�ݺ�ߣshows by User: rastrick

�ݺ�ߣshows by User: rastrick / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: rastrick / Fri, 09 Sep 2016 19:50:35 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: rastrick Always On: Building Highly Available Applications on Cassandra /slideshow/always-on-building-highly-available-applications-on-cassandra/65872786 alwayson-160909195035
Cassandra was built from the ground up to enable linearly scalable, always-on applications. But the path to high availability has many land mines that can mean failure for the inexperienced user. In this talk, I will offer practical advice on how to achieve 100% uptime on millions of transactions per second. I'll address all aspects of the topic, including deployment, configuration, application design, and operations.]]>
Cassandra was built from the ground up to enable linearly scalable, always-on applications. But the path to high availability has many land mines that can mean failure for the inexperienced user. In this talk, I will offer practical advice on how to achieve 100% uptime on millions of transactions per second. I'll address all aspects of the topic, including deployment, configuration, application design, and operations.]]> Fri, 09 Sep 2016 19:50:35 GMT /slideshow/always-on-building-highly-available-applications-on-cassandra/65872786 rastrick@slideshare.net(rastrick) Always On: Building Highly Available Applications on Cassandra rastrick Cassandra was built from the ground up to enable linearly scalable, always-on applications. But the path to high availability has many land mines that can mean failure for the inexperienced user. In this talk, I will offer practical advice on how to achieve 100% uptime on millions of transactions per second. I'll address all aspects of the topic, including deployment, configuration, application design, and operations. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/alwayson-160909195035-thumbnail.jpg?width=120&height=120&fit=bounds" /> Cassandra was built from the ground up to enable linearly scalable, always-on applications. But the path to high availability has many land mines that can mean failure for the inexperienced user. In this talk, I will offer practical advice on how to achieve 100% uptime on millions of transactions per second. I'll address all aspects of the topic, including deployment, configuration, application design, and operations.

Always On: Building Highly Available Applications on Cassandra from Robbie Strickland

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0 Lambda at Weather Scale - Cassandra Summit 2015 /slideshow/lambda-at-weather-scale-cassandra-summit-2015/53222636 lambdaweatherscalemin-150926122856-lva1-app6892
We hear a lot about lambda architectures and how Cassandra and Spark can help us crunch our data both in batch and real-time. After a year in the trenches, I'll share how we at The Weather Company built a general purpose, weather-scale event processing pipeline to make sense of billions of events each day. If you want to avoid much of the pain learning how to get it right, this talk is for you. ]]>
We hear a lot about lambda architectures and how Cassandra and Spark can help us crunch our data both in batch and real-time. After a year in the trenches, I'll share how we at The Weather Company built a general purpose, weather-scale event processing pipeline to make sense of billions of events each day. If you want to avoid much of the pain learning how to get it right, this talk is for you. ]]> Sat, 26 Sep 2015 12:28:56 GMT /slideshow/lambda-at-weather-scale-cassandra-summit-2015/53222636 rastrick@slideshare.net(rastrick) Lambda at Weather Scale - Cassandra Summit 2015 rastrick We hear a lot about lambda architectures and how Cassandra and Spark can help us crunch our data both in batch and real-time. After a year in the trenches, I'll share how we at The Weather Company built a general purpose, weather-scale event processing pipeline to make sense of billions of events each day. If you want to avoid much of the pain learning how to get it right, this talk is for you. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lambdaweatherscalemin-150926122856-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> We hear a lot about lambda architectures and how Cassandra and Spark can help us crunch our data both in batch and real-time. After a year in the trenches, I'll share how we at The Weather Company built a general purpose, weather-scale event processing pipeline to make sense of billions of events each day. If you want to avoid much of the pain learning how to get it right, this talk is for you.

Lambda at Weather Scale - Cassandra Summit 2015 from Robbie Strickland

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0 New Analytics Toolbox DevNexus 2015 /slideshow/new-analytics-toolbox-devnexus-2015/45778625 newanalyticstoolbox-150312194519-conversion-gate01
The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. In this talk I will introduce you to a powerhouse combination of Cassandra and Spark, which provides a high-speed platform for both real-time and batch analysis.]]>
The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. In this talk I will introduce you to a powerhouse combination of Cassandra and Spark, which provides a high-speed platform for both real-time and batch analysis.]]> Thu, 12 Mar 2015 19:45:19 GMT /slideshow/new-analytics-toolbox-devnexus-2015/45778625 rastrick@slideshare.net(rastrick) New Analytics Toolbox DevNexus 2015 rastrick The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. In this talk I will introduce you to a powerhouse combination of Cassandra and Spark, which provides a high-speed platform for both real-time and batch analysis. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/newanalyticstoolbox-150312194519-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. In this talk I will introduce you to a powerhouse combination of Cassandra and Spark, which provides a high-speed platform for both real-time and batch analysis.

