capabilities, and find out how Storm caters to this requirement in the Hadoop environment. Get the details on Storm and how HDInsight is made so simple and seamless that it becomes almost intuitive for solutions and embedding real-time capabilities within them. Learn Microsoft Big Data Fundamentals HDInsight Makes Hadoop Easy Hive and Tez: Querying Big Data Pig, Sqoop, Oozie, and Mahout A New Paradigm Storm Essentials Recommended Resources & Next Steps Video source via: MVA ---------------------------------------------------- Website: https://goo.gl/XnM72d Website: https://goo.gl/AWpXfC Playlist: https://goo.gl/cknV8C Fanpage: https://goo.gl/kMBCFs Twitter: https://goo.gl/pNw922 Wordpress: https://goo.gl/qAJxMe Pinterest: https://goo.gl/GrRx7B Tumblr: https://goo.gl/6fTauh
Views: 7013 Big Data Training
"Big Data Revolution" - PBS Documentary
 
52:23
Big Data. This massive gathering and analyzing of data in real time is allowing us to not only address some of humanity biggest challenges but is also helping create a new kind of planetary nervous system. Yet as Edward Snowden and the release of the Prism documents have shown, the accessibility of all these data comes at a steep price. This documentary captures the promise and peril of this extraordinary knowledge revolution - Big Data Revolution. THIS IS A COPY OF ORIGINAL VIDEOCLIP THAT CAN BE FOUND HERE: https://www.youtube.com/watch?v=ahNdJdf867A
Views: 80656 Bitcoin TV
What is Big Data?
 
05:47
What is Big Data (2018)? (In 5 mins) Big Data basically refers to, huge volume of data that cannot be, stored and processed using the traditional approach within the given time frame. The next big question that comes to our mind is? How huge this data needs to be? In order to be classified as Big Data? There is a lot of misconception, while referring the term Big Data. We usually use the term big data, to refer to the data, that is, either in gigabytes or terabytes or petabytes or exabytes or anything that is larger than this in size. This does not defines the term Big Data completely. Even a small amount of data can be referred to as Big Data depending on the context it is used. Let me take an example and try to explain it to you. For instance if we try to attach a document that is of 100 megabytes in size to an email we would not be able to do so. As the email system would not support an attachment of this size. Therefore this 100 megabytes of attachment with respect to email can be referred to as Big Data. Let me take another example and try to explain the term Big Data. Let us say we have around 10 terabytes of image files, upon which certain processing needs to be done. For instance we may want to resize and enhance these images within a given time frame. Suppose if we make use of the traditional system to perform this task. We would not be able to accomplish this task within the given time frame. As the computing resources of the traditional system would not be sufficient to accomplish this task on time. Therefore this 10 terabytes of image files can be referred to as Big Data. Now let us try to understand Big Data using some real world examples. I believe you all might be aware of some the popular social networking sites, such as Facebook, Twitter, LinkedIn, Google Plus and YouTube. Each of this site, receives huge volume of data on a daily basis. It has been reported on some of the popular tech blogs that. Facebook alone receives around 100 terabytes of data each day. Whereas Twitter processes around 400 million tweets each day. As far as LinkedIn and Google Plus are concerned each of the site receives tens of terabytes of data on a daily basis. And finally coming to YouTube, it has been reported that, each minute around 48 hours of fresh videos are uploaded to YouTube. You can just imagine, how much volume of data is being stored and processed on these sites. But as the number of users keep growing on these sites, storing and processing this data becomes a challenging task. Since this data holds a lot of valuable information, this data needs to be processed in a short span of time. By using this valuable information, companies can boost their sales and generate more revenue. By making using of the traditional computing system, we would not be able to accomplish this task within the given time frame, as the computing resources of the traditional system would not be sufficient for processing and storing, such a huge volume of data. This is where Hadoop comes into picture, we would be discussing Hadoop in more detail in the later sessions. Therefore we can term this huge volume of data as Big Data. Let me take another real world example related to the airline industry and try to explain the term Big Data. For instance the aircrafts, while they are flying keep transmitting data to the air traffic control located at the airports. The air traffic control uses this data to track and monitor the status and progress of the flight on a real time basis. Since multiple aircrafts would be transmitting this data simultaneously to the air traffic control. A huge volume of data gets accumulated at the air traffic control within a short span of time. Therefore it becomes a challenging task to manage and process this huge volume of data using the traditional approach. Hence we can term this huge volume of data as Big Data. I hope you all might have understood, "What is Big Data". Big Data and Hadoop for Absolute Beginners: Click to enroll at a deep discounted price https://goo.gl/HsbEC8 Please don't forget to subscribe to our channel. https://www.youtube.com/user/itskillsindemand If you like this video, please like and share it. Follow Us On Facebook: https://www.facebook.com/itskillsindemand Twitter: https://twitter.com/itskillsdemand Google+: https://plus.google.com/+Itskillsindemand-com YouTube: http://www.youtube.com/user/itskillsindemand
Views: 186252 NetVersity
Python Options in Hadoop
 
