ºÝºÝߣshows by User: dcnielsen / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: dcnielsen / Thu, 28 Nov 2019 09:22:22 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: dcnielsen 10 Ways to Scale with Redis - LA Redis Meetup 2019 /slideshow/10-ways-to-scale-with-redis-la-redis-meetup-2019/198787023 10-ways-to-scale-with-redis-la-redis-meetup-2019-191128092222
Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture.]]>
Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture.]]> Thu, 28 Nov 2019 09:22:22 GMT /slideshow/10-ways-to-scale-with-redis-la-redis-meetup-2019/198787023 dcnielsen@slideshare.net(dcnielsen) 10 Ways to Scale with Redis - LA Redis Meetup 2019 dcnielsen Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/10-ways-to-scale-with-redis-la-redis-meetup-2019-191128092222-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture.
10 Ways to Scale with Redis - LA Redis Meetup 2019 from Dave Nielsen
]]> 380 0 https://cdn.slidesharecdn.com/ss_thumbnails/10-ways-to-scale-with-redis-la-redis-meetup-2019-191128092222-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 10 Ways to Scale Your Website Silicon Valley Code Camp 2019 /slideshow/10-ways-to-scale-your-website-silicon-valley-cloud-camp-2019/185504773 data-structures-silicon-valley-cloud-camp-2019-191022225437
Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture.]]>
Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture.]]> Tue, 22 Oct 2019 22:54:37 GMT /slideshow/10-ways-to-scale-your-website-silicon-valley-cloud-camp-2019/185504773 dcnielsen@slideshare.net(dcnielsen) 10 Ways to Scale Your Website Silicon Valley Code Camp 2019 dcnielsen Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/data-structures-silicon-valley-cloud-camp-2019-191022225437-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture.
10 Ways to Scale Your Website Silicon Valley Code Camp 2019 from Dave Nielsen
]]> 236 0 https://cdn.slidesharecdn.com/ss_thumbnails/data-structures-silicon-valley-cloud-camp-2019-191022225437-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Redis Streams plus Spark Structured Streaming /slideshow/redis-streams-plus-spark-structuredstreaming/169354454 redis-streams-redis-spark-structured-streaming-190905175520
Continuous applications have 3 things in common: They collect data from sources (ex: IoT devices), process them in real-time (example: ETL), and deliver them to machine learning serving layer for decision making. Continuous applications face many challenges as they grow to production. Often, due to the rapid increase in the number of devices or end-users or other data sources, the size of their data set grows exponentially. This results in a backlog of data to be processed. The data will no longer be processed in near-real-time. Redis Streams enables you to collect both binary and text data in the time series format. The consumer groups of Redis Stream help you match the data processing rate of your continuous application with the rate of data arrival from various sources. Apache Spark’s Structured Streaming API enables real-time decision making for Continuous Applications. In this session, Dave will perform a live demonstration of how to integrate open source Redis with Apache Spark’s Structured Streaming API using Spark-Redis library. I will also walk through the code and run a live continuous application.]]>
Continuous applications have 3 things in common: They collect data from sources (ex: IoT devices), process them in real-time (example: ETL), and deliver them to machine learning serving layer for decision making. Continuous applications face many challenges as they grow to production. Often, due to the rapid increase in the number of devices or end-users or other data sources, the size of their data set grows exponentially. This results in a backlog of data to be processed. The data will no longer be processed in near-real-time. Redis Streams enables you to collect both binary and text data in the time series format. The consumer groups of Redis Stream help you match the data processing rate of your continuous application with the rate of data arrival from various sources. Apache Spark’s Structured Streaming API enables real-time decision making for Continuous Applications. In this session, Dave will perform a live demonstration of how to integrate open source Redis with Apache Spark’s Structured Streaming API using Spark-Redis library. I will also walk through the code and run a live continuous application.]]> Thu, 05 Sep 2019 17:55:20 GMT /slideshow/redis-streams-plus-spark-structuredstreaming/169354454 dcnielsen@slideshare.net(dcnielsen) Redis Streams plus Spark Structured Streaming dcnielsen Continuous applications have 3 things in common: They collect data from sources (ex: IoT devices), process them in real-time (example: ETL), and deliver them to machine learning serving layer for decision making. Continuous applications face many challenges as they grow to production. Often, due to the rapid increase in the number of devices or end-users or other data sources, the size of their data set grows exponentially. This results in a backlog of data to be processed. The data will no longer be processed in near-real-time. Redis Streams enables you to collect both binary and text data in the time series format. The consumer groups of Redis Stream help you match the data processing rate of your continuous application with the rate of data arrival from various sources. Apache Spark’s Structured Streaming API enables real-time decision making for Continuous Applications. In this session, Dave will perform a live demonstration of how to integrate open source Redis with Apache Spark’s Structured Streaming API using Spark-Redis library. I will also walk through the code and run a live continuous application. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redis-streams-redis-spark-structured-streaming-190905175520-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Continuous applications have 3 things in common: They collect data from sources (ex: IoT devices), process them in real-time (example: ETL), and deliver them to machine learning serving layer for decision making. Continuous applications face many challenges as they grow to production. Often, due to the rapid increase in the number of devices or end-users or other data sources, the size of their data set grows exponentially. This results in a backlog of data to be processed. The data will no longer be processed in near-real-time. Redis Streams enables you to collect both binary and text data in the time series format. The consumer groups of Redis Stream help you match the data processing rate of your continuous application with the rate of data arrival from various sources. Apache Spark’s Structured Streaming API enables real-time decision making for Continuous Applications. In this session, Dave will perform a live demonstration of how to integrate open source Redis with Apache Spark’s Structured Streaming API using Spark-Redis library. I will also walk through the code and run a live continuous application.
