�ݺ�ߣshows by User: TzachZohar

�ݺ�ߣshows by User: TzachZohar / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: TzachZohar / Thu, 09 Jun 2016 17:37:04 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: TzachZohar Spark Your Legacy (Spark Summit 2016) /slideshow/spark-your-legacy-spark-summit-2016/62904381 sparkyourlegacysparksummit2016-160609173704
Apache Spark is already a proven leader as a heavy-weight distributed processing framework. But how does one migrate an 8-years-old, single-server, MySQL-based legacy system to such new shiny frameworks? How do you accurately preserve the behavior of a system consuming Terabytes of data every day, hiding numerous undocumented implicit gotchas and changing constantly, while shifting to brand new development paradigms? In this talk we present Kenshoo’s attempt at this challenge, where we migrated a legacy aggregation system to Spark. Our solutions include heavy usage of metrics and graphite for analyzing production data; “local-mode” client enabling reuse of legacy tests suits; data validations using side-by-side execution; and maximum reuse of code through refactoring and composition. Some of these solutions use Spark-specific characteristics and features.]]>
Apache Spark is already a proven leader as a heavy-weight distributed processing framework. But how does one migrate an 8-years-old, single-server, MySQL-based legacy system to such new shiny frameworks? How do you accurately preserve the behavior of a system consuming Terabytes of data every day, hiding numerous undocumented implicit gotchas and changing constantly, while shifting to brand new development paradigms? In this talk we present Kenshoo’s attempt at this challenge, where we migrated a legacy aggregation system to Spark. Our solutions include heavy usage of metrics and graphite for analyzing production data; “local-mode” client enabling reuse of legacy tests suits; data validations using side-by-side execution; and maximum reuse of code through refactoring and composition. Some of these solutions use Spark-specific characteristics and features.]]> Thu, 09 Jun 2016 17:37:04 GMT /slideshow/spark-your-legacy-spark-summit-2016/62904381 TzachZohar@slideshare.net(TzachZohar) Spark Your Legacy (Spark Summit 2016) TzachZohar Apache Spark is already a proven leader as a heavy-weight distributed processing framework. But how does one migrate an 8-years-old, single-server, MySQL-based legacy system to such new shiny frameworks? How do you accurately preserve the behavior of a system consuming Terabytes of data every day, hiding numerous undocumented implicit gotchas and changing constantly, while shifting to brand new development paradigms? In this talk we present Kenshoo’s attempt at this challenge, where we migrated a legacy aggregation system to Spark. Our solutions include heavy usage of metrics and graphite for analyzing production data; “local-mode” client enabling reuse of legacy tests suits; data validations using side-by-side execution; and maximum reuse of code through refactoring and composition. Some of these solutions use Spark-specific characteristics and features. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sparkyourlegacysparksummit2016-160609173704-thumbnail.jpg?width=120&height=120&fit=bounds" /> Apache Spark is already a proven leader as a heavy-weight distributed processing framework. But how does one migrate an 8-years-old, single-server, MySQL-based legacy system to such new shiny frameworks? How do you accurately preserve the behavior of a system consuming Terabytes of data every day, hiding numerous undocumented implicit gotchas and changing constantly, while shifting to brand new development paradigms? In this talk we present Kenshoo’s attempt at this challenge, where we migrated a legacy aggregation system to Spark. Our solutions include heavy usage of metrics and graphite for analyzing production data; “local-mode” client enabling reuse of legacy tests suits; data validations using side-by-side execution; and maximum reuse of code through refactoring and composition. Some of these solutions use Spark-specific characteristics and features.

