狠狠撸shows by User: OmidVahdaty / http://www.slideshare.net/images/logo.gif 狠狠撸shows by User: OmidVahdaty / Tue, 11 Feb 2020 07:34:00 GMT 狠狠撸Share feed for 狠狠撸shows by User: OmidVahdaty Data Pipline Observability meetup /slideshow/data-pipline-observability-meetup/227594271 observabilitymeetup-shared-200211073400
Data Pipeline Observability]]>
Data Pipeline Observability]]> Tue, 11 Feb 2020 07:34:00 GMT /slideshow/data-pipline-observability-meetup/227594271 OmidVahdaty@slideshare.net(OmidVahdaty) Data Pipline Observability meetup OmidVahdaty Data Pipeline Observability <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/observabilitymeetup-shared-200211073400-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Data Pipeline Observability
Data Pipline Observability meetup from Omid Vahdaty
]]> 463 0 https://cdn.slidesharecdn.com/ss_thumbnails/observabilitymeetup-shared-200211073400-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Couchbase Data Platform | Big Data Demystified /slideshow/couchbase-data-platform-big-data-demystified/202830633 couchbasedemystified-191207174312
Couchbase is a popular open source NoSQL platform used by giants like Apple, LinkedIn, Walmart, Visa and many others and runs on-premise or in a public/hybrid/multi cloud. Couchbase has a sub-millisecond K/V cache integrated with a document based DB, a unique and many more services and features. In this session we will talk about the unique architecture of Couchbase, its unique N1QL language - a SQL-Like language that is ANSI compliant, the services and features Couchbase offers and demonstrate some of them live. We will also discuss what makes Couchbase different than other popular NoSQL platforms like MongoDB, Cassandra, Redis, DynamoDB etc. At the end we will talk about the next version of Couchbase (6.5) that will be released later this year and about Couchbase 7.0 that will be released next year.]]>
Couchbase is a popular open source NoSQL platform used by giants like Apple, LinkedIn, Walmart, Visa and many others and runs on-premise or in a public/hybrid/multi cloud. Couchbase has a sub-millisecond K/V cache integrated with a document based DB, a unique and many more services and features. In this session we will talk about the unique architecture of Couchbase, its unique N1QL language - a SQL-Like language that is ANSI compliant, the services and features Couchbase offers and demonstrate some of them live. We will also discuss what makes Couchbase different than other popular NoSQL platforms like MongoDB, Cassandra, Redis, DynamoDB etc. At the end we will talk about the next version of Couchbase (6.5) that will be released later this year and about Couchbase 7.0 that will be released next year.]]> Sat, 07 Dec 2019 17:43:12 GMT /slideshow/couchbase-data-platform-big-data-demystified/202830633 OmidVahdaty@slideshare.net(OmidVahdaty) Couchbase Data Platform | Big Data Demystified OmidVahdaty Couchbase is a popular open source NoSQL platform used by giants like Apple, LinkedIn, Walmart, Visa and many others and runs on-premise or in a public/hybrid/multi cloud. Couchbase has a sub-millisecond K/V cache integrated with a document based DB, a unique and many more services and features. In this session we will talk about the unique architecture of Couchbase, its unique N1QL language - a SQL-Like language that is ANSI compliant, the services and features Couchbase offers and demonstrate some of them live. We will also discuss what makes Couchbase different than other popular NoSQL platforms like MongoDB, Cassandra, Redis, DynamoDB etc. At the end we will talk about the next version of Couchbase (6.5) that will be released later this year and about Couchbase 7.0 that will be released next year. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/couchbasedemystified-191207174312-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Couchbase is a popular open source NoSQL platform used by giants like Apple, LinkedIn, Walmart, Visa and many others and runs on-premise or in a public/hybrid/multi cloud. Couchbase has a sub-millisecond K/V cache integrated with a document based DB, a unique and many more services and features. In this session we will talk about the unique architecture of Couchbase, its unique N1QL language - a SQL-Like language that is ANSI compliant, the services and features Couchbase offers and demonstrate some of them live. We will also discuss what makes Couchbase different than other popular NoSQL platforms like MongoDB, Cassandra, Redis, DynamoDB etc. At the end we will talk about the next version of Couchbase (6.5) that will be released later this year and about Couchbase 7.0 that will be released next year.
Couchbase Data Platform | Big Data Demystified from Omid Vahdaty
]]> 242 1 https://cdn.slidesharecdn.com/ss_thumbnails/couchbasedemystified-191207174312-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Machine Learning Essentials Demystified part2 | Big Data Demystified /slideshow/machine-learning-essentials-demystified-part2-big-data-demystified/186224093 ml-demistifiedpart2-191024102243
achine Learning Essentials Abstract: Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about? In this session we will talk about what ML actually is and in which cases it is useful. We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python. The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.]]>
achine Learning Essentials Abstract: Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about? In this session we will talk about what ML actually is and in which cases it is useful. We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python. The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.]]> Thu, 24 Oct 2019 10:22:43 GMT /slideshow/machine-learning-essentials-demystified-part2-big-data-demystified/186224093 OmidVahdaty@slideshare.net(OmidVahdaty) Machine Learning Essentials Demystified part2 | Big Data Demystified OmidVahdaty achine Learning Essentials Abstract: Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about? In this session we will talk about what ML actually is and in which cases it is useful. We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python. The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ml-demistifiedpart2-191024102243-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> achine Learning Essentials Abstract: Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about? In this session we will talk about what ML actually is and in which cases it is useful. We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python. The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.
Machine Learning Essentials Demystified part2 | Big Data Demystified from Omid Vahdaty
]]> 566 0 https://cdn.slidesharecdn.com/ss_thumbnails/ml-demistifiedpart2-191024102243-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Machine Learning Essentials Demystified part1 | Big Data Demystified /slideshow/machine-learning-essentials-demystified-part1-big-data-demystified/169968230 ml-demistifiedpart1-190908110738
Machine Learning Essentials Abstract: Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about? In this session we will talk about what ML actually is and in which cases it is useful. We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python. The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.]]>
Machine Learning Essentials Abstract: Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about? In this session we will talk about what ML actually is and in which cases it is useful. We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python. The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.]]> Sun, 08 Sep 2019 11:07:38 GMT /slideshow/machine-learning-essentials-demystified-part1-big-data-demystified/169968230 OmidVahdaty@slideshare.net(OmidVahdaty) Machine Learning Essentials Demystified part1 | Big Data Demystified OmidVahdaty Machine Learning Essentials Abstract: Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about? In this session we will talk about what ML actually is and in which cases it is useful. We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python. The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ml-demistifiedpart1-190908110738-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Machine Learning Essentials Abstract: Machine Learning (ML) is one of the hottest topics in the IT world today. But what is it really all about? In this session we will talk about what ML actually is and in which cases it is useful. We will talk about a few common algorithms for creating ML models and demonstrate their use with Python. We will also take a peek at Deep Learning (DL) and Artificial Neural Networks and explain how they work (without too much math) and demonstrate DL model with Python. The target audience are developers, data engineers and DBAs that do not have prior experience with ML and want to know how it actually works.