Redis Streams plus Spark Structured Streaming from Dave Nielsen
]]> 409 0 https://cdn.slidesharecdn.com/ss_thumbnails/redis-streams-redis-spark-structured-streaming-190905175520-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Microservices - Is it time to breakup? /slideshow/microservices-is-it-time-to-breakup/122196511 microservices-isittimetobreakupv0-181106225803
Deploying Microservices with Kubernetes and Redis]]>
Deploying Microservices with Kubernetes and Redis]]> Tue, 06 Nov 2018 22:58:03 GMT /slideshow/microservices-is-it-time-to-breakup/122196511 dcnielsen@slideshare.net(dcnielsen) Microservices - Is it time to breakup? dcnielsen Deploying Microservices with Kubernetes and Redis <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/microservices-isittimetobreakupv0-181106225803-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Deploying Microservices with Kubernetes and Redis
Microservices - Is it time to breakup? from Dave Nielsen
]]> 317 1 https://cdn.slidesharecdn.com/ss_thumbnails/microservices-isittimetobreakupv0-181106225803-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Add Redis to Postgres to Make Your Microservices Go Boom! /slideshow/add-redis-to-postgres-to-make-your-microservices-go-boom/113519554 whypostgresneedsredisv1-180908151504
ºÝºÝߣs for talk delivered at PostgresOpen 2018 in San Francisco https://postgresql.us/events/pgopen2018/schedule/session/538-add-redis-to-postgres-to-make-your-microservice-go-boom/]]>
ºÝºÝߣs for talk delivered at PostgresOpen 2018 in San Francisco https://postgresql.us/events/pgopen2018/schedule/session/538-add-redis-to-postgres-to-make-your-microservice-go-boom/]]> Sat, 08 Sep 2018 15:15:04 GMT /slideshow/add-redis-to-postgres-to-make-your-microservices-go-boom/113519554 dcnielsen@slideshare.net(dcnielsen) Add Redis to Postgres to Make Your Microservices Go Boom! dcnielsen ºÝºÝߣs for talk delivered at PostgresOpen 2018 in San Francisco https://postgresql.us/events/pgopen2018/schedule/session/538-add-redis-to-postgres-to-make-your-microservice-go-boom/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/whypostgresneedsredisv1-180908151504-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> ºÝºÝߣs for talk delivered at PostgresOpen 2018 in San Francisco https://postgresql.us/events/pgopen2018/schedule/session/538-add-redis-to-postgres-to-make-your-microservice-go-boom/
Add Redis to Postgres to Make Your Microservices Go Boom! from Dave Nielsen
]]> 6204 5 https://cdn.slidesharecdn.com/ss_thumbnails/whypostgresneedsredisv1-180908151504-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 BigDL Deep Learning in Apache Spark - AWS re:invent 2017 /slideshow/bigdl-deep-learning-in-apache-spark-aws-reinvent-2017/83083200 bigdl-deep-learning-spark-aws-reinvent-2017-171130213810
In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent. ]]>
In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent. ]]> Thu, 30 Nov 2017 21:38:10 GMT /slideshow/bigdl-deep-learning-in-apache-spark-aws-reinvent-2017/83083200 dcnielsen@slideshare.net(dcnielsen) BigDL Deep Learning in Apache Spark - AWS re:invent 2017 dcnielsen In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam & Dave Nielsen at re:invent. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bigdl-deep-learning-spark-aws-reinvent-2017-171130213810-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this talk, you will learn how to use, or create Deep Learning architectures for Image Recognition and other neural network computations in Apache Spark. Alex, Tim and Sujee will begin with an introduction to Deep Learning using BigDL. Then they will explain and demonstrate how image recognition works using step by step diagrams, and code which will give you a fundamental understanding of how you can perform image recognition tasks within Apache Spark. Then, they will give a quick overview of how to perform image recognition on a much larger dataset using the Inception architecture. BigDL was created specifically for Spark and takes advantage of Spark’s ability to distribute data processing workloads across many nodes. As an attendee in this session, you will learn how to run the demos on your laptop, on your own cluster, or use the BigDL AMI in the AWS Marketplace. Either way, you walk away with a much better understanding of how to run deep learning workloads using Apache Spark with BigDL. Presentation by Alex Kalinin, Tim Fox, Sujee Maniyam &amp; Dave Nielsen at re:invent.