Spark Your Legacy (Spark Summit 2016) from Tzach Zohar

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0 “Insulin” for Scala’s Syntactic Diabetes /slideshow/insulin-for-scalas-syntactic-diabetes/61668954 insulinforscalassyntacticdiabetes-160504131735
See http://www.scalapeno.org.il/#!tzach-zohar/jwhyy: One of the most common criticisms of Scala, and indeed one of the most concerning ones - is its "Syntactic Diabetes": There's so much "Syntactic Sugar" - so many different ways to code the same thing - that developers might easily get lost. This makes many developers and organizations weary of adopting Scala as a primary language, fearing the training and maintenance costs this problem might entail. In this talk we'll explain the problem through some real-life examples from Kenshoo's 2- year experience with Scala, and move on to the solutions applied at Kenshoo and elsewhere to resolve this. Among these, we'll discuss style guides, automatic style checkers, Odersky's "Scala Levels", code review tips and more. If you're a developer thinking about trying Scala out, or a Scala enthusiast trying to convince your teammates or bosses to do so - this talk is for you.]]>
See http://www.scalapeno.org.il/#!tzach-zohar/jwhyy: One of the most common criticisms of Scala, and indeed one of the most concerning ones - is its "Syntactic Diabetes": There's so much "Syntactic Sugar" - so many different ways to code the same thing - that developers might easily get lost. This makes many developers and organizations weary of adopting Scala as a primary language, fearing the training and maintenance costs this problem might entail. In this talk we'll explain the problem through some real-life examples from Kenshoo's 2- year experience with Scala, and move on to the solutions applied at Kenshoo and elsewhere to resolve this. Among these, we'll discuss style guides, automatic style checkers, Odersky's "Scala Levels", code review tips and more. If you're a developer thinking about trying Scala out, or a Scala enthusiast trying to convince your teammates or bosses to do so - this talk is for you.]]> Wed, 04 May 2016 13:17:34 GMT /slideshow/insulin-for-scalas-syntactic-diabetes/61668954 TzachZohar@slideshare.net(TzachZohar) “Insulin” for Scala’s Syntactic Diabetes TzachZohar See http://www.scalapeno.org.il/#!tzach-zohar/jwhyy: One of the most common criticisms of Scala, and indeed one of the most concerning ones - is its "Syntactic Diabetes": There's so much "Syntactic Sugar" - so many different ways to code the same thing - that developers might easily get lost. This makes many developers and organizations weary of adopting Scala as a primary language, fearing the training and maintenance costs this problem might entail. In this talk we'll explain the problem through some real-life examples from Kenshoo's 2- year experience with Scala, and move on to the solutions applied at Kenshoo and elsewhere to resolve this. Among these, we'll discuss style guides, automatic style checkers, Odersky's "Scala Levels", code review tips and more. If you're a developer thinking about trying Scala out, or a Scala enthusiast trying to convince your teammates or bosses to do so - this talk is for you. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/insulinforscalassyntacticdiabetes-160504131735-thumbnail.jpg?width=120&height=120&fit=bounds" /> See http://www.scalapeno.org.il/#!tzach-zohar/jwhyy: One of the most common criticisms of Scala, and indeed one of the most concerning ones - is its "Syntactic Diabetes": There's so much "Syntactic Sugar" - so many different ways to code the same thing - that developers might easily get lost. This makes many developers and organizations weary of adopting Scala as a primary language, fearing the training and maintenance costs this problem might entail. In this talk we'll explain the problem through some real-life examples from Kenshoo's 2- year experience with Scala, and move on to the solutions applied at Kenshoo and elsewhere to resolve this. Among these, we'll discuss style guides, automatic style checkers, Odersky's "Scala Levels", code review tips and more. If you're a developer thinking about trying Scala out, or a Scala enthusiast trying to convince your teammates or bosses to do so - this talk is for you.

