Скачать с ютуб Gautam Siwach on Big Data в хорошем качестве

Gautam Siwach on Big Data

What is Big Data ? What are three major sources of Big Data ? What are the skills that a data Scientist must have ? In New York, Join to be member of the largest AI meetup Link to Join :- https://www.meetup.com/AI-Modeling-an... Learn more, Connect via Instagram -   / siwachgautam   This video is a replay of the Big Data Masterclass at 590 Madison Avenue, New York City (Nov 5,2019). The complete overview of Big Data with the review of quiz to earn a Big Data Certification from IBM affiliate Cognitive Class. In this tutorial is the Big Data Overview | Big Data ethics | Big Data Governance | Three major sources of Big Data | Skills of a data Scientist | Graded review questions | Big Data Exploration | What is a method of exploring Data The time is right as Cloudera, Hortonworks are merged, and now debuting with Edge to AI, this class gives a knowledge of upcoming new, and utilizing old. A must and good to-know session on Big Data Technologies. Getting together to get your question answered by Subject Matter Expert's and IBM team members Elizabeth Krivan, Matt Tallman, Max Holland, Nicole Marlin, Bob Green. We shall cover topics ranging from Spark AI, Big Data, Cloudera, IBM, Hortonworks, ESS, Red Hat and what it bring to the table. We expect keen clients, and personals to take advantage of this unique learning event. What is Big Data ? Big Data is more than high-volume, high-velocity data. In this session you will learn what big data is, why it matters and how Big Data can help you make better decisions every day. In many cases clients have Hadoop or are considering Hadoop for their Big Data platform. Spark does not require Hadoop, however it is important to understand the basics of Hadoop and the advantages Spark offers. • Hadoop offers a cost effective storage solution for large data volumes with no format requirements. • Spark does not include a storage layer but can run against Hadoop and other storage platforms. • Hadoop offers a rich platform to store and process very large data sets across hundreds to thousands of computing nodes that operate in parallel. • Hadoop processes data by storing working data on disk. Spark stores working data in memory. This typically leads to much higher performance. • Hadoop is designed for long-running jobs, Spark can be used for real-time streaming and interactive queries. Certain questions to ask yourself in order to asses where are you in you data platform journey. Have you built a big data environment? Are you using Hadoop? If so, do you have a need for faster processing? Are your programmers struggling with MapReduce? What types of analytics applications do you have today or foresee in the near future?