�ݺ�ߣshows by User: PetrZapletal1

�ݺ�ߣshows by User: PetrZapletal1 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: PetrZapletal1 / Fri, 15 Nov 2019 18:53:49 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: PetrZapletal1 Change Data Capture - Scale by the Bay 2019 /slideshow/change-data-capture-scale-by-the-bay-2019/194027687 change-data-capture-pzapletal-191115185349
Modern systems are usually designed as a collection of cooperating micro-services. These services commonly have their dedicated data stores for their individual needs. To support various requirements corresponding data are often stored in data stores with very different characteristics and use cases. A fundamental requirement emerging from these architectures is the need to reliably capture primary data changes. Change Data Capture (CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data. In this talk, I’d like to discuss the advantages and disadvantages of various CDC approaches, provide you guidance in this area and also share our experience including various samples, and recommendations.]]>
Modern systems are usually designed as a collection of cooperating micro-services. These services commonly have their dedicated data stores for their individual needs. To support various requirements corresponding data are often stored in data stores with very different characteristics and use cases. A fundamental requirement emerging from these architectures is the need to reliably capture primary data changes. Change Data Capture (CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data. In this talk, I’d like to discuss the advantages and disadvantages of various CDC approaches, provide you guidance in this area and also share our experience including various samples, and recommendations.]]> Fri, 15 Nov 2019 18:53:49 GMT /slideshow/change-data-capture-scale-by-the-bay-2019/194027687 PetrZapletal1@slideshare.net(PetrZapletal1) Change Data Capture - Scale by the Bay 2019 PetrZapletal1 Modern systems are usually designed as a collection of cooperating micro-services. These services commonly have their dedicated data stores for their individual needs. To support various requirements corresponding data are often stored in data stores with very different characteristics and use cases. A fundamental requirement emerging from these architectures is the need to reliably capture primary data changes. Change Data Capture (CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data. In this talk, I’d like to discuss the advantages and disadvantages of various CDC approaches, provide you guidance in this area and also share our experience including various samples, and recommendations. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/change-data-capture-pzapletal-191115185349-thumbnail.jpg?width=120&height=120&fit=bounds" /> Modern systems are usually designed as a collection of cooperating micro-services. These services commonly have their dedicated data stores for their individual needs. To support various requirements corresponding data are often stored in data stores with very different characteristics and use cases. A fundamental requirement emerging from these architectures is the need to reliably capture primary data changes. Change Data Capture (CDC) is a set of software design patterns used to determine and track the data that has changed so that action can be taken using the changed data. In this talk, I’d like to discuss the advantages and disadvantages of various CDC approaches, provide you guidance in this area and also share our experience including various samples, and recommendations.

Change Data Capture - Scale by the Bay 2019 from Petr Zapletal

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0 Adopting GraalVM - NE Scala 2019 /slideshow/adopting-graalvm-ne-scala-2019/139756578 adoptinggraalvm-nescala2019-190405194115
After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. ]]>
After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. ]]> Fri, 05 Apr 2019 19:41:15 GMT /slideshow/adopting-graalvm-ne-scala-2019/139756578 PetrZapletal1@slideshare.net(PetrZapletal1) Adopting GraalVM - NE Scala 2019 PetrZapletal1 After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/adoptinggraalvm-nescala2019-190405194115-thumbnail.jpg?width=120&height=120&fit=bounds" /> After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic.

Adopting GraalVM - NE Scala 2019 from Petr Zapletal

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0 Adopting GraalVM - Scala eXchange London 2018 /slideshow/adopting-graalvm-scala-exchange-london-2018/125886133 pzapletal-adopting-graal-scala-exchange-181214111200
After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. ]]>
After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. ]]> Fri, 14 Dec 2018 11:12:00 GMT /slideshow/adopting-graalvm-scala-exchange-london-2018/125886133 PetrZapletal1@slideshare.net(PetrZapletal1) Adopting GraalVM - Scala eXchange London 2018 PetrZapletal1 After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pzapletal-adopting-graal-scala-exchange-181214111200-thumbnail.jpg?width=120&height=120&fit=bounds" /> After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic.