New Analytics Toolbox DevNexus 2015 from Robbie Strickland

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0 CQL Under the Hood /slideshow/cql-under-the-hood-39023557/39023557 cqlunderhood-140912124628-phpapp01
As a reformed CQL critic, I'd like to help dispel the myths around CQL and extol its awesomeness. Most criticism comes from people like me who were early Cassandra adopters and are concerned about the SQL-like syntax, the apparent lack of control, and the reliance on a defined schema. I'll pop open the hood, showing just how the various CQL constructs translate to the underlying storage layer--and in the process I hope to give novices and old-timers alike a reason to love CQL.]]>
As a reformed CQL critic, I'd like to help dispel the myths around CQL and extol its awesomeness. Most criticism comes from people like me who were early Cassandra adopters and are concerned about the SQL-like syntax, the apparent lack of control, and the reliance on a defined schema. I'll pop open the hood, showing just how the various CQL constructs translate to the underlying storage layer--and in the process I hope to give novices and old-timers alike a reason to love CQL.]]> Fri, 12 Sep 2014 12:46:28 GMT /slideshow/cql-under-the-hood-39023557/39023557 rastrick@slideshare.net(rastrick) CQL Under the Hood rastrick As a reformed CQL critic, I'd like to help dispel the myths around CQL and extol its awesomeness. Most criticism comes from people like me who were early Cassandra adopters and are concerned about the SQL-like syntax, the apparent lack of control, and the reliance on a defined schema. I'll pop open the hood, showing just how the various CQL constructs translate to the underlying storage layer--and in the process I hope to give novices and old-timers alike a reason to love CQL. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cqlunderhood-140912124628-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> As a reformed CQL critic, I'd like to help dispel the myths around CQL and extol its awesomeness. Most criticism comes from people like me who were early Cassandra adopters and are concerned about the SQL-like syntax, the apparent lack of control, and the reliance on a defined schema. I'll pop open the hood, showing just how the various CQL constructs translate to the underlying storage layer--and in the process I hope to give novices and old-timers alike a reason to love CQL.

CQL Under the Hood from Robbie Strickland

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0 New Analytics Toolbox /slideshow/new-analytics-toolbox/38088932 newanalyticstoolbox-140818071420-phpapp02
The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. Learn the serious advantages to the new tools, and get an analysis of the current state--including pros and cons as well as what's needed to bootstrap and operate the various options.]]>
The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. Learn the serious advantages to the new tools, and get an analysis of the current state--including pros and cons as well as what's needed to bootstrap and operate the various options.]]> Mon, 18 Aug 2014 07:14:20 GMT /slideshow/new-analytics-toolbox/38088932 rastrick@slideshare.net(rastrick) New Analytics Toolbox rastrick The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. Learn the serious advantages to the new tools, and get an analysis of the current state--including pros and cons as well as what's needed to bootstrap and operate the various options. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/newanalyticstoolbox-140818071420-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> The state of analytics has changed dramatically over the last few years. Hadoop is now commonplace, and the ecosystem has evolved to include new tools such as Spark, Shark, and Drill, that live alongside the old MapReduce-based standards. It can be difficult to keep up with the pace of change, and newcomers are left with a dizzying variety of seemingly similar choices. This is compounded by the number of possible deployment permutations, which can cause all but the most determined to simply stick with the tried and true. Learn the serious advantages to the new tools, and get an analysis of the current state--including pros and cons as well as what's needed to bootstrap and operate the various options.

New Analytics Toolbox from Robbie Strickland

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0 Big Data Grows Up - A (re)introduction to Cassandra /slideshow/big-data-growsupdevnexus/31631025 bigdatagrowsupdevnexus-140225120726-phpapp02
For the last several years Cassandra has been the heavyweight in the NoSQL space. But its massive scalability was accompanied by a bare bones feature set, a substantial learning curve, and a Thrift-based RPC mechanism that left newbies bewildered by a sea of potential client libraries–all with their own fragmented semantics. Over the last year that’s all changed, culminating in the recently unveiled Cassandra 2.0. In this talk I’ll bring you up to speed on Cassandra Query Language, cursors, the new native libraries, lightweight transactions, virtual nodes, and loads of other new goodies. Whether you’re completely new to Cassandra or a seasoned veteran who wants the latest scoop, this talk has something for you.]]>
For the last several years Cassandra has been the heavyweight in the NoSQL space. But its massive scalability was accompanied by a bare bones feature set, a substantial learning curve, and a Thrift-based RPC mechanism that left newbies bewildered by a sea of potential client libraries–all with their own fragmented semantics. Over the last year that’s all changed, culminating in the recently unveiled Cassandra 2.0. In this talk I’ll bring you up to speed on Cassandra Query Language, cursors, the new native libraries, lightweight transactions, virtual nodes, and loads of other new goodies. Whether you’re completely new to Cassandra or a seasoned veteran who wants the latest scoop, this talk has something for you.]]> Tue, 25 Feb 2014 12:07:25 GMT /slideshow/big-data-growsupdevnexus/31631025 rastrick@slideshare.net(rastrick) Big Data Grows Up - A (re)introduction to Cassandra rastrick For the last several years Cassandra has been the heavyweight in the NoSQL space. But its massive scalability was accompanied by a bare bones feature set, a substantial learning curve, and a Thrift-based RPC mechanism that left newbies bewildered by a sea of potential client libraries–all with their own fragmented semantics. Over the last year that’s all changed, culminating in the recently unveiled Cassandra 2.0. In this talk I’ll bring you up to speed on Cassandra Query Language, cursors, the new native libraries, lightweight transactions, virtual nodes, and loads of other new goodies. Whether you’re completely new to Cassandra or a seasoned veteran who wants the latest scoop, this talk has something for you. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdatagrowsupdevnexus-140225120726-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> For the last several years Cassandra has been the heavyweight in the NoSQL space. But its massive scalability was accompanied by a bare bones feature set, a substantial learning curve, and a Thrift-based RPC mechanism that left newbies bewildered by a sea of potential client libraries–all with their own fragmented semantics. Over the last year that’s all changed, culminating in the recently unveiled Cassandra 2.0. In this talk I’ll bring you up to speed on Cassandra Query Language, cursors, the new native libraries, lightweight transactions, virtual nodes, and loads of other new goodies. Whether you’re completely new to Cassandra or a seasoned veteran who wants the latest scoop, this talk has something for you.