04:43
Big Data Big Questions: What the options for developers in the Hadoop ecosystem with Python? In Hadoop Java always seems to the the go to factor for Hadoop development but there are options for Python and Scala developers. I'll answer a viewers questions today about how to get started in the Hadoop ecosystem with Python skills.
Views: 1307 Thomas Henson
Rethinking Big Data Analytics with Google Cloud (Cloud Next '18)
 
51:59
In this session, we'll discuss Google Cloud’s vision and engineering strategy that can help you move big data analytics solutions to the next level of benefits. Google Cloud Platform combines powerful serverless solutions for enterprise data warehousing, streaming analytics, managed Spark and Hadoop, modern BI, planet-scale data lake, and AI. We'll share how our customers are seamlessly integrating GCP services to innovative big data solutions, explain new partner solutions that are making it easy for you to capture value from big data, and demonstrate new solutions and product capabilities. SPTL108 Event schedule → http://g.co/next18 Watch more Spotlight sessions here → http://bit.ly/2Lby9sW Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Views: 4072 Google Cloud Platform
How to Architect a Big Data Application to Unleash its Full Potential
 
01:01:57
Big Data Solutions are required in organization that are wanting to manage their data. And for that they have to implement Big Data architecture. In this webinar, we will discuss how to take the maximum value from your Big Data/ Hadoop implementation at the enterprise level. We will explore commonly prevailing architecture, patterns, and various tools/options available in the big data space. For more recorded webinars, please visit: http://www.osscube.in/about-osscube/videos
Views: 7311 OSSCubeIndia
Why Big Data is my Career Option ? My Inspirations
 
05:18
This is the starting video of my video podcast for bid data technologies which talks about why I choose big data as my career option. It also talks about the top companies and the big data technologies used in their companies.
Big Data and Hadoop Developer 2018 | Big Data as Career Path | Introduction to Big Data and Hadoop
 
04:26
https://acadgild.com/big-data/big-data-development-training-certification/ Big Data and Hadoop Developer 2018 | Big Data as Career Path | Introduction to Big Data and Hadoop Big Data is growing explosively bigger & bigger every day. Get to know what makes Big Data the next big thing. What is Big Data all about? Big Data has been described in multiple ways by the industry experts. Let’s have a look at what Wikipedia has to say. Big Data is a term for datasets that are so large or complex that traditional data processing applications are inadequate. To put it in simple words, Big Data is the large volumes of structured and unstructured data. Did You Know? • According to Wikibon and IDC, 2.4 quintillion bits are generated every day? • Did you know, the data from our digital world will grow from 4.4 trillion gigabytes in 2013 to 44 trillion gigabytes in 2020? • In addition to that, data from embedded systems will grow from 2% in 2013 to 10% in 2020 The sheer volume of the data generated these days has made it absolutely necessary to re-think how we handle them. And with growing implementation of Big Data in sectors like banking, logistics, retail, e-commerce and social media, the volume of data is expected to grow even higher. Other than its Sheer size, what else makes Big data so important? • Mountains of data that can be gathered and analyzed to discover insights ad make better decisions. • Using Big data, social media comments can be analyzed in a far timelier and relevant manner offering a richer data set. • With Big Data, banks can now use information to constantly monitor their client’s transaction behaviors in real time. • Big Data is used for trade analytics, pre-trade decision-support analytics, sentiment measurement, predictive Analytics etc. • Organizations in media and entertainment industry use Big Data to analyze customer data along with behavioral data to create detailed customer profiles. • In the manufacturing and natural resources industry, Big Data allows for predictive modeling to support decision making. What is Big Data and its importance in the near future Gartner analyst Doug Laney introduced the 3Vs concept in 2001. Since then, Big Data has been further classified, based on the 5Vs. • Volume – The vast amounts of data generated every second. • Variety – The different types of data which contribute to the problem, such as text, videos, Images, audio files etc. • Velocity – The speed at which new data is generated and moves around. • Value – Having an access to Big Data in no good unless we turn it into value • Veracity - Refers to the messiness or trustworthiness of the data. Why is Big Data considered as an excellent career path? According to IDC forecast, the Big Data market is predicted to be worth of $46.34 billion by 2018 and is expected to have a sturdy growth over the next five years. Big Data Salary: As per Indeed, the average salary for Big Data professionals is about 114,000 USD per annum, which is around 98% higher than average salaries for all job postings nationwide. And Glassdoor quotes the median salary for Big Data professionals to be 104,850 USD per annum. Big Data professionals get a high percentage of hike in salary and Data scientists get a very good hike up to 8.90% YOY. As the Big Data market grows, so does the demand for the skilled workforce? According to Wanted Analytics, the demand for Big Data skills is to increase by 118% over the previous year. Do you need more reasons to believe in the power of Big Data? EMC, IBM, Cisco, Oracle, Adobe, Amazon, Accenture are just a few of the top companies who are constantly looking for Big Data skills. With the right training and hands-on experience, you too can find your dream career in one of these top companies. The path to your dream job is no longer a mystery. Sign up now & get started with your dream career. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 48717 ACADGILD
Big data analysis Option Value for IC Testing : Option Value Description
 