Adopting GraalVM - Scala eXchange London 2018 from Petr Zapletal

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0 Adopting GraalVM - Scale by the Bay 2018 /slideshow/adopting-graalvm-scale-by-the-bay-2018/123228606 pzapletal-adopting-graalvm-sbtb2018-181116205712
After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. ]]>
After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. ]]> Fri, 16 Nov 2018 20:57:12 GMT /slideshow/adopting-graalvm-scale-by-the-bay-2018/123228606 PetrZapletal1@slideshare.net(PetrZapletal1) Adopting GraalVM - Scale by the Bay 2018 PetrZapletal1 After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pzapletal-adopting-graalvm-sbtb2018-181116205712-thumbnail.jpg?width=120&height=120&fit=bounds" /> After many years of development, Oracle finally published GraalVM and sparkled a lot of interest in the community. GraalVM is a high-performance polyglot VM with a number of potentially interesting traits we can take advantage of like increased performance and lowered cost. It can also tackle shortcomings of JVM/Scala we are struggling for years like slow-startup times or large jars. Lastly, thanks to its polyglot nature it can open interesting doors we may want to discover. On the other hand, GraalVM may still be bleeding edge technology and having a hard time to deliver the promised features. In this talk, I’d like to discuss advantages and disadvantages of adopting GraalVM, provide you guidance if you decide to do so and also share our story in this area including various samples, and recommendations. This talk is focused on JVM and Scala but should be beneficial for everyone with interested in this topic.

Adopting GraalVM - Scale by the Bay 2018 from Petr Zapletal

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0 Real World Serverless /slideshow/real-world-serverless-82243924/82243924 pzapletal-sbtb2017-final-171117221322
Serverless is a hot topic in the software architecture world and also one of the points of contention. Serverless let us run our code without provisioning or managing servers. We don't have to think about servers at all. Things like elasticity or resilience might not longer be our problem anymore. On the other hand, we have to embrace a little bit different approach how to design our applications. We also have to give up a lot of control we might want and the most importantly we have to use technology which just might not be ready. In this talk, I’d like to discuss if it is worth to use serverless in our applications, what are the advantages and disadvantages of this approach. Secondly, I'd like to describe various use cases we were considering serverless and what was the result. And finally, I’d like to talk about how Scala fits into this. This talk should be interesting for everyone who is considering using serverless or just heard this word somewhere and would like to learn more. The talk is a little bit more focused on AWS but the understanding of the concepts I’m going to talk about should be beneficial even if you prefer a different service provider.]]>
Serverless is a hot topic in the software architecture world and also one of the points of contention. Serverless let us run our code without provisioning or managing servers. We don't have to think about servers at all. Things like elasticity or resilience might not longer be our problem anymore. On the other hand, we have to embrace a little bit different approach how to design our applications. We also have to give up a lot of control we might want and the most importantly we have to use technology which just might not be ready. In this talk, I’d like to discuss if it is worth to use serverless in our applications, what are the advantages and disadvantages of this approach. Secondly, I'd like to describe various use cases we were considering serverless and what was the result. And finally, I’d like to talk about how Scala fits into this. This talk should be interesting for everyone who is considering using serverless or just heard this word somewhere and would like to learn more. The talk is a little bit more focused on AWS but the understanding of the concepts I’m going to talk about should be beneficial even if you prefer a different service provider.]]> Fri, 17 Nov 2017 22:13:22 GMT /slideshow/real-world-serverless-82243924/82243924 PetrZapletal1@slideshare.net(PetrZapletal1) Real World Serverless PetrZapletal1 Serverless is a hot topic in the software architecture world and also one of the points of contention. Serverless let us run our code without provisioning or managing servers. We don't have to think about servers at all. Things like elasticity or resilience might not longer be our problem anymore. On the other hand, we have to embrace a little bit different approach how to design our applications. We also have to give up a lot of control we might want and the most importantly we have to use technology which just might not be ready. In this talk, I’d like to discuss if it is worth to use serverless in our applications, what are the advantages and disadvantages of this approach. Secondly, I'd like to describe various use cases we were considering serverless and what was the result. And finally, I’d like to talk about how Scala fits into this. This talk should be interesting for everyone who is considering using serverless or just heard this word somewhere and would like to learn more. The talk is a little bit more focused on AWS but the understanding of the concepts I’m going to talk about should be beneficial even if you prefer a different service provider. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pzapletal-sbtb2017-final-171117221322-thumbnail.jpg?width=120&height=120&fit=bounds" /> Serverless is a hot topic in the software architecture world and also one of the points of contention. Serverless let us run our code without provisioning or managing servers. We don't have to think about servers at all. Things like elasticity or resilience might not longer be our problem anymore. On the other hand, we have to embrace a little bit different approach how to design our applications. We also have to give up a lot of control we might want and the most importantly we have to use technology which just might not be ready. In this talk, I’d like to discuss if it is worth to use serverless in our applications, what are the advantages and disadvantages of this approach. Secondly, I'd like to describe various use cases we were considering serverless and what was the result. And finally, I’d like to talk about how Scala fits into this. This talk should be interesting for everyone who is considering using serverless or just heard this word somewhere and would like to learn more. The talk is a little bit more focused on AWS but the understanding of the concepts I’m going to talk about should be beneficial even if you prefer a different service provider.