Machine Learning Essentials Demystified part1 | Big Data Demystified from Omid Vahdaty
]]> 659 5 https://cdn.slidesharecdn.com/ss_thumbnails/ml-demistifiedpart1-190908110738-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 The technology of fake news between a new front and a new frontier | Big Data Demystified /OmidVahdaty/the-technology-of-fake-news-between-a-new-front-and-a-new-frontier-big-data-demystified thetechnologyoffakenewsbetweenanewfrontandanewfrontier-190908080334
拽讜专讗讬诐 诇讬 谞讬爪谉 讗讜专 拽讚专讗讬 讜讗谞讬 注讜诪讚转 讘爪讜诪转 讛诪注谞讬讬谞转 砖讘讬谉 讟讻谞讜诇讜讙讬讛, 诪讚讬讛 讜讗拽讟讬讘讬讝诐. 讘讗专讘注 讜讞爪讬 讛砖谞讬诐 讛讗讞专讜谞讜转 讗谞讬 注讜讘讚转 讘讬讚讬注讜转 讗讞专讜谞讜转, 讘讛转讞诇讛 讻诪谞讛诇转 讛诪讜爪专 砖诇 讗驻诇讬拽爪讬讬转 ynet 讜讻讬讜诐 讻诪谞讛诇转 讛讞讚砖谞讜转. 讛讬讬转讬 砖讜转驻讛 讘讛拽诪转 注诪讜转转 住讟讗专讟-讗讞, 注诪讜转讛 讛诪住驻拽转 砖讬专讜转讬 驻讬转讜讞 讜诪讜爪专 注讘讜专 注诪讜转讜转 讗讞专讜转, 讜诇讗讞专讜谞讛 诪转注住拽转 讘讛拽诪转 拽讛讬诇讛 砖诪讟专转讛 诇讞拽讜专 讗转 讛讛讬讘讟讬诐 讛讟讻谞讜诇讜讙讬讬诐 砖诇 转讜驻注转 讛驻讬讬拽 谞讬讜讝 讜讘谞讬讬转 讻诇讬诐 讗驻诇讬拽讟讬讘讬讬诐 诇爪讜专讱 谞讬讛讜诇 讞讻诐 砖诇 讛诪诇讞诪讛 讘转讜驻注讛. 讛讛专爪讗讛 转讚讘专 注诇 转讜驻注转 讛驻讬讬拽 谞讬讜讝. 谞转诪拽讚 讘讟讻谞讜诇讜讙讬讛 砖诪讗驻砖专转 讗转 讛驻爪转 讛驻讬讬拽 谞讬讜讝 讜谞专讗讛 讚讜讙诪讗讜转 诇砖讬诪讜砖 讘讟讻谞讜诇讜讙讬讛 讝讜. 谞讘讞谉 讗转 讛讬拽祝 讛转讜驻注讛 讘专砖转讜转 讛讞讘专转讬讜转 讜谞诇诪讚 讗讬讱 注谞拽讬讜转 讛讟讻谞讜诇讜讙讬讛 诪谞住讜转 诇讛讬诇讞诐 讘讛. ]]>
拽讜专讗讬诐 诇讬 谞讬爪谉 讗讜专 拽讚专讗讬 讜讗谞讬 注讜诪讚转 讘爪讜诪转 讛诪注谞讬讬谞转 砖讘讬谉 讟讻谞讜诇讜讙讬讛, 诪讚讬讛 讜讗拽讟讬讘讬讝诐. 讘讗专讘注 讜讞爪讬 讛砖谞讬诐 讛讗讞专讜谞讜转 讗谞讬 注讜讘讚转 讘讬讚讬注讜转 讗讞专讜谞讜转, 讘讛转讞诇讛 讻诪谞讛诇转 讛诪讜爪专 砖诇 讗驻诇讬拽爪讬讬转 ynet 讜讻讬讜诐 讻诪谞讛诇转 讛讞讚砖谞讜转. 讛讬讬转讬 砖讜转驻讛 讘讛拽诪转 注诪讜转转 住讟讗专讟-讗讞, 注诪讜转讛 讛诪住驻拽转 砖讬专讜转讬 驻讬转讜讞 讜诪讜爪专 注讘讜专 注诪讜转讜转 讗讞专讜转, 讜诇讗讞专讜谞讛 诪转注住拽转 讘讛拽诪转 拽讛讬诇讛 砖诪讟专转讛 诇讞拽讜专 讗转 讛讛讬讘讟讬诐 讛讟讻谞讜诇讜讙讬讬诐 砖诇 转讜驻注转 讛驻讬讬拽 谞讬讜讝 讜讘谞讬讬转 讻诇讬诐 讗驻诇讬拽讟讬讘讬讬诐 诇爪讜专讱 谞讬讛讜诇 讞讻诐 砖诇 讛诪诇讞诪讛 讘转讜驻注讛. 讛讛专爪讗讛 转讚讘专 注诇 转讜驻注转 讛驻讬讬拽 谞讬讜讝. 谞转诪拽讚 讘讟讻谞讜诇讜讙讬讛 砖诪讗驻砖专转 讗转 讛驻爪转 讛驻讬讬拽 谞讬讜讝 讜谞专讗讛 讚讜讙诪讗讜转 诇砖讬诪讜砖 讘讟讻谞讜诇讜讙讬讛 讝讜. 谞讘讞谉 讗转 讛讬拽祝 讛转讜驻注讛 讘专砖转讜转 讛讞讘专转讬讜转 讜谞诇诪讚 讗讬讱 注谞拽讬讜转 讛讟讻谞讜诇讜讙讬讛 诪谞住讜转 诇讛讬诇讞诐 讘讛. ]]> Sun, 08 Sep 2019 08:03:34 GMT /OmidVahdaty/the-technology-of-fake-news-between-a-new-front-and-a-new-frontier-big-data-demystified OmidVahdaty@slideshare.net(OmidVahdaty) The technology of fake news between a new front and a new frontier | Big Data Demystified OmidVahdaty 拽讜专讗讬诐 诇讬 谞讬爪谉 讗讜专 拽讚专讗讬 讜讗谞讬 注讜诪讚转 讘爪讜诪转 讛诪注谞讬讬谞转 砖讘讬谉 讟讻谞讜诇讜讙讬讛, 诪讚讬讛 讜讗拽讟讬讘讬讝诐. 讘讗专讘注 讜讞爪讬 讛砖谞讬诐 讛讗讞专讜谞讜转 讗谞讬 注讜讘讚转 讘讬讚讬注讜转 讗讞专讜谞讜转, 讘讛转讞诇讛 讻诪谞讛诇转 讛诪讜爪专 砖诇 讗驻诇讬拽爪讬讬转 ynet 讜讻讬讜诐 讻诪谞讛诇转 讛讞讚砖谞讜转. 讛讬讬转讬 砖讜转驻讛 讘讛拽诪转 注诪讜转转 住讟讗专讟-讗讞, 注诪讜转讛 讛诪住驻拽转 砖讬专讜转讬 驻讬转讜讞 讜诪讜爪专 注讘讜专 注诪讜转讜转 讗讞专讜转, 讜诇讗讞专讜谞讛 诪转注住拽转 讘讛拽诪转 拽讛讬诇讛 砖诪讟专转讛 诇讞拽讜专 讗转 讛讛讬讘讟讬诐 讛讟讻谞讜诇讜讙讬讬诐 砖诇 转讜驻注转 讛驻讬讬拽 谞讬讜讝 讜讘谞讬讬转 讻诇讬诐 讗驻诇讬拽讟讬讘讬讬诐 诇爪讜专讱 谞讬讛讜诇 讞讻诐 砖诇 讛诪诇讞诪讛 讘转讜驻注讛. 讛讛专爪讗讛 转讚讘专 注诇 转讜驻注转 讛驻讬讬拽 谞讬讜讝. 谞转诪拽讚 讘讟讻谞讜诇讜讙讬讛 砖诪讗驻砖专转 讗转 讛驻爪转 讛驻讬讬拽 谞讬讜讝 讜谞专讗讛 讚讜讙诪讗讜转 诇砖讬诪讜砖 讘讟讻谞讜诇讜讙讬讛 讝讜. 谞讘讞谉 讗转 讛讬拽祝 讛转讜驻注讛 讘专砖转讜转 讛讞讘专转讬讜转 讜谞诇诪讚 讗讬讱 注谞拽讬讜转 讛讟讻谞讜诇讜讙讬讛 诪谞住讜转 诇讛讬诇讞诐 讘讛. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/thetechnologyoffakenewsbetweenanewfrontandanewfrontier-190908080334-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 拽讜专讗讬诐 诇讬 谞讬爪谉 讗讜专 拽讚专讗讬 讜讗谞讬 注讜诪讚转 讘爪讜诪转 讛诪注谞讬讬谞转 砖讘讬谉 讟讻谞讜诇讜讙讬讛, 诪讚讬讛 讜讗拽讟讬讘讬讝诐. 讘讗专讘注 讜讞爪讬 讛砖谞讬诐 讛讗讞专讜谞讜转 讗谞讬 注讜讘讚转 讘讬讚讬注讜转 讗讞专讜谞讜转, 讘讛转讞诇讛 讻诪谞讛诇转 讛诪讜爪专 砖诇 讗驻诇讬拽爪讬讬转 ynet 讜讻讬讜诐 讻诪谞讛诇转 讛讞讚砖谞讜转. 讛讬讬转讬 砖讜转驻讛 讘讛拽诪转 注诪讜转转 住讟讗专讟-讗讞, 注诪讜转讛 讛诪住驻拽转 砖讬专讜转讬 驻讬转讜讞 讜诪讜爪专 注讘讜专 注诪讜转讜转 讗讞专讜转, 讜诇讗讞专讜谞讛 诪转注住拽转 讘讛拽诪转 拽讛讬诇讛 砖诪讟专转讛 诇讞拽讜专 讗转 讛讛讬讘讟讬诐 讛讟讻谞讜诇讜讙讬讬诐 砖诇 转讜驻注转 讛驻讬讬拽 谞讬讜讝 讜讘谞讬讬转 讻诇讬诐 讗驻诇讬拽讟讬讘讬讬诐 诇爪讜专讱 谞讬讛讜诇 讞讻诐 砖诇 讛诪诇讞诪讛 讘转讜驻注讛. 讛讛专爪讗讛 转讚讘专 注诇 转讜驻注转 讛驻讬讬拽 谞讬讜讝. 谞转诪拽讚 讘讟讻谞讜诇讜讙讬讛 砖诪讗驻砖专转 讗转 讛驻爪转 讛驻讬讬拽 谞讬讜讝 讜谞专讗讛 讚讜讙诪讗讜转 诇砖讬诪讜砖 讘讟讻谞讜诇讜讙讬讛 讝讜. 谞讘讞谉 讗转 讛讬拽祝 讛转讜驻注讛 讘专砖转讜转 讛讞讘专转讬讜转 讜谞诇诪讚 讗讬讱 注谞拽讬讜转 讛讟讻谞讜诇讜讙讬讛 诪谞住讜转 诇讛讬诇讞诐 讘讛.
The technology of fake news between a new front and a new frontier | Big Data Demystified from Omid Vahdaty
]]> 238 2 https://cdn.slidesharecdn.com/ss_thumbnails/thetechnologyoffakenewsbetweenanewfrontandanewfrontier-190908080334-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Big Data in 200 km/h | AWS Big Data Demystified #1.3 /slideshow/big-data-in-200-kmh-aws-big-data-demystified-13/160888500 bigdatain200kmhawsbigdatademystified1-190804061539
What we're about A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry鈥 Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS infrastructure to answer the basic questions of anyone starting their way in the big data world. how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORCwhich technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL?how to handle streaming?how to manage costs?Performance tips?Security tip?Cloud best practices tips? Some of our online materials: Website: https://big-data-demystified.ninja/ Youtube channels: https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber Meetup: https://www.meetup.com/AWS-Big-Data-Demystified/ https://www.meetup.com/Big-Data-Demystified Facebook Group : https://www.facebook.com/groups/amazon.aws.big.data.demystified/ Facebook page (https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/) Audience: Data Engineers Data Science DevOps Engineers Big Data Architects Solution Architects CTO VP R&D]]>