33:32
SANDIN TECHNOLOGY CO,.LTD. 炫鼎科技 Tel:886-3-5316996
Views: 32 陳威瑀
Azure Big Data Scenarios
 
05:55
This video is part of the Architecting Microsoft Azure Solutions course available on EdX. To sign up for the course, visit: http://aka.ms/edxazurearchitecture
Views: 4386 DEV205x
AWS re:Invent 2017: Big Data Architectural Patterns and Best Practices on AWS (ABD201)
 
59:56
In this session, we simplify big data processing as a data bus comprising various stages: collect, store, process, analyze, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Views: 29999 Amazon Web Services
Storage Options for Big Data Solutions in Azure
 
57:51
In this webinar, we will compare the various Big Data storage options available in Microsoft Azure to help you choose the right one for your Big Data solution. Learn more about Opsgility at: https://www.opsgility.com/
Views: 94 Opsgility
Big Data: GeoEvent & GeoAnalytics, Leveraging the Spatiotemporal Big Data Store
 
58:55
At ArcGIS 10.5, both GeoEvent and GeoAnalytics work with real-time and big data through the spatiotemporal option of the ArcGIS Data Store. Real-time observational data can come from moving objects, changing attributes of stationary sensors, or both. The spatiotemporal big data store enables the archival of observation data, sustains high-velocity write throughput, and runs across multiple nodes to enable high-volume storage. Additionally, GeoAnalytics Server now leverages the spatiotemporal big data store both as a data source and to store the results of big data analytics. This session will walk through how to configure your ArcGIS Data Store, write high-velocity data to it, visualize and query high-volume data via map and feature services, and use it for the input or results of distributed analytics. Presented by Adam Mollenkopf and Ricardo Trujillo
Views: 3617 Esri Events
Big data storage  options & recommendations
 
50:45
Hadoop clusters are often built around commodity storage, but architects now have a wide selection of Big Data storage choices, including solid-state or spinning disk for clusters and enterprise storage for compatibility layers and connectors. In this webinar, our experts will review the storage options available to Hadoop architects and provide recommendations for each use case, including an active-active replication option that makes data available across multiple storage systems.
Views: 68 WANdisco
Python for Big Data Analytics - 1 | Python Hadoop Tutorial for Beginners | Python Tutorial | Edureka
 
56:08
( Python Training : https://www.edureka.co/python ) This Python tutorial will help you understand why Python is popular with Big Data and how Hadoop and Python goes hand in hand. This Python tutorial is ideal for beginners. This video will help you learn: • What is Big Data? • Why Python is popular with Big Data? • Hadoop with Python • Python NLTK on Hadoop • Python and Data Science • Demo on Zombie Invasion Model The topics related to ‘Python’ have been widely covered in our course. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 78600 edureka!
CSCI-E63, 2015 Final Project - Messaging options for Big Data: Kafka vs. LogStash vs Fluentd
 
02:30
Messaging options for Big Data: Kafka vs. LogStash vs Fluentd
TEDxUofM - Jameson Toole - Big Data for Tomorrow
 
13:46
TEDxUofM took place April 8th, 2011 at the historic Michigan Theater on the campus of the University of Michigan, Ann Arbor. Jameson Toole -- University of Michigan 2010, Jameson Toole is a PhD in MIT's Engineering Systems Division with the support of a National Science Foundation Graduate Research Fellowship. His research focuses on using large behavioral data sets generated from the web (Twitter, Facebook, Google, etc.), cell phones, or any other means, to find emergent patterns in human behavior and dynamics. Toole completed his undergraduate degrees at the University of Michigan majoring in Physics, Mathematical Physics, and Economics. About TEDx, x = independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized. (Subject to certain rules and regulations.)
Views: 44161 TEDx Talks
How Can BDE Help Someone Get Started With Big Data?
 