“Insulin” for Scala’s Syntactic Diabetes from Tzach Zohar

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0 Scala - THE language for Big Data /slideshow/scala-the-language-for-big-data/59629505 scala-thelanguageforbigdatanoanimations-160316121023
The Big Data ecosystem has matured. Idioms such as eventual-consistency, immutability, CAP theorem and many more have been researched and successfully implemented in various Big Data tools and systems. In recent years, characteristics of Big Data systems started infiltrating the lower levels of design, all the way down to the choice of language. In this light, Scala - the Object Oriented, Strongly-Typed, Functional language - started to shine as a perfect fit for this environment, with tools like Apache Spark attesting to its benefits. In this talk I'll try to share my view of why Scala is THE language for Big Data processing, with some real-world examples of the advantages this combination creates. Full version (with animations): https://docs.google.com/presentation/d/1m4_BBXQKbkaGWImFRwa33HFVEuTcOdwa4kY80dVpvAg ]]>
The Big Data ecosystem has matured. Idioms such as eventual-consistency, immutability, CAP theorem and many more have been researched and successfully implemented in various Big Data tools and systems. In recent years, characteristics of Big Data systems started infiltrating the lower levels of design, all the way down to the choice of language. In this light, Scala - the Object Oriented, Strongly-Typed, Functional language - started to shine as a perfect fit for this environment, with tools like Apache Spark attesting to its benefits. In this talk I'll try to share my view of why Scala is THE language for Big Data processing, with some real-world examples of the advantages this combination creates. Full version (with animations): https://docs.google.com/presentation/d/1m4_BBXQKbkaGWImFRwa33HFVEuTcOdwa4kY80dVpvAg ]]> Wed, 16 Mar 2016 12:10:23 GMT /slideshow/scala-the-language-for-big-data/59629505 TzachZohar@slideshare.net(TzachZohar) Scala - THE language for Big Data TzachZohar The Big Data ecosystem has matured. Idioms such as eventual-consistency, immutability, CAP theorem and many more have been researched and successfully implemented in various Big Data tools and systems. In recent years, characteristics of Big Data systems started infiltrating the lower levels of design, all the way down to the choice of language. In this light, Scala - the Object Oriented, Strongly-Typed, Functional language - started to shine as a perfect fit for this environment, with tools like Apache Spark attesting to its benefits. In this talk I'll try to share my view of why Scala is THE language for Big Data processing, with some real-world examples of the advantages this combination creates. Full version (with animations): https://docs.google.com/presentation/d/1m4_BBXQKbkaGWImFRwa33HFVEuTcOdwa4kY80dVpvAg <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scala-thelanguageforbigdatanoanimations-160316121023-thumbnail.jpg?width=120&height=120&fit=bounds" /> The Big Data ecosystem has matured. Idioms such as eventual-consistency, immutability, CAP theorem and many more have been researched and successfully implemented in various Big Data tools and systems. In recent years, characteristics of Big Data systems started infiltrating the lower levels of design, all the way down to the choice of language. In this light, Scala - the Object Oriented, Strongly-Typed, Functional language - started to shine as a perfect fit for this environment, with tools like Apache Spark attesting to its benefits. In this talk I'll try to share my view of why Scala is THE language for Big Data processing, with some real-world examples of the advantages this combination creates. Full version (with animations): https://docs.google.com/presentation/d/1m4_BBXQKbkaGWImFRwa33HFVEuTcOdwa4kY80dVpvAg