Real World Serverless from Petr Zapletal

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0 Reactive mistakes - ScalaDays Chicago 2017 /slideshow/reactive-mistakes-scaladays-chicago-2017/75248754 reactivemistakes-scaladayschicago2017-170420225232
Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.]]>
Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.]]> Thu, 20 Apr 2017 22:52:32 GMT /slideshow/reactive-mistakes-scaladays-chicago-2017/75248754 PetrZapletal1@slideshare.net(PetrZapletal1) Reactive mistakes - ScalaDays Chicago 2017 PetrZapletal1 Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/reactivemistakes-scaladayschicago2017-170420225232-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.

Reactive mistakes - ScalaDays Chicago 2017 from Petr Zapletal

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0 Reactive mistakes reactive nyc /slideshow/reactive-mistakes-reactive-nyc/72720751 reactivemistakes-reactivenyc-170302032405
Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications working as expected. I’d like to talk about typical pitfalls that might cause problems, about trade-offs that might not be fully understood and important choices that might be overlooked. These include persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or has already deployed a reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on the Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.]]>
Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications working as expected. I’d like to talk about typical pitfalls that might cause problems, about trade-offs that might not be fully understood and important choices that might be overlooked. These include persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or has already deployed a reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on the Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.]]> Thu, 02 Mar 2017 03:24:05 GMT /slideshow/reactive-mistakes-reactive-nyc/72720751 PetrZapletal1@slideshare.net(PetrZapletal1) Reactive mistakes reactive nyc PetrZapletal1 Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications working as expected. I’d like to talk about typical pitfalls that might cause problems, about trade-offs that might not be fully understood and important choices that might be overlooked. These include persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or has already deployed a reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on the Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/reactivemistakes-reactivenyc-170302032405-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications working as expected. I’d like to talk about typical pitfalls that might cause problems, about trade-offs that might not be fully understood and important choices that might be overlooked. These include persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or has already deployed a reactive application. My goal is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on the Lightbend platform but understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.

Reactive mistakes reactive nyc from Petr Zapletal

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0 Distributed Stream Processing - Spark Summit East 2017 /slideshow/distributed-stream-processing-spark-summit-east-2017/71983932 streamprocessing-sparksummit2017-170210002610
The demand for stream processing is increasing a lot these days. Immense amounts of data have to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples. A number of powerful, easy-to-use open source platforms have emerged to address this. But the same problem can be solved differently, various but sometimes overlapping use-cases can be targeted or different vocabularies for similar concepts can be used. This may lead to confusion, longer development time or costly wrong decisions.]]>
The demand for stream processing is increasing a lot these days. Immense amounts of data have to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples. A number of powerful, easy-to-use open source platforms have emerged to address this. But the same problem can be solved differently, various but sometimes overlapping use-cases can be targeted or different vocabularies for similar concepts can be used. This may lead to confusion, longer development time or costly wrong decisions.]]> Fri, 10 Feb 2017 00:26:10 GMT /slideshow/distributed-stream-processing-spark-summit-east-2017/71983932 PetrZapletal1@slideshare.net(PetrZapletal1) Distributed Stream Processing - Spark Summit East 2017 PetrZapletal1 The demand for stream processing is increasing a lot these days. Immense amounts of data have to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples. A number of powerful, easy-to-use open source platforms have emerged to address this. But the same problem can be solved differently, various but sometimes overlapping use-cases can be targeted or different vocabularies for similar concepts can be used. This may lead to confusion, longer development time or costly wrong decisions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/streamprocessing-sparksummit2017-170210002610-thumbnail.jpg?width=120&height=120&fit=bounds" /> The demand for stream processing is increasing a lot these days. Immense amounts of data have to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples. A number of powerful, easy-to-use open source platforms have emerged to address this. But the same problem can be solved differently, various but sometimes overlapping use-cases can be targeted or different vocabularies for similar concepts can be used. This may lead to confusion, longer development time or costly wrong decisions.