01:09
The topic of big data can be daunting - there are so many tools, options and methods. Hajira Jabeen explains how the BDE Integrator Platform can help someone who, like her, is new to big data, get up and running quickly and with a minimum of hassle.
Views: 149 Big Data Europe
Scaling Analytics with Big Data
 
05:58
This video shows how to make your current KNIME workflow more scalable for more future data using Spark and Big Data Platforms. The cool feature of the KNIME Big Data Extension is that the nodes that run the functions on Spark have the same GUI as the nodes that run the same functions on KNIME. You do not need to learn anything new! The first part of the original workflow, running on KNIME Analytics Platform and implementing advanced ETL procedures, is shown in this other video "Advanced ETL Functionalities and Machine Learning Pre-processing" https://youtu.be/IEAsUTN8q68
Views: 3309 KNIMETV
Top 5 Career Options in 2018 with High Salaries - 5 Top Trending Career Options in 2018
 
05:20
Check out my latest Video: https://www.youtube.com/watch?v=VCrrLdxEXtA TOP 5 CAREER OPTIONS IN 2018 WITH HIGH SALARIES Book Link here: http://amzn.to/2DFALyS Article Link: https://beginnertuts.net/top-5-career-options-in-2018-with-high-salaries/ Career means an occupation undertaken for a substantial period of one’s life and with opportunities for growth. It is a representational journey through learning, work and other aspects of life that helps in the occupation undertaken. Now how chose the best career option for one’s self?!? To choose an occupation as a career, your personality plays a vital role. Always choose a career that suits your personality and your ambitions the best. Down below are few points that will help you in identifying which occupation or career path to choose. Then we will look in Top 5 career option in 2018 or Top career options in US and world around. 1. DEVOPS ENGINEER DevOps Engineer is an IT professional who works along with software developers and system operations to create automation for a different process. Automating different task helps companies in completing their task quicker and hence profits increases. That is why many companies are moving towards automating different processes and that increases the demand for DevOps Engineers and their average annual salary is $99,000. 2. ANALYTICAL MANAGER As the market is getting bigger, more data is being collected. It is the task of the Analytical manager to collect this raw data from multiple sources (customer feedback, operations, IT) and crack it into a valuable business insight so that companies have a better overview of the industry. The annual salary of these managers averages about $113,000. They also develop a strategy for data analysis; relate industry knowledge to improve performance. 3. DATA SCIENTIST Again it is a computer science career, in which you also have to be a part mathematician and a part trend spotter. This career was not on the radar a decade ago, but as big data begin to grow the need for Data Scientist has also increased. Data is no longer IT’s job to handle but it has become the key factor to convert ideas into new ways to make a profit. Its basic purpose is to unearth the data that is required for business insight and analysis. Data Scientists earns averagely at $120,000 annually. 4. STRATEGY MANAGER They have the highest average per annum of $ 135,000. Their main task to help companies grows by developing strategies which have minimum risk factor involved. They help the company achieve this aim by assessing company goals and work with company’s executives to develop an action plan to accomplish the said targets. Read full article here: https://beginnertuts.net/top-5-career-options-in-2018-with-high-salaries/
Views: 5778 Beginner Tuts
Analyzing Big Data with Twitter - Lecture 1 - Intro to course; Twitter basics
 
51:23
http://blogs.ischool.berkeley.edu/i290-abdt-s12/ Course: Information 290. Analyzing Big Data with Twitter School of Information UC Berkeley Lecture 1: August 23, 2012 Course description: How to store, process, analyze and make sense of Big Data is of increasing interest and importance to technology companies, a wide range of industries, and academic institutions. In this course, UC Berkeley professors and Twitter engineers will lecture on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter microblog data. Topics will include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing. Social science perspectives on analyzing social media will also be covered. This is a hands-on project course in which students are expected to form teams to complete intensive programming and analytics projects using the real-world example of Twitter data and code bases. Engineers from Twitter will help advise student projects, and students will have the option of presenting their final project presentations to an audience of engineers at the headquarters of Twitter in San Francisco (in addition to on campus). Project topics include building on existing infrastructure tools, building Twitter apps, and analyzing Twitter data. Access to data will be provided.
Buying Cheap Options | Trading Data Science
 