What we're about A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry鈥 Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS infrastructure to answer the basic questions of anyone starting their way in the big data world. how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORCwhich technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL?how to handle streaming?how to manage costs?Performance tips?Security tip?Cloud best practices tips? Some of our online materials: Website: https://big-data-demystified.ninja/ Youtube channels: https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber Meetup: https://www.meetup.com/AWS-Big-Data-Demystified/ https://www.meetup.com/Big-Data-Demystified Facebook Group : https://www.facebook.com/groups/amazon.aws.big.data.demystified/ Facebook page (https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/) Audience: Data Engineers Data Science DevOps Engineers Big Data Architects Solution Architects CTO VP R&D]]> Sun, 04 Aug 2019 06:15:39 GMT /slideshow/big-data-in-200-kmh-aws-big-data-demystified-13/160888500 OmidVahdaty@slideshare.net(OmidVahdaty) Big Data in 200 km/h | AWS Big Data Demystified #1.3 OmidVahdaty What we're about A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry鈥 Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS infrastructure to answer the basic questions of anyone starting their way in the big data world. how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORCwhich technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL?how to handle streaming?how to manage costs?Performance tips?Security tip?Cloud best practices tips? Some of our online materials: Website: https://big-data-demystified.ninja/ Youtube channels: https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber Meetup: https://www.meetup.com/AWS-Big-Data-Demystified/ https://www.meetup.com/Big-Data-Demystified Facebook Group : https://www.facebook.com/groups/amazon.aws.big.data.demystified/ Facebook page (https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/) Audience: Data Engineers Data Science DevOps Engineers Big Data Architects Solution Architects CTO VP R&D <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatain200kmhawsbigdatademystified1-190804061539-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> What we&#39;re about A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry鈥 Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS infrastructure to answer the basic questions of anyone starting their way in the big data world. how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORCwhich technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL?how to handle streaming?how to manage costs?Performance tips?Security tip?Cloud best practices tips? Some of our online materials: Website: https://big-data-demystified.ninja/ Youtube channels: https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber Meetup: https://www.meetup.com/AWS-Big-Data-Demystified/ https://www.meetup.com/Big-Data-Demystified Facebook Group : https://www.facebook.com/groups/amazon.aws.big.data.demystified/ Facebook page (https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/) Audience: Data Engineers Data Science DevOps Engineers Big Data Architects Solution Architects CTO VP R&amp;D
Big Data in 200 km/h | AWS Big Data Demystified #1.3 from Omid Vahdaty
]]> 486 5 https://cdn.slidesharecdn.com/ss_thumbnails/bigdatain200kmhawsbigdatademystified1-190804061539-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Making your analytics talk business | Big Data Demystified /slideshow/making-your-analytics-talk-business-big-data-demystified/157228988 makingyouranalyticstalkbusinessjul2019-190723090818
MAKING YOUR ANALYTICS TALK BUSINESS Aligning your analysis to the business is fundamental for all types of analytics (digital or product analytics, business intelligence, etc) and is vertical- and tool agnostic. In this talk we will build on the discussion that was started in the previous meetup, and will discuss how analysts can learn to derive their stakeholders' expectations, how to shift from metrics to "real" KPIs, and how to approach an analysis in order to create real impact. This session is primarily geared towards those starting out into analytics, practitioners who feel that they are still struggling to prove their value in the organization or simply folks who want to power up their reporting and recommendation skills. If you are already a master at aligning your analysis to the business, you're most welcome as well: join us to share your experiences so that we can all learn from each other and improve! Bios: Eliza Savov - Eliza is the team lead of the Customer Experience and Analytics team at Clicktale, the worldwide leader in behavioral analytics. She has extensive experience working with data analytics, having previously worked at Clicktale as a senior customer experience analyst, and as a product analyst at Seeking Alpha. ]]>