Distributed Stream Processing - Spark Summit East 2017 from Petr Zapletal

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0 Top Mistakes When Writing Reactive Applications - Scala by the Bay 2016 /slideshow/top-mistakes-when-writing-reactive-applications-scala-by-the-bay-2016/68788482 reactivemistakes-scalabythebay2016-updated-161112190818
Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But the design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed a reactive application. My goal is to provide is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but the understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field. ]]>
Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But the design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed a reactive application. My goal is to provide is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but the understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field. ]]> Sat, 12 Nov 2016 19:08:18 GMT /slideshow/top-mistakes-when-writing-reactive-applications-scala-by-the-bay-2016/68788482 PetrZapletal1@slideshare.net(PetrZapletal1) Top Mistakes When Writing Reactive Applications - Scala by the Bay 2016 PetrZapletal1 Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But the design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed a reactive application. My goal is to provide is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but the understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/reactivemistakes-scalabythebay2016-updated-161112190818-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reactive applications are becoming a de-facto industry standard and, if employed correctly, toolkits like Lightbend Reactive Platform make the implementation easier than ever. But the design of these systems might be challenging as it requires particular mindset shift to tackle problems we might not be used to. In this talk, we’re going to discuss the most common things I’ve seen in the field that prevented applications to work as expected. I’d like to talk about typical pitfalls that might cause troubles, about trade-offs that might not be fully understood or important choices that might be overlooked including persistent actors pitfalls, tackling of network partitions, proper implementations of graceful shutdown or distributed transactions, trade-offs of micro-services or actors and more. This talk should be interesting for anyone who is thinking about, implementing, or have already deployed a reactive application. My goal is to provide is to provide a comprehensive explanation of common problems to be sure they won’t be repeated by fellow developers. The talk is a little bit more focused on Lightbend platform but the understanding of the concepts we are going to talk about should be beneficial for everyone interested in this field.

Top Mistakes When Writing Reactive Applications - Scala by the Bay 2016 from Petr Zapletal

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0 Distributed Real-Time Stream Processing: Why and How 2.0 /slideshow/distributed-realtime-stream-processing-why-and-how-20/61930736 sd2016-160512034804
The demand for stream processing is increasing a lot these day. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples. In this talk we are going to discuss various state of the art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs and their intended use-cases. Apart of that, I’m going to speak about Fast Data, theory of streaming, framework evaluation and so on. My goal is to provide comprehensive overview about modern streaming frameworks and to help fellow developers with picking the best possible for their particular use-case. ]]>
The demand for stream processing is increasing a lot these day. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples. In this talk we are going to discuss various state of the art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs and their intended use-cases. Apart of that, I’m going to speak about Fast Data, theory of streaming, framework evaluation and so on. My goal is to provide comprehensive overview about modern streaming frameworks and to help fellow developers with picking the best possible for their particular use-case. ]]> Thu, 12 May 2016 03:48:04 GMT /slideshow/distributed-realtime-stream-processing-why-and-how-20/61930736 PetrZapletal1@slideshare.net(PetrZapletal1) Distributed Real-Time Stream Processing: Why and How 2.0 PetrZapletal1 The demand for stream processing is increasing a lot these day. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples. In this talk we are going to discuss various state of the art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs and their intended use-cases. Apart of that, I’m going to speak about Fast Data, theory of streaming, framework evaluation and so on. My goal is to provide comprehensive overview about modern streaming frameworks and to help fellow developers with picking the best possible for their particular use-case. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sd2016-160512034804-thumbnail.jpg?width=120&height=120&fit=bounds" /> The demand for stream processing is increasing a lot these day. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications include trading, social networks, Internet of things, system monitoring, and many other examples. In this talk we are going to discuss various state of the art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs and their intended use-cases. Apart of that, I’m going to speak about Fast Data, theory of streaming, framework evaluation and so on. My goal is to provide comprehensive overview about modern streaming frameworks and to help fellow developers with picking the best possible for their particular use-case.