14:19
The large moves we have seen recently in the markets may have some thinking about buying cheap options for the very limited risk and the unlimited reward. What's the catch? Dr. Data (Michael Rechenthin, Ph.D.) explains it all. A table of the results of buying OTM calls and puts at a cost of $0.05 and with 45 days to expiration (DTE) in SPY (S&P 500 ETF) over the last 10 years was displayed. The table included the percentage of profitable trades at expiration, and at anytime during the trade. The table showed that 42% of the calls and 32% of the puts were profitable at some point before expiration, but only 1.1% and 1.2% were profitable at expiration. A graphic was displayed of a SPY put bought on August 28th, 2008 (shortly before the financial crisis) that was eventually worth $1,255 at one point in time. It was noted that a $0.05 option is up over $100 at anytime before expiration in only 2% of all the occurrences tested. Some might think that they can beat the odds by waiting until the market experiences a large drop. Dr. Mike explained that it wouldn’t work because the large increase in implied volatility (IV) would result in a call being bought that was much farther OTM. He used two graphs to illustrate his point. Selling cheap options is also a bad bet. Selling premium may be a winning strategy, but the risk/reward ratio of selling cheap options is poor, and more importantly, the return on capital (ROC) is very poor. ======== tastytrade.com ======== Finally a financial network for traders, built by traders. Hosted by Tom Sosnoff and Tony Battista, tastytrade is a real financial network with 8 hours of live programming five days a week during market hours. From pop culture to advanced investment strategies, tastytrade has a broad spectrum of content for viewers of all kinds! Tune in and learn how to trade options successfully and make the most of your investments! Watch tastytrade LIVE daily Monday-Friday 7am-3:30pmCT: http://ow.ly/EbzUU Subscribe to our YouTube channel: https://www.youtube.com/user/tastytrade1?sub_confirmation=1 Follow tastytrade: Twitter: https://twitter.com/tastytrade Facebook: https://www.facebook.com/tastytrade LinkedIn: http://www.linkedin.com/company/tastytrade Instagram: http://instagram.com/tastytrade Pinterest: http://www.pinterest.com/tastytrade/
Views: 10080 tastytrade
Task Processing, APIs, and Big Data in JavaScript
 
55:00
https://developer.oracle.com/code/online | Pratik Patel: There are tons of options for doing data processing in other languages such as Java and Python. With actionhero.js, you can use Node.js's event loop model to create a scalable and cohesive API for processing and serving large amounts of data. From the actionhero.js documentation, here's a quick synopsis of this great framework: actionhero.js is a multi-transport API server with integrated cluster capabilities and delayed tasks. The goal of actionhero.js is to create an easy-to-use toolkit for making reusable and scalable APIs. In this session, we'll use actionhero.js to build some examples, with lots of code, to demonstrate the capabilities of this framework. We'll consume some data, do some task processing, then access the data via an API. We'll use WebSockets along with standard HTTP to build a real-time, web-enabled, application.
Views: 434 Oracle Developers
Big Data Virtualization & Open Source Software | vSphere
 
05:59
Learn about Big Data Virtualization and open source software with vSphere. Virtualizing Big Data applications offers a lot of benefits that cannot be obtained on physical infrastructure or in the cloud. Simplifying the management of your big data infrastructure gets faster time to results, making it more cost-effective.
Views: 982 VMware vSphere
Data in a Microservices world: from conundrum to options by Emmanuel Bernard, Madou Coulibaly
 