MAKING YOUR ANALYTICS TALK BUSINESS Aligning your analysis to the business is fundamental for all types of analytics (digital or product analytics, business intelligence, etc) and is vertical- and tool agnostic. In this talk we will build on the discussion that was started in the previous meetup, and will discuss how analysts can learn to derive their stakeholders' expectations, how to shift from metrics to "real" KPIs, and how to approach an analysis in order to create real impact. This session is primarily geared towards those starting out into analytics, practitioners who feel that they are still struggling to prove their value in the organization or simply folks who want to power up their reporting and recommendation skills. If you are already a master at aligning your analysis to the business, you're most welcome as well: join us to share your experiences so that we can all learn from each other and improve! Bios: Eliza Savov - Eliza is the team lead of the Customer Experience and Analytics team at Clicktale, the worldwide leader in behavioral analytics. She has extensive experience working with data analytics, having previously worked at Clicktale as a senior customer experience analyst, and as a product analyst at Seeking Alpha. ]]> Tue, 23 Jul 2019 09:08:18 GMT /slideshow/making-your-analytics-talk-business-big-data-demystified/157228988 OmidVahdaty@slideshare.net(OmidVahdaty) Making your analytics talk business | Big Data Demystified OmidVahdaty MAKING YOUR ANALYTICS TALK BUSINESS Aligning your analysis to the business is fundamental for all types of analytics (digital or product analytics, business intelligence, etc) and is vertical- and tool agnostic. In this talk we will build on the discussion that was started in the previous meetup, and will discuss how analysts can learn to derive their stakeholders' expectations, how to shift from metrics to "real" KPIs, and how to approach an analysis in order to create real impact. This session is primarily geared towards those starting out into analytics, practitioners who feel that they are still struggling to prove their value in the organization or simply folks who want to power up their reporting and recommendation skills. If you are already a master at aligning your analysis to the business, you're most welcome as well: join us to share your experiences so that we can all learn from each other and improve! Bios: Eliza Savov - Eliza is the team lead of the Customer Experience and Analytics team at Clicktale, the worldwide leader in behavioral analytics. She has extensive experience working with data analytics, having previously worked at Clicktale as a senior customer experience analyst, and as a product analyst at Seeking Alpha. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/makingyouranalyticstalkbusinessjul2019-190723090818-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> MAKING YOUR ANALYTICS TALK BUSINESS Aligning your analysis to the business is fundamental for all types of analytics (digital or product analytics, business intelligence, etc) and is vertical- and tool agnostic. In this talk we will build on the discussion that was started in the previous meetup, and will discuss how analysts can learn to derive their stakeholders&#39; expectations, how to shift from metrics to &quot;real&quot; KPIs, and how to approach an analysis in order to create real impact. This session is primarily geared towards those starting out into analytics, practitioners who feel that they are still struggling to prove their value in the organization or simply folks who want to power up their reporting and recommendation skills. If you are already a master at aligning your analysis to the business, you&#39;re most welcome as well: join us to share your experiences so that we can all learn from each other and improve! Bios: Eliza Savov - Eliza is the team lead of the Customer Experience and Analytics team at Clicktale, the worldwide leader in behavioral analytics. She has extensive experience working with data analytics, having previously worked at Clicktale as a senior customer experience analyst, and as a product analyst at Seeking Alpha.