Scala - THE language for Big Data from Tzach Zohar

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0 Monitoring Spark Applications /slideshow/monitoring-spark-applications-59338130/59338130 monitoringsparkapplications-160309205525
Spark is quickly becoming the most popular framework in the MapReduce family. With better performance and much better APIs - it's easier than ever to perform the actual data wrangling; But as always - the challenges of operating, verifying and optimizing your application over time are much greater than the initial setup - and all the more so with distributes systems. In Kenshoo, we've used and developed some tools and techniques to monitor the state of our Spark application: health, correctness, performance, utilization, and business KPIs. We'll discuss some standard tools and less standard techniques to get the most information out of your Spark cluster. ]]>
Spark is quickly becoming the most popular framework in the MapReduce family. With better performance and much better APIs - it's easier than ever to perform the actual data wrangling; But as always - the challenges of operating, verifying and optimizing your application over time are much greater than the initial setup - and all the more so with distributes systems. In Kenshoo, we've used and developed some tools and techniques to monitor the state of our Spark application: health, correctness, performance, utilization, and business KPIs. We'll discuss some standard tools and less standard techniques to get the most information out of your Spark cluster. ]]> Wed, 09 Mar 2016 20:55:25 GMT /slideshow/monitoring-spark-applications-59338130/59338130 TzachZohar@slideshare.net(TzachZohar) Monitoring Spark Applications TzachZohar Spark is quickly becoming the most popular framework in the MapReduce family. With better performance and much better APIs - it's easier than ever to perform the actual data wrangling; But as always - the challenges of operating, verifying and optimizing your application over time are much greater than the initial setup - and all the more so with distributes systems. In Kenshoo, we've used and developed some tools and techniques to monitor the state of our Spark application: health, correctness, performance, utilization, and business KPIs. We'll discuss some standard tools and less standard techniques to get the most information out of your Spark cluster. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/monitoringsparkapplications-160309205525-thumbnail.jpg?width=120&height=120&fit=bounds" /> Spark is quickly becoming the most popular framework in the MapReduce family. With better performance and much better APIs - it's easier than ever to perform the actual data wrangling; But as always - the challenges of operating, verifying and optimizing your application over time are much greater than the initial setup - and all the more so with distributes systems. In Kenshoo, we've used and developed some tools and techniques to monitor the state of our Spark application: health, correctness, performance, utilization, and business KPIs. We'll discuss some standard tools and less standard techniques to get the most information out of your Spark cluster.

Monitoring Spark Applications from Tzach Zohar

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0 Developers like winning - gamifying code reviews /slideshow/developers-like-winning-gamifying-code-reviews/45831762 developerslikewinning-gamifyingcodereviews-150314102932-conversion-gate01
How we gamified the code review process to make it an integral part of our development flow. Given as in ignite talk at 2015 Reversim Summit]]>
How we gamified the code review process to make it an integral part of our development flow. Given as in ignite talk at 2015 Reversim Summit]]> Sat, 14 Mar 2015 10:29:32 GMT /slideshow/developers-like-winning-gamifying-code-reviews/45831762 TzachZohar@slideshare.net(TzachZohar) Developers like winning - gamifying code reviews TzachZohar How we gamified the code review process to make it an integral part of our development flow. Given as in ignite talk at 2015 Reversim Summit <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/developerslikewinning-gamifyingcodereviews-150314102932-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> How we gamified the code review process to make it an integral part of our development flow. Given as in ignite talk at 2015 Reversim Summit

Developers like winning - gamifying code reviews from Tzach Zohar

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0 Continuous Delivery in Practice (extended) /slideshow/continuous-delivery-in-practice-extended/37654777 continuousdeliveryinpracticeextended-140804154401-phpapp01
Extended version of a previously uploaded presentation: 10 practical field-proven tips for building a continuously delivered service, based on Kenshoo's experience with its RTB service - a critical, high throughput, highly available component, serving millions of requests per minute in under 50 milliseconds. From coding practices to test automation, from monitoring tools to feature A/B testing - the entire development chain should be focused around removing blockers and manual steps between your code and your clients, without ever settling for quality. Join to see what makes our clients and developers happy and effective.]]>
Extended version of a previously uploaded presentation: 10 practical field-proven tips for building a continuously delivered service, based on Kenshoo's experience with its RTB service - a critical, high throughput, highly available component, serving millions of requests per minute in under 50 milliseconds. From coding practices to test automation, from monitoring tools to feature A/B testing - the entire development chain should be focused around removing blockers and manual steps between your code and your clients, without ever settling for quality. Join to see what makes our clients and developers happy and effective.]]> Mon, 04 Aug 2014 15:44:00 GMT /slideshow/continuous-delivery-in-practice-extended/37654777 TzachZohar@slideshare.net(TzachZohar) Continuous Delivery in Practice (extended) TzachZohar Extended version of a previously uploaded presentation: 10 practical field-proven tips for building a continuously delivered service, based on Kenshoo's experience with its RTB service - a critical, high throughput, highly available component, serving millions of requests per minute in under 50 milliseconds. From coding practices to test automation, from monitoring tools to feature A/B testing - the entire development chain should be focused around removing blockers and manual steps between your code and your clients, without ever settling for quality. Join to see what makes our clients and developers happy and effective. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/continuousdeliveryinpracticeextended-140804154401-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Extended version of a previously uploaded presentation: 10 practical field-proven tips for building a continuously delivered service, based on Kenshoo's experience with its RTB service - a critical, high throughput, highly available component, serving millions of requests per minute in under 50 milliseconds. From coding practices to test automation, from monitoring tools to feature A/B testing - the entire development chain should be focused around removing blockers and manual steps between your code and your clients, without ever settling for quality. Join to see what makes our clients and developers happy and effective.