47:59
Subscribe to Devoxx on YouTube @ https://bit.ly/devoxx-youtube Like Devoxx on Facebook @ https://www.facebook.com/devoxxcom Follow Devoxx on Twitter @ https://twitter.com/devoxx Microservices are great, problems arise when you start to have two of them and when you want to deal with data :) Pun aside, data and state is a big subject that is largely ignored when discussing Microservices. Conundrum #1 : What is the aimed data architecture in a perfect Microservices architecture? Conundrum #2 : How do you share state between instances of a given Microservice in a stateless 12 factor approach? Conundrum #3 : how to echange state between Microservices that must remain independent? Conundrum #4 : how do I go from my brownfield database to a fleet of Microservices IRL without a Big Bang? Conundrum #5 : with many Microservices touching many data sets, how do I guarantee uniformed security (GDPR anyone)? And the list goes on. This presentation is an opinionated answer to these questions. And yes we do demo these concepts. Emmanuel Bernard From Red Hat Emmanuel Bernard is Chief Architect, Data at Red Hat. He oversees how data and middleware interact and is involved in open source communities like Hibernate, Infinispan, Teiid, Debezium (Change Data Capture) and more. To sum up, everything data :) He is a Java Champion and a Java Community Process expert group member (JPA) and former spec lead (Bean Validation). He co-hosts two podcasts Les Cast Codeurs and JBoss Community Asylum. You can follow him on twitter at @emmanuelbernard. Madou Coulibaly From Red Hat Hello, my name is Madou Coulibaly and I am a EMEA Specialist Solution Architect at Red Hat since 2016 with a strong focus on Data. With 8+ years experience in Data & information management (BI, DW, Big Data, …), I am now working with customers across EMEA for providing expertise, guidance and knowledge about these Data products. Microservices is a new journey Data has to embark on with the big challenge to “live” in a distributed and complex environment. So to help it, Data brings me into this new adventure.
Views: 286 Devoxx
Big Data, Fast Data  The Need for In Memory Database Technology
 
01:12:51
Originally aired June 25, 2014. Today's data is big. But it's also fast. It comes in unrelenting, blazingly fast streams from mobile devices, M2M, the Internet of Things, social media and beyond in the form of observations, log records, interactions, sensor readings, clicks, game play, and similar events that happen hundreds to millions of times per second. Interacting with fast data is a fundamentally different process than interacting with big data that is at rest. And few businesses have the ability to extract the value of that data when it matters most — at the moment it arrives — because traditional database technology simply hasn't kept pace. Dr. Michael Stonebraker, Professor at MIT and co-founder of VoltDB, has long held the belief that, without the right database architecture in place, today's organizations run the risk of being left behind in a world that's smarter and faster than what legacy systems can handle. And now the rest of the world is catching up, with technologists realizing that the whole "data economy" is transforming, with a very important distinction between the two major ways in which we interact with data. This unique dynamic is driving innovation and adoption of new technologies at an unbelievable pace — and causing a huge change in the way companies manage data. In this webcast, Scott Jarr, co-founder and chief strategy officer at VoltDB, will discuss the new corporate data architecture — and the necessary technology components for facing this data management challenge. And then database pioneer Dr. Michael Stonebraker will share his "one-size-never-fits-all" perspective for developing the ideal architecture for managing — and maximizing the value of — fast, big data in your organization. Don't be left behind — join Scott Jarr and Mike Stonebraker for this informative, interactive introduction to in-memory database technology. About Michael Stonebraker Co-founder, VoltDB Dr. Michael Stonebraker has been a pioneer of database research and technology for more than a quarter of a century. He was the main architect of the Ingres relational DBMS, and the object-relational DBMS PostgreSQL. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at MIT, he was a co-architect of the Aurora Borealis stream processing engine (commercialized as StreamBase), the C-Store column-oriented DBMS (commercialized as Vertica), and the H-Store transaction processing engine (commercialized as VoltDB). Currently, he is working on science-oriented DBMSs and search engines for accessing the deep web. He is the co-founder of six venture capital-backed start-ups. Professor Stonebraker is the author of scores of research papers on database technology, operating systems and the architecture of system software services. He was awarded the ACM System Software Award in 1992, for his work on Ingres. Additionally, he was awarded the first annual Innovation award by the ACM SIGMOD special interest group in 1994, and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005, and is presently Adjunct Professor of Computer Science at MIT. About Scott Jarr Co-Founder & Chief Strategy Officer, VoltDB Scott Jarr brings more than 20 years of experience building, launching and growing technology companies from inception to market leadership in highly competitive environments. Prior to joining VoltDB, Scott was VP Product Management and Marketing at on-line backup SaaS leader LiveVault Corporation. While at LiveVault, Scott was key in growing the recurring revenue business to 2,000 customers strong, leading to an acquisition by Iron Mountain. Scott has also served as board member and advisor to other early-stage companies in the search, mobile, security, storage and virtualization markets. Scott has an undergraduate degree in mathematical programming from the University of Tampa and an MBA from the University of South Florida. - Don't miss an upload! Subscribe! http://goo.gl/szEauh - Stay Connected to O'Reilly Media. Visit http://oreillymedia.com Sign up to one of our newsletters - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia http://www.oreilly.com/webcasts
Views: 3032 O'Reilly
EN - ESCP Europe - Master in Big Data and Business Analytics
 