Making your analytics talk business | Big Data Demystified from Omid Vahdaty
]]> 356 8 https://cdn.slidesharecdn.com/ss_thumbnails/makingyouranalyticstalkbusinessjul2019-190723090818-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 BI STRATEGY FROM A BIRD'S EYE VIEW (How to become a trusted advisor) | Omri Halak Director of Business Operations | Logz.io /slideshow/bi-strategy-from-a-birds-eye-view-how-to-become-a-trusted-advisor-omri-halak-director-of-business-operations-logzio/157228448 bistrategy-190723090252
In the talk we will discuss how to break down the company鈥檚 overall goals all the way to your BI team鈥檚 daily activities in 3 simple stages: 1. Understanding the path to success - Creating a revenue model 2. Gathering support and strategizing - Structuring a team 3. Executing - Tracking KPIs Bios: Omri Halak -Omri is the director of business operations at Logz.io, an intelligent and scalable machine data analytics platform built on ELK & Grafana that empowers engineers to monitor, troubleshoot, and secure mission-critical applications more effectively. In this position, Omri combines actionable business insights from the BI side with fast and effective delivery on the Operations side. Omri has ample experience connecting data with business, with previous positions at SimilarWeb as a business analyst, at Woobi as finance director, and as Head of State Guarantees at Israel Ministry of Finance.]]>
In the talk we will discuss how to break down the company鈥檚 overall goals all the way to your BI team鈥檚 daily activities in 3 simple stages: 1. Understanding the path to success - Creating a revenue model 2. Gathering support and strategizing - Structuring a team 3. Executing - Tracking KPIs Bios: Omri Halak -Omri is the director of business operations at Logz.io, an intelligent and scalable machine data analytics platform built on ELK & Grafana that empowers engineers to monitor, troubleshoot, and secure mission-critical applications more effectively. In this position, Omri combines actionable business insights from the BI side with fast and effective delivery on the Operations side. Omri has ample experience connecting data with business, with previous positions at SimilarWeb as a business analyst, at Woobi as finance director, and as Head of State Guarantees at Israel Ministry of Finance.]]> Tue, 23 Jul 2019 09:02:52 GMT /slideshow/bi-strategy-from-a-birds-eye-view-how-to-become-a-trusted-advisor-omri-halak-director-of-business-operations-logzio/157228448 OmidVahdaty@slideshare.net(OmidVahdaty) BI STRATEGY FROM A BIRD'S EYE VIEW (How to become a trusted advisor) | Omri Halak Director of Business Operations | Logz.io OmidVahdaty In the talk we will discuss how to break down the company鈥檚 overall goals all the way to your BI team鈥檚 daily activities in 3 simple stages: 1. Understanding the path to success - Creating a revenue model 2. Gathering support and strategizing - Structuring a team 3. Executing - Tracking KPIs Bios: Omri Halak -Omri is the director of business operations at Logz.io, an intelligent and scalable machine data analytics platform built on ELK & Grafana that empowers engineers to monitor, troubleshoot, and secure mission-critical applications more effectively. In this position, Omri combines actionable business insights from the BI side with fast and effective delivery on the Operations side. Omri has ample experience connecting data with business, with previous positions at SimilarWeb as a business analyst, at Woobi as finance director, and as Head of State Guarantees at Israel Ministry of Finance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bistrategy-190723090252-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In the talk we will discuss how to break down the company鈥檚 overall goals all the way to your BI team鈥檚 daily activities in 3 simple stages: 1. Understanding the path to success - Creating a revenue model 2. Gathering support and strategizing - Structuring a team 3. Executing - Tracking KPIs Bios: Omri Halak -Omri is the director of business operations at Logz.io, an intelligent and scalable machine data analytics platform built on ELK &amp; Grafana that empowers engineers to monitor, troubleshoot, and secure mission-critical applications more effectively. In this position, Omri combines actionable business insights from the BI side with fast and effective delivery on the Operations side. Omri has ample experience connecting data with business, with previous positions at SimilarWeb as a business analyst, at Woobi as finance director, and as Head of State Guarantees at Israel Ministry of Finance.
BI STRATEGY FROM A BIRD'S EYE VIEW (How to become a trusted advisor) | Omri Halak Director of Business Operations | Logz.io from Omid Vahdaty
]]> 387 7 https://cdn.slidesharecdn.com/ss_thumbnails/bistrategy-190723090252-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AI and Big Data in Health Sector Opportunities and challenges | Big Data Demystified /slideshow/ai-and-big-data-in-health-sector-opportunities-and-challenges-big-data-demystified/153352471 bigdatainhealthcare2-190703134939
Lecturer has Deep experience defining Cloud computing, security models for IaaS, PaaS, and SaaS architectures specifically as the architecture relates to IAM. Deep Experience Defining Privacy protection Policy, a big fan of GDPR interpretation. DeelExperience in Information security, Defining Healthcare security best practices including AI and Big Data, IT Security and ICS security and privacy controls in the industrial environments. Deep knowledge of security frameworks such as Cloud Security Alliance (CSA), International Organization for Standardization (ISO), National Institute of Standards and Technology (NIST), IBM ITCS104 etc. What Will You learn: Every day, the website collects a huge amount of data. The data allows to analyze the behavior of Internet users, their interests, their purchasing behavior and the conversion rates. In order to increase business, big data offers the tools to analyze and process data in order to reveal competitive advantages from the data. What Healthcare has to do with Big Data How AI can assist in patient care? Why some are afraid? Are there any dangers?]]>
Lecturer has Deep experience defining Cloud computing, security models for IaaS, PaaS, and SaaS architectures specifically as the architecture relates to IAM. Deep Experience Defining Privacy protection Policy, a big fan of GDPR interpretation. DeelExperience in Information security, Defining Healthcare security best practices including AI and Big Data, IT Security and ICS security and privacy controls in the industrial environments. Deep knowledge of security frameworks such as Cloud Security Alliance (CSA), International Organization for Standardization (ISO), National Institute of Standards and Technology (NIST), IBM ITCS104 etc. What Will You learn: Every day, the website collects a huge amount of data. The data allows to analyze the behavior of Internet users, their interests, their purchasing behavior and the conversion rates. In order to increase business, big data offers the tools to analyze and process data in order to reveal competitive advantages from the data. What Healthcare has to do with Big Data How AI can assist in patient care? Why some are afraid? Are there any dangers?]]> Wed, 03 Jul 2019 13:49:39 GMT /slideshow/ai-and-big-data-in-health-sector-opportunities-and-challenges-big-data-demystified/153352471 OmidVahdaty@slideshare.net(OmidVahdaty) AI and Big Data in Health Sector Opportunities and challenges | Big Data Demystified OmidVahdaty Lecturer has Deep experience defining Cloud computing, security models for IaaS, PaaS, and SaaS architectures specifically as the architecture relates to IAM. Deep Experience Defining Privacy protection Policy, a big fan of GDPR interpretation. DeelExperience in Information security, Defining Healthcare security best practices including AI and Big Data, IT Security and ICS security and privacy controls in the industrial environments. Deep knowledge of security frameworks such as Cloud Security Alliance (CSA), International Organization for Standardization (ISO), National Institute of Standards and Technology (NIST), IBM ITCS104 etc. What Will You learn: Every day, the website collects a huge amount of data. The data allows to analyze the behavior of Internet users, their interests, their purchasing behavior and the conversion rates. In order to increase business, big data offers the tools to analyze and process data in order to reveal competitive advantages from the data. What Healthcare has to do with Big Data How AI can assist in patient care? Why some are afraid? Are there any dangers? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatainhealthcare2-190703134939-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Lecturer has Deep experience defining Cloud computing, security models for IaaS, PaaS, and SaaS architectures specifically as the architecture relates to IAM. Deep Experience Defining Privacy protection Policy, a big fan of GDPR interpretation. DeelExperience in Information security, Defining Healthcare security best practices including AI and Big Data, IT Security and ICS security and privacy controls in the industrial environments. Deep knowledge of security frameworks such as Cloud Security Alliance (CSA), International Organization for Standardization (ISO), National Institute of Standards and Technology (NIST), IBM ITCS104 etc. What Will You learn: Every day, the website collects a huge amount of data. The data allows to analyze the behavior of Internet users, their interests, their purchasing behavior and the conversion rates. In order to increase business, big data offers the tools to analyze and process data in order to reveal competitive advantages from the data. What Healthcare has to do with Big Data How AI can assist in patient care? Why some are afraid? Are there any dangers?