Big Data Grows Up - A (re)introduction to Cassandra from Robbie Strickland

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0 Online Analytics with Hadoop and Cassandra /slideshow/presentation-12982302/12982302 presentation-120518073922-phpapp01
To date, Hadoop usage has focused primarily on offline analysis--making sense of web logs, parsing through loads of unstructured data in HDFS, etc. But what if you want to run map/reduce against your live data set without affecting online performance? Combining Hadoop with Cassandra's multi-datacenter replication capabilities makes this possible. If you're interested in getting value from your data without the hassle and latency of first moving it into Hadoop, this talk is for you. I'll show you how to connect all the parts, enabling you to write map/reduce jobs or run Pig queries against your live data. As a bonus I'll cover writing map/reduce in Scala, which is particularly well-suited for the task.]]>
To date, Hadoop usage has focused primarily on offline analysis--making sense of web logs, parsing through loads of unstructured data in HDFS, etc. But what if you want to run map/reduce against your live data set without affecting online performance? Combining Hadoop with Cassandra's multi-datacenter replication capabilities makes this possible. If you're interested in getting value from your data without the hassle and latency of first moving it into Hadoop, this talk is for you. I'll show you how to connect all the parts, enabling you to write map/reduce jobs or run Pig queries against your live data. As a bonus I'll cover writing map/reduce in Scala, which is particularly well-suited for the task.]]> Fri, 18 May 2012 07:39:20 GMT /slideshow/presentation-12982302/12982302 rastrick@slideshare.net(rastrick) Online Analytics with Hadoop and Cassandra rastrick To date, Hadoop usage has focused primarily on offline analysis--making sense of web logs, parsing through loads of unstructured data in HDFS, etc. But what if you want to run map/reduce against your live data set without affecting online performance? Combining Hadoop with Cassandra's multi-datacenter replication capabilities makes this possible. If you're interested in getting value from your data without the hassle and latency of first moving it into Hadoop, this talk is for you. I'll show you how to connect all the parts, enabling you to write map/reduce jobs or run Pig queries against your live data. As a bonus I'll cover writing map/reduce in Scala, which is particularly well-suited for the task. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-120518073922-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> To date, Hadoop usage has focused primarily on offline analysis--making sense of web logs, parsing through loads of unstructured data in HDFS, etc. But what if you want to run map/reduce against your live data set without affecting online performance? Combining Hadoop with Cassandra's multi-datacenter replication capabilities makes this possible. If you're interested in getting value from your data without the hassle and latency of first moving it into Hadoop, this talk is for you. I'll show you how to connect all the parts, enabling you to write map/reduce jobs or run Pig queries against your live data. As a bonus I'll cover writing map/reduce in Scala, which is particularly well-suited for the task.

Online Analytics with Hadoop and Cassandra from Robbie Strickland

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0 https://cdn.slidesharecdn.com/profile-photo-rastrick-48x48.jpg?cb=1527578416 Experienced technology leader who enjoys solving emerging problems with the latest technology, mentoring and leading others, and building world-changing applications at massive scale. Specialties: Team building, Certified Scrum Master, IoT, Cassandra MVP, Scala, Spark, Hadoop, big data, highly scalable applications, real-time big data analytics, weather, energy management, utilities, aviation, real estate, finance https://cdn.slidesharecdn.com/ss_thumbnails/alwayson-160909195035-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/always-on-building-highly-available-applications-on-cassandra/65872786 Always On: Building Hi... https://cdn.slidesharecdn.com/ss_thumbnails/lambdaweatherscalemin-150926122856-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/lambda-at-weather-scale-cassandra-summit-2015/53222636 Lambda at Weather Scal... https://cdn.slidesharecdn.com/ss_thumbnails/newanalyticstoolbox-150312194519-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/new-analytics-toolbox-devnexus-2015/45778625 New Analytics Toolbox ...