40:36
Find the REPLAY & THE ANALYSIS by Campus-Channel for this program here: http://www.campus-channel.com/fr/escp-europe-master-in-big-data-and-business-analytics.html GUESTS: Wei ZHOU - Directeur Scientifique Marion LEPARMENTIER - Directrice des Mastères Spécialisées QUESTIONS: 00:17 The Pitch 01:47 Do we have the option to study on both the Paris and Berlin campus or do we complete the program at one location? 04:26 Hello, what should be the level of my programming language? Will I be admitted without any programming knowledge? 06:30 The international seminar in China is obligatory? It is included in the tuition? 09:28 What are the major leading companies with which you have strong partnerships? Does it mean that we will have the opportunity to do real business cases with those companies? 11:52 Will there be any competency test regarding our knowledge of the different business analytics tools or is it just a personality interview? 15:27 Three Words Max 16:51 What would be the projected starting salary with this degree? 18:17 I understand students of all backgrounds are welcome to apply. But do you think this program is more fit for students of statistics/math/CS background or more fit for students with a business degree? 19:54 May I know what we should expect for the interview? What are the important qualities you look for in candidates? 21:55 How many students are enrolled in the program, or will be allowed to enroll next year? 22:48 Do students still need to take the English test if they have been studying in English for the whole duration of their bachelor (non English speaking country)? If no, can they submit their English test after sending the application? 24:49 Does the program have any affiliation with SAS and / or IBM. Have these organizations designed any course topic, and do students after completion of program receive any certifications from these organizations? 27:46 How much prior work experience is required? For example I am still doing my bachelor degree and have completed a 6-month internship in marketing analytics, is this sufficient (with excellent motivation)? 29:00 The Expert Question 29:13 The Expert Question: One of the primary uses for big data analytics by businesses is of course for marketing and to increase sales. But what other ways can we use big data in business that are not profits oriented, and what trends do you see for big data in the future? 32:55 Can an American student find a job in France upon completion of the program? 34:10 I want to participate in your program as a 37-year old American student. Are there be any scholarships available to pay tuition (the fee is quite high). I have a BBA and have worked as a high school science teacher for nine years. 35:20 Is the internship obligatory to complete the program? (for those with experience who want to start working). 37:30 There is something else payable to the school apart from the tuition? Does the school help the students to find accommodation near the different campuses? 38:57 Conclusion
Views: 4086 Campus-Channel
Big Data and Beyond | 2017 Wharton People Analytics Conference
 
29:53
Matthew Salganik is Professor of Sociology at Princeton University. Keith McNulty has worked for over a decade in I/O Psychology, Psychometrics and People Analytics.  He is currently Head of People Analytics at McKinsey & Company. Moderated by Peter S. Fader, Frances and Pei-Yuan Chia professor of Marketing, The Wharton School.
Views: 984 Wharton School
The Big Data Era Explained: Indicators, Analytics, Examples, Finance, Innovation, Marketing
 
57:19
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, finance, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5] Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10] Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration." https://en.wikipedia.org/wiki/Big_data In 2001, Strassel was the first mainstream journalist to cover problems with historian Michael Bellesiles's book Arming America (2000). Bellesiles resigned his professorship at Emory University in 2002 following an investigation launched by the university, and the Bancroft Prize for the book was revoked.[4] She became a senior editorial writer and member of the editorial board of the Wall Street Journal in 2005.[3] In 2006, Strassel co-wrote Leaving Women Behind: Modern Families, Outdated Laws (ISBN 0-7425-4545-8), which argues that government regulation interferes with marketplace initiatives to provide women with economic opportunity. In 2007, Strassel began writing the long-running "Potomac Watch" column for the Wall Street Journal.[3] Strassel favorably profiled then-candidate for US vice president Sarah Palin shortly before the 2008 election in an article entitled "'I Haven't Always Just Toed the Line'".[5] The article originally appeared in the Weekend Interview section of The Wall Street Journal on November 1, 2008. In 2012, Strassel wrote an editorial in the WSJ that alleged the Obama campaign was targeting Frank L. VanderSloot, a national finance co-chair for Mitt Romney's 2012 presidential campaign and a top campaign donor.[6] Strassel's editorial was disputed by journalists and liberal commentators.[7][8][9] In May 2013, as part of the IRS targeting controversy, Strassel reported that the IRS (not the Obama campaign) targeted conservatives, including Frank L. VanderSloot.[10] In 2014, Strassel won the $250,000 Bradley Prize, an honor which she shares with columnist George Will and former CEO of Fox News Roger Ailes. On the occasion of the award, the president of the conservative Bradley Foundation, Michael Grebe, noted "Ms. Strassel’s 'Potomac Watch' column is an essential example of journalistic excellence. Her keen focus on government transparency and accountability as well as her important analyses on issues of the day strengthen the American fabric.”[11] In February 2016, Strassel was among the panelists for the South Carolina Republican presidential debate.[12] In June 2016, she published a book called The Intimidation Game: How the Left Is Silencing Free Speech, detailing her assertions about the IRS's alleged harassment of conservatives and other similar events. https://en.wikipedia.org/wiki/Kimberley_Strassel
Views: 1401 The Film Archives
Scala Tutorial 23 - Scala Options Type
 