AI and Big Data in Health Sector Opportunities and challenges | Big Data Demystified from Omid Vahdaty
]]> 1060 4 https://cdn.slidesharecdn.com/ss_thumbnails/bigdatainhealthcare2-190703134939-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Aerospike meetup july 2019 | Big Data Demystified /slideshow/aerospike-meetup-july-2019-big-data-demystified/153128898 aeropsikemeetupjuly2019-190702113937
Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway... In this session we will get to know Aerospike, an enterprise distributed primary key database solution. - We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments. - We will understand the 'magic' behind Aerospike ability to handle small, medium and even Petabyte scale data, and still guarantee predictable performance of sub-millisecond latency - We will learn how Aerospike devops is different than other solutions in the market, and see how easy it is to run it on cloud environments as well as on premise. We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer.]]>
Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway... In this session we will get to know Aerospike, an enterprise distributed primary key database solution. - We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments. - We will understand the 'magic' behind Aerospike ability to handle small, medium and even Petabyte scale data, and still guarantee predictable performance of sub-millisecond latency - We will learn how Aerospike devops is different than other solutions in the market, and see how easy it is to run it on cloud environments as well as on premise. We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer.]]> Tue, 02 Jul 2019 11:39:37 GMT /slideshow/aerospike-meetup-july-2019-big-data-demystified/153128898 OmidVahdaty@slideshare.net(OmidVahdaty) Aerospike meetup july 2019 | Big Data Demystified OmidVahdaty Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway... In this session we will get to know Aerospike, an enterprise distributed primary key database solution. - We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments. - We will understand the 'magic' behind Aerospike ability to handle small, medium and even Petabyte scale data, and still guarantee predictable performance of sub-millisecond latency - We will learn how Aerospike devops is different than other solutions in the market, and see how easy it is to run it on cloud environments as well as on premise. We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aeropsikemeetupjuly2019-190702113937-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway... In this session we will get to know Aerospike, an enterprise distributed primary key database solution. - We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments. - We will understand the &#39;magic&#39; behind Aerospike ability to handle small, medium and even Petabyte scale data, and still guarantee predictable performance of sub-millisecond latency - We will learn how Aerospike devops is different than other solutions in the market, and see how easy it is to run it on cloud environments as well as on premise. We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer.
Aerospike meetup july 2019 | Big Data Demystified from Omid Vahdaty
]]> 296 2 https://cdn.slidesharecdn.com/ss_thumbnails/aeropsikemeetupjuly2019-190702113937-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS' UNSPOKEN NEEDS, by Eyal Steiner, Senior BI Engineer, Alexa Shopping at Amazon /slideshow/aligning-your-bi-operations-with-your-customers-unspoken-needs-by-eyal-steiner-senior-bi-engineer-alexa-shopping-at-amazon/149380354 bimeetup20190602-190613064255
ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS' UNSPOKEN NEEDS -Learn how to connect BI and product management to solve business problems -Discover how to lead clients to ask the right questions to get the data and insight they really want -Get pointers on saving your time and your company's resources by understanding what your customers need, not what they ask for]]>
ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS' UNSPOKEN NEEDS -Learn how to connect BI and product management to solve business problems -Discover how to lead clients to ask the right questions to get the data and insight they really want -Get pointers on saving your time and your company's resources by understanding what your customers need, not what they ask for]]> Thu, 13 Jun 2019 06:42:55 GMT /slideshow/aligning-your-bi-operations-with-your-customers-unspoken-needs-by-eyal-steiner-senior-bi-engineer-alexa-shopping-at-amazon/149380354 OmidVahdaty@slideshare.net(OmidVahdaty) ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS' UNSPOKEN NEEDS, by Eyal Steiner, Senior BI Engineer, Alexa Shopping at Amazon OmidVahdaty ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS' UNSPOKEN NEEDS -Learn how to connect BI and product management to solve business problems -Discover how to lead clients to ask the right questions to get the data and insight they really want -Get pointers on saving your time and your company's resources by understanding what your customers need, not what they ask for <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bimeetup20190602-190613064255-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS&#39; UNSPOKEN NEEDS -Learn how to connect BI and product management to solve business problems -Discover how to lead clients to ask the right questions to get the data and insight they really want -Get pointers on saving your time and your company&#39;s resources by understanding what your customers need, not what they ask for
ALIGNING YOUR BI OPERATIONS WITH YOUR CUSTOMERS' UNSPOKEN NEEDS, by Eyal Steiner, Senior BI Engineer, Alexa Shopping at Amazon from Omid Vahdaty
]]> 143 1 https://cdn.slidesharecdn.com/ss_thumbnails/bimeetup20190602-190613064255-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AWS Big Data Demystified #1.2 | Big Data architecture lessons learned /slideshow/aws-big-data-demystified-12-big-data-architecture-lessons-learned/139231780 aws-big-data-demystified1-190402122418
A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry鈥 Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS & GCP and Data Center infrastructure to answer the basic questions of anyone starting their way in the big data world. how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORC,AVRO which technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL? GCS? Big Query? Data flow? Data Lab? tensor flow? how to handle streaming? how to manage costs? Performance tips? Security tip? Cloud best practices tips? In this meetup we shall present lecturers working on several cloud vendors, various big data platforms such hadoop, Data warehourses , startups working on big data products. basically - if it is related to big data - this is THE meetup. Some of our online materials (mixed content from several cloud vendor): Website: https://big-data-demystified.ninja (under construction) Meetups: https://www.meetup.com/Big-Data-Demystified https://www.meetup.com/AWS-Big-Data-Demystified/ You tube channels: https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber Audience: Data Engineers Data Science DevOps Engineers Big Data Architects Solution Architects CTO VP R&D]]>