Continuous Delivery in Practice (extended) from Tzach Zohar

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0 Continuous delivery in practice (public) /slideshow/continuous-delivery-in-practice-public/36715371 continuousdeliveryinpracticepublic-140707135437-phpapp01
10 practical field-proven tips for building a continuously delivered service, based on Kenshoo's experience with its RTB service - a critical, high throughput, highly available component, serving millions of requests per minute in under 50 milliseconds. From coding practices to test automation, from monitoring tools to feature A/B testing - the entire development chain should be focused around removing blockers and manual steps between your code and your clients, without ever settling for quality. Join to see what makes our clients and developers happy and effective. ]]>
10 practical field-proven tips for building a continuously delivered service, based on Kenshoo's experience with its RTB service - a critical, high throughput, highly available component, serving millions of requests per minute in under 50 milliseconds. From coding practices to test automation, from monitoring tools to feature A/B testing - the entire development chain should be focused around removing blockers and manual steps between your code and your clients, without ever settling for quality. Join to see what makes our clients and developers happy and effective. ]]> Mon, 07 Jul 2014 13:54:36 GMT /slideshow/continuous-delivery-in-practice-public/36715371 TzachZohar@slideshare.net(TzachZohar) Continuous delivery in practice (public) TzachZohar 10 practical field-proven tips for building a continuously delivered service, based on Kenshoo's experience with its RTB service - a critical, high throughput, highly available component, serving millions of requests per minute in under 50 milliseconds. From coding practices to test automation, from monitoring tools to feature A/B testing - the entire development chain should be focused around removing blockers and manual steps between your code and your clients, without ever settling for quality. Join to see what makes our clients and developers happy and effective. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/continuousdeliveryinpracticepublic-140707135437-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> 10 practical field-proven tips for building a continuously delivered service, based on Kenshoo's experience with its RTB service - a critical, high throughput, highly available component, serving millions of requests per minute in under 50 milliseconds. From coding practices to test automation, from monitoring tools to feature A/B testing - the entire development chain should be focused around removing blockers and manual steps between your code and your clients, without ever settling for quality. Join to see what makes our clients and developers happy and effective.

Continuous delivery in practice (public) from Tzach Zohar

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0 https://cdn.slidesharecdn.com/profile-photo-TzachZohar-48x48.jpg?cb=1516833828 See http://stackoverflow.com/story/tzach-zohar Experienced developer and architect, specializing in building high-scale enterprise solutions, from whiteboard brainstorming to hands-on coding. Interested in optimizing development throughput, quality and joy by using the best tools and techniques, and then looking for better ones. http://www.kenshoo.com/blog-post/building-and-delivering-production-ready-services-with-gradle-dropwizard-graylog2-and-puppet/ https://cdn.slidesharecdn.com/ss_thumbnails/sparkyourlegacysparksummit2016-160609173704-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/spark-your-legacy-spark-summit-2016/62904381 Spark Your Legacy (Spa... https://cdn.slidesharecdn.com/ss_thumbnails/insulinforscalassyntacticdiabetes-160504131735-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/insulin-for-scalas-syntactic-diabetes/61668954 “Insulin” for Scala’s ... https://cdn.slidesharecdn.com/ss_thumbnails/scala-thelanguageforbigdatanoanimations-160316121023-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/scala-the-language-for-big-data/59629505 Scala - THE language f...