10:14
In this video we will cover the basic syntax and capabilities of Options in Scala. In Scala Options are the collections which Represents optional values. Instances of `Option` are either an instance of $some or the object $none.. -------------------Online Courses to learn---------------------------- Blockchain Course - http://bit.ly/2Mmzcv0 Big Data Hadoop Course - http://bit.ly/2MV97PL Java - https://bit.ly/2H6wqXk C++ - https://bit.ly/2q8VWl1 AngularJS - https://bit.ly/2qebsLu Python - https://bit.ly/2Eq0VSt C- https://bit.ly/2HfZ6L8 Android - https://bit.ly/2qaRSAS Linux - https://bit.ly/2IwOuqz AWS Certified Solutions Architect - https://bit.ly/2JrGoAF Modern React with Redux - https://bit.ly/2H6wDtA MySQL - https://bit.ly/2qcF63Z ----------------------Follow--------------------------------------------- My Website - http://www.codebind.com My Blog - https://goo.gl/Nd2pFn My Facebook Page - https://goo.gl/eLp2cQ Google+ - https://goo.gl/lvC5FX Twitter - https://twitter.com/ProgrammingKnow Pinterest - https://goo.gl/kCInUp Text Case Converter - https://goo.gl/pVpcwL -------------------------Stuff I use to make videos ------------------- Stuff I use to make videos Windows notebook – http://amzn.to/2zcXPyF Apple MacBook Pro – http://amzn.to/2BTJBZ7 Ubuntu notebook - https://amzn.to/2GE4giY Desktop - http://amzn.to/2zct252 Microphone – http://amzn.to/2zcYbW1 notebook mouse – http://amzn.to/2BVs4Q3 ------------------Facebook Links ---------------------------------------- http://fb.me/ProgrammingKnowledgeLearning/ http://fb.me/AndroidTutorialsForBeginners http://fb.me/Programmingknowledge http://fb.me/CppProgrammingLanguage http://fb.me/JavaTutorialsAndCode http://fb.me/SQLiteTutorial http://fb.me/UbuntuLinuxTutorials http://fb.me/EasyOnlineConverter
Views: 4143 ProgrammingKnowledge
Leveraging Apex as a Data Streaming Option...(Applications Track) @ Apex Big Data World 2017, Pune
 
26:14
This presentation took place at Apex Big Data World 2017, Pune, India. Leveraging Apex as a Data Streaming Option in Kogni Speakers: Vijay Datla, Lead - Big Data Practice @Clairvoyant India Pvt Ltd & Shantanu Mirajkar, Managing Director @Clairvoyant India Pvt Ltd Abstract: Kogni is a suite of end-to-end tools and solutions for your Data Lake, designed to help data scientists, engineers, and business users unlock the value of data at every stage of the data journey, from data federation to decision making. Below are few capabilities that help accelerate your Data Lake Implementations in a fast, secured and governed manner. a. Ingesting/Processing data into the data lake in batches and real-time b. Inspecting the data pre-post ingestion c. Securing the data in Data Lake Kogni removes the grunt work associated with building out a Data Lake and the masking, tokenization and anonymization capabilities of the platform helps the customers focus on building secure Data Lakes right from day one. Integration with Apex: We are working on utilizing Apache Apex as one of the options in the platform for Data Ingestion and processing in Realtime. This is to facilitate the client to configure the data ingestion/processing settings for Apex in case they prefer to use Apex over other similar technologies like Apache Spark.
Views: 19 DataTorrent

Solu medrol 125mg 2ml is how many ounces
Side effects metoclopramide hcl tab mg
Cincofarm 100mg benadryl
Pramipexole 0.25 mg
Para que serve metformin 500 mg