A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry鈥 Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS & GCP and Data Center infrastructure to answer the basic questions of anyone starting their way in the big data world. how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORC,AVRO which technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL? GCS? Big Query? Data flow? Data Lab? tensor flow? how to handle streaming? how to manage costs? Performance tips? Security tip? Cloud best practices tips? In this meetup we shall present lecturers working on several cloud vendors, various big data platforms such hadoop, Data warehourses , startups working on big data products. basically - if it is related to big data - this is THE meetup. Some of our online materials (mixed content from several cloud vendor): Website: https://big-data-demystified.ninja (under construction) Meetups: https://www.meetup.com/Big-Data-Demystified https://www.meetup.com/AWS-Big-Data-Demystified/ You tube channels: https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber Audience: Data Engineers Data Science DevOps Engineers Big Data Architects Solution Architects CTO VP R&D]]> Tue, 02 Apr 2019 12:24:17 GMT /slideshow/aws-big-data-demystified-12-big-data-architecture-lessons-learned/139231780 OmidVahdaty@slideshare.net(OmidVahdaty) AWS Big Data Demystified #1.2 | Big Data architecture lessons learned OmidVahdaty A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry鈥 Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS & GCP and Data Center infrastructure to answer the basic questions of anyone starting their way in the big data world. how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORC,AVRO which technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL? GCS? Big Query? Data flow? Data Lab? tensor flow? how to handle streaming? how to manage costs? Performance tips? Security tip? Cloud best practices tips? In this meetup we shall present lecturers working on several cloud vendors, various big data platforms such hadoop, Data warehourses , startups working on big data products. basically - if it is related to big data - this is THE meetup. Some of our online materials (mixed content from several cloud vendor): Website: https://big-data-demystified.ninja (under construction) Meetups: https://www.meetup.com/Big-Data-Demystified https://www.meetup.com/AWS-Big-Data-Demystified/ You tube channels: https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber Audience: Data Engineers Data Science DevOps Engineers Big Data Architects Solution Architects CTO VP R&D <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aws-big-data-demystified1-190402122418-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> A while ago I entered the challenging world of Big Data. As an engineer, at first, I was not so impressed with this field. As time went by, I realised more and more, The technological challenges in this area are too great to master by one person. Just look at the picture in this articles, it only covers a small fraction of the technologies in the Big Data industry鈥 Consequently, I created a meetup detailing all the challenges of Big Data, especially in the world of cloud. I am using AWS &amp; GCP and Data Center infrastructure to answer the basic questions of anyone starting their way in the big data world. how to transform data (TXT, CSV, TSV, JSON) into Parquet, ORC,AVRO which technology should we use to model the data ? EMR? Athena? Redshift? Spectrum? Glue? Spark? SparkSQL? GCS? Big Query? Data flow? Data Lab? tensor flow? how to handle streaming? how to manage costs? Performance tips? Security tip? Cloud best practices tips? In this meetup we shall present lecturers working on several cloud vendors, various big data platforms such hadoop, Data warehourses , startups working on big data products. basically - if it is related to big data - this is THE meetup. Some of our online materials (mixed content from several cloud vendor): Website: https://big-data-demystified.ninja (under construction) Meetups: https://www.meetup.com/Big-Data-Demystified https://www.meetup.com/AWS-Big-Data-Demystified/ You tube channels: https://www.youtube.com/channel/UCMSdNB0fGmX5dXI7S7Y_LFA?view_as=subscriber https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber Audience: Data Engineers Data Science DevOps Engineers Big Data Architects Solution Architects CTO VP R&amp;D
AWS Big Data Demystified #1.2 | Big Data architecture lessons learned from Omid Vahdaty
]]> 1187 3 https://cdn.slidesharecdn.com/ss_thumbnails/aws-big-data-demystified1-190402122418-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AWS big-data-demystified #1.1 | Big Data Architecture Lessons Learned | English /slideshow/aws-bigdatademystified-11-big-data-architecture-lessons-learned-english/114985376 aws-big-data-demystified1-180917124824
Aws big-data-demystified #1.1 | Big Data Architecture Lessons Learned | spoken Language: English. AWS. Big Data. Architecture. Hadoop. Open Source. EMR. ]]>
Aws big-data-demystified #1.1 | Big Data Architecture Lessons Learned | spoken Language: English. AWS. Big Data. Architecture. Hadoop. Open Source. EMR. ]]> Mon, 17 Sep 2018 12:48:24 GMT /slideshow/aws-bigdatademystified-11-big-data-architecture-lessons-learned-english/114985376 OmidVahdaty@slideshare.net(OmidVahdaty) AWS big-data-demystified #1.1 | Big Data Architecture Lessons Learned | English OmidVahdaty Aws big-data-demystified #1.1 | Big Data Architecture Lessons Learned | spoken Language: English. AWS. Big Data. Architecture. Hadoop. Open Source. EMR. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aws-big-data-demystified1-180917124824-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Aws big-data-demystified #1.1 | Big Data Architecture Lessons Learned | spoken Language: English. AWS. Big Data. Architecture. Hadoop. Open Source. EMR.
AWS big-data-demystified #1.1 | Big Data Architecture Lessons Learned | English from Omid Vahdaty
]]> 1153 4 https://cdn.slidesharecdn.com/ss_thumbnails/aws-big-data-demystified1-180917124824-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AWS Big Data Demystified #4 data governance demystified [security, network and data access management] /slideshow/aws-big-data-demystified-4-data-governance-demystified-security-network-amp-data-access-management/108731363 awsbigdatademystified4datagovernancedemystifiedsecuritynetworkdataaccessmanagement-180806053240
aws big data. compliance, network, security, account segregation.]]>
aws big data. compliance, network, security, account segregation.]]> Mon, 06 Aug 2018 05:32:40 GMT /slideshow/aws-big-data-demystified-4-data-governance-demystified-security-network-amp-data-access-management/108731363 OmidVahdaty@slideshare.net(OmidVahdaty) AWS Big Data Demystified #4 data governance demystified [security, network and data access management] OmidVahdaty aws big data. compliance, network, security, account segregation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/awsbigdatademystified4datagovernancedemystifiedsecuritynetworkdataaccessmanagement-180806053240-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> aws big data. compliance, network, security, account segregation.
AWS Big Data Demystified #4 data governance demystified [security, network and data access management] from Omid Vahdaty
]]> 666 4 https://cdn.slidesharecdn.com/ss_thumbnails/awsbigdatademystified4datagovernancedemystifiedsecuritynetworkdataaccessmanagement-180806053240-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AWS Big Data Demystified #3 | Zeppelin + spark sql, jdbc + thrift, ganglia, r+ spark r + livy /slideshow/aws-big-data-demystified-3-zeppelin-spark-sql-jdbc-thrift-ganglia-r-spark-r-livy/103910088 awsbigdatademystified3zeppelinsparksqljdbcthriftgangliarsparkrlivy-180702065139
AWS Big Data Demystified is all about knowledge sharing b/c knowledge should be given for free. in this lecture we will dicusss the advantages of working with Zeppelin + spark sql, jdbc + thrift, ganglia, r+ spark r + livy, and a litte bit about ganglia on EMR.\ subscribe to you youtube channel to see the video of this lecture: https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber]]>
AWS Big Data Demystified is all about knowledge sharing b/c knowledge should be given for free. in this lecture we will dicusss the advantages of working with Zeppelin + spark sql, jdbc + thrift, ganglia, r+ spark r + livy, and a litte bit about ganglia on EMR.\ subscribe to you youtube channel to see the video of this lecture: https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber]]> Mon, 02 Jul 2018 06:51:39 GMT /slideshow/aws-big-data-demystified-3-zeppelin-spark-sql-jdbc-thrift-ganglia-r-spark-r-livy/103910088 OmidVahdaty@slideshare.net(OmidVahdaty) AWS Big Data Demystified #3 | Zeppelin + spark sql, jdbc + thrift, ganglia, r+ spark r + livy OmidVahdaty AWS Big Data Demystified is all about knowledge sharing b/c knowledge should be given for free. in this lecture we will dicusss the advantages of working with Zeppelin + spark sql, jdbc + thrift, ganglia, r+ spark r + livy, and a litte bit about ganglia on EMR.\ subscribe to you youtube channel to see the video of this lecture: https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/awsbigdatademystified3zeppelinsparksqljdbcthriftgangliarsparkrlivy-180702065139-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> AWS Big Data Demystified is all about knowledge sharing b/c knowledge should be given for free. in this lecture we will dicusss the advantages of working with Zeppelin + spark sql, jdbc + thrift, ganglia, r+ spark r + livy, and a litte bit about ganglia on EMR.\ subscribe to you youtube channel to see the video of this lecture: https://www.youtube.com/channel/UCzeGqhZIWU-hIDczWa8GtgQ?view_as=subscriber