Distributed Real-Time Stream Processing: Why and How 2.0 from Petr Zapletal

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0 Distributed real time stream processing- why and how /slideshow/distributed-real-time-stream-processing-why-and-how/56051212 distributedreal-timestreamprocessing-whyandhow-151211120330
In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more. The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this. Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.]]>
In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more. The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this. Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.]]> Fri, 11 Dec 2015 12:03:30 GMT /slideshow/distributed-real-time-stream-processing-why-and-how/56051212 PetrZapletal1@slideshare.net(PetrZapletal1) Distributed real time stream processing- why and how PetrZapletal1 In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more. The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this. Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/distributedreal-timestreamprocessing-whyandhow-151211120330-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more. The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this. Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.

Distributed real time stream processing- why and how from Petr Zapletal

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0 Spark Concepts - Spark SQL, Graphx, Streaming /slideshow/spark-concepts-spark-sql-graphx-streaming/46164390 concepts-sparksqlgraphxstreaming-150323062213-conversion-gate01
Introduction to Spark]]>
Introduction to Spark]]> Mon, 23 Mar 2015 06:22:13 GMT /slideshow/spark-concepts-spark-sql-graphx-streaming/46164390 PetrZapletal1@slideshare.net(PetrZapletal1) Spark Concepts - Spark SQL, Graphx, Streaming PetrZapletal1 Introduction to Spark <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/concepts-sparksqlgraphxstreaming-150323062213-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to Spark

Spark Concepts - Spark SQL, Graphx, Streaming from Petr Zapletal

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0 MLlib and Machine Learning on Spark /slideshow/mllib-and-machine-learning-on-spark/46164352 mllibandmachinelearningonspark1-150323062058-conversion-gate01
Introduction to Spark]]>
Introduction to Spark]]> Mon, 23 Mar 2015 06:20:57 GMT /slideshow/mllib-and-machine-learning-on-spark/46164352 PetrZapletal1@slideshare.net(PetrZapletal1) MLlib and Machine Learning on Spark PetrZapletal1 Introduction to Spark <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mllibandmachinelearningonspark1-150323062058-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Introduction to Spark

MLlib and Machine Learning on Spark from Petr Zapletal

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0 https://cdn.slidesharecdn.com/profile-photo-PetrZapletal1-48x48.jpg?cb=1573844018 Petr is a Consultant who specialises in the design and implementation of highly scalable, reactive and resilient distributed systems. He is a functional programming and open source evangelist and has expertise in the area of big data and machine classification techniques. Petr participates in the whole software delivery lifecycle; from requirement analysis & design, through to maintaining systems in production. During his career, Petr has worked for various companies from start-ups to large international corporations. Petr's current interests are Reactive Systems, Distributed Streaming, and Deep Learning. Petr is also an author of #ThisWeekInScala and an occasional conference speaker. https://github.com/pzapletal https://cdn.slidesharecdn.com/ss_thumbnails/change-data-capture-pzapletal-191115185349-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/change-data-capture-scale-by-the-bay-2019/194027687 Change Data Capture - ... https://cdn.slidesharecdn.com/ss_thumbnails/adoptinggraalvm-nescala2019-190405194115-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/adopting-graalvm-ne-scala-2019/139756578 Adopting GraalVM - NE ... https://cdn.slidesharecdn.com/ss_thumbnails/pzapletal-adopting-graal-scala-exchange-181214111200-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/adopting-graalvm-scala-exchange-london-2018/125886133 Adopting GraalVM - Sca...