AWS Big Data Demystified #3 | Zeppelin + spark sql, jdbc + thrift, ganglia, r+ spark r + livy from Omid Vahdaty
]]> 690 4 https://cdn.slidesharecdn.com/ss_thumbnails/awsbigdatademystified3zeppelinsparksqljdbcthriftgangliarsparkrlivy-180702065139-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive /slideshow/aws-big-data-demystified-2-athena-spectrum-emr-hive/100327184 awsbigdatademystified2athenaspectrumemrhive-180603203855
AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive | Omid Vahdaty | Big Data Ninja]]>
AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive | Omid Vahdaty | Big Data Ninja]]> Sun, 03 Jun 2018 20:38:55 GMT /slideshow/aws-big-data-demystified-2-athena-spectrum-emr-hive/100327184 OmidVahdaty@slideshare.net(OmidVahdaty) AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive OmidVahdaty AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive | Omid Vahdaty | Big Data Ninja <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/awsbigdatademystified2athenaspectrumemrhive-180603203855-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive | Omid Vahdaty | Big Data Ninja
AWS Big Data Demystified #2 | Athena, Spectrum, Emr, Hive from Omid Vahdaty
]]> 2261 6 https://cdn.slidesharecdn.com/ss_thumbnails/awsbigdatademystified2athenaspectrumemrhive-180603203855-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Amazon aws big data demystified | Introduction to streaming and messaging flume kafka sqs kinesis /slideshow/amazon-aws-big-data-demystified-introduction-to-streaming-and-messaging-flume-kafka-sqs-kinesis/98090932 amazonawsbigdatademystifiedintroductiontostreamingmessagingflumekafkasqskinesis-180522151011
amazon aws big data demystified meetup: https://www.meetup.com/AWS-Big-Data-Demystified/ Introduction to streaming and messaging flume kafka sqs kinesis]]>
amazon aws big data demystified meetup: https://www.meetup.com/AWS-Big-Data-Demystified/ Introduction to streaming and messaging flume kafka sqs kinesis]]> Tue, 22 May 2018 15:10:11 GMT /slideshow/amazon-aws-big-data-demystified-introduction-to-streaming-and-messaging-flume-kafka-sqs-kinesis/98090932 OmidVahdaty@slideshare.net(OmidVahdaty) Amazon aws big data demystified | Introduction to streaming and messaging flume kafka sqs kinesis OmidVahdaty amazon aws big data demystified meetup: https://www.meetup.com/AWS-Big-Data-Demystified/ Introduction to streaming and messaging flume kafka sqs kinesis <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/amazonawsbigdatademystifiedintroductiontostreamingmessagingflumekafkasqskinesis-180522151011-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> amazon aws big data demystified meetup: https://www.meetup.com/AWS-Big-Data-Demystified/ Introduction to streaming and messaging flume kafka sqs kinesis
Amazon aws big data demystified | Introduction to streaming and messaging flume kafka sqs kinesis from Omid Vahdaty
]]> 487 2 https://cdn.slidesharecdn.com/ss_thumbnails/amazonawsbigdatademystifiedintroductiontostreamingmessagingflumekafkasqskinesis-180522151011-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AWS Big Data Demystified #1: Big data architecture lessons learned /OmidVahdaty/aws-big-data-demystified-1-big-data-architecture-lessons-learned bigdataarchitecturelessonslearned-180507062621
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company The video: https://youtu.be/l5KmaZNQxaU dont forget to subcribe to the youtube channel The website: https://amazon-aws-big-data-demystified.ninja/ The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/ The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/ ]]>
AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company The video: https://youtu.be/l5KmaZNQxaU dont forget to subcribe to the youtube channel The website: https://amazon-aws-big-data-demystified.ninja/ The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/ The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/ ]]> Mon, 07 May 2018 06:26:21 GMT /OmidVahdaty/aws-big-data-demystified-1-big-data-architecture-lessons-learned OmidVahdaty@slideshare.net(OmidVahdaty) AWS Big Data Demystified #1: Big data architecture lessons learned OmidVahdaty AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company The video: https://youtu.be/l5KmaZNQxaU dont forget to subcribe to the youtube channel The website: https://amazon-aws-big-data-demystified.ninja/ The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/ The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdataarchitecturelessonslearned-180507062621-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> AWS Big Data Demystified #1: Big data architecture lessons learned . a quick overview of a big data techonoligies, which were selected and disregard in our company The video: https://youtu.be/l5KmaZNQxaU dont forget to subcribe to the youtube channel The website: https://amazon-aws-big-data-demystified.ninja/ The meetup : https://www.meetup.com/AWS-Big-Data-Demystified/ The facebook group : https://www.facebook.com/Amazon-AWS-Big-Data-Demystified-1832900280345700/
AWS Big Data Demystified #1: Big data architecture lessons learned from Omid Vahdaty
]]> 1688 3 https://cdn.slidesharecdn.com/ss_thumbnails/bigdataarchitecturelessonslearned-180507062621-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Emr spark tuning demystified /slideshow/emr-spark-tuning-demystified/89802263 emrsparktuningdemystified-180306145601
AWS EMR hadoop spark tuning performance theory explained ]]>
AWS EMR hadoop spark tuning performance theory explained ]]> Tue, 06 Mar 2018 14:56:01 GMT /slideshow/emr-spark-tuning-demystified/89802263 OmidVahdaty@slideshare.net(OmidVahdaty) Emr spark tuning demystified OmidVahdaty AWS EMR hadoop spark tuning performance theory explained <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/emrsparktuningdemystified-180306145601-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> AWS EMR hadoop spark tuning performance theory explained
Emr spark tuning demystified from Omid Vahdaty
]]> 7958 3 https://cdn.slidesharecdn.com/ss_thumbnails/emrsparktuningdemystified-180306145601-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Emr zeppelin & Livy demystified /slideshow/emr-zeppelin-livy-demystified/86985555 emrzeppelindemystified-180131171046
AWS EMR zeppelin and livy demystified]]>
AWS EMR zeppelin and livy demystified]]> Wed, 31 Jan 2018 17:10:46 GMT /slideshow/emr-zeppelin-livy-demystified/86985555 OmidVahdaty@slideshare.net(OmidVahdaty) Emr zeppelin & Livy demystified OmidVahdaty AWS EMR zeppelin and livy demystified <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/emrzeppelindemystified-180131171046-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> AWS EMR zeppelin and livy demystified
Emr zeppelin & Livy demystified from Omid Vahdaty
]]> 1561 1 https://cdn.slidesharecdn.com/ss_thumbnails/emrzeppelindemystified-180131171046-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/observabilitymeetup-shared-200211073400-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/data-pipline-observability-meetup/227594271 Data Pipline Observabi... https://cdn.slidesharecdn.com/ss_thumbnails/couchbasedemystified-191207174312-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/couchbase-data-platform-big-data-demystified/202830633 Couchbase Data Platfor... https://cdn.slidesharecdn.com/ss_thumbnails/ml-demistifiedpart2-191024102243-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/machine-learning-essentials-demystified-part2-big-data-demystified/186224093 Machine Learning Essen...