BigDL Deep Learning in Apache Spark - AWS re:invent 2017 from Dave Nielsen
]]> 263 1 https://cdn.slidesharecdn.com/ss_thumbnails/bigdl-deep-learning-spark-aws-reinvent-2017-171130213810-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Redis as a Main Database, Scaling and HA /slideshow/redis-as-a-main-database-scaling-and-ha/64262577 redisasamaindatabasescalingandha-160721205119
Iskren Chernev, an Independent developer, uses a lot of Redis. In this talk, Iskren will look at a particular Redis use-case -- using it as the main database (not cache). Iskren will show how to achieve reasonable guarantees about data integrity, speed, high-availability in an event of failure and infinite horizontal scalability. This particular approach has proven successful in managing clusters of up to 2400 nodes, and storing data north of 7TB before replication. We'll cover ways to separate your data appropriately into many nodes, performing different types of migrations (from another database, from one cluster to another, scaling migrations and migrating out of Redis), moving nodes without downtime, some configuration tips and monitoring.]]>
Iskren Chernev, an Independent developer, uses a lot of Redis. In this talk, Iskren will look at a particular Redis use-case -- using it as the main database (not cache). Iskren will show how to achieve reasonable guarantees about data integrity, speed, high-availability in an event of failure and infinite horizontal scalability. This particular approach has proven successful in managing clusters of up to 2400 nodes, and storing data north of 7TB before replication. We'll cover ways to separate your data appropriately into many nodes, performing different types of migrations (from another database, from one cluster to another, scaling migrations and migrating out of Redis), moving nodes without downtime, some configuration tips and monitoring.]]> Thu, 21 Jul 2016 20:51:19 GMT /slideshow/redis-as-a-main-database-scaling-and-ha/64262577 dcnielsen@slideshare.net(dcnielsen) Redis as a Main Database, Scaling and HA dcnielsen Iskren Chernev, an Independent developer, uses a lot of Redis. In this talk, Iskren will look at a particular Redis use-case -- using it as the main database (not cache). Iskren will show how to achieve reasonable guarantees about data integrity, speed, high-availability in an event of failure and infinite horizontal scalability. This particular approach has proven successful in managing clusters of up to 2400 nodes, and storing data north of 7TB before replication. We'll cover ways to separate your data appropriately into many nodes, performing different types of migrations (from another database, from one cluster to another, scaling migrations and migrating out of Redis), moving nodes without downtime, some configuration tips and monitoring. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/redisasamaindatabasescalingandha-160721205119-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Iskren Chernev, an Independent developer, uses a lot of Redis. In this talk, Iskren will look at a particular Redis use-case -- using it as the main database (not cache). Iskren will show how to achieve reasonable guarantees about data integrity, speed, high-availability in an event of failure and infinite horizontal scalability. This particular approach has proven successful in managing clusters of up to 2400 nodes, and storing data north of 7TB before replication. We&#39;ll cover ways to separate your data appropriately into many nodes, performing different types of migrations (from another database, from one cluster to another, scaling migrations and migrating out of Redis), moving nodes without downtime, some configuration tips and monitoring.
Redis as a Main Database, Scaling and HA from Dave Nielsen
]]> 788 3 https://cdn.slidesharecdn.com/ss_thumbnails/redisasamaindatabasescalingandha-160721205119-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Redis Functions, Data Structures for Web Scale Apps /slideshow/redis-functions-data-structures-for-web-scale-apps/56149325 austindevopstop5usesofredis7-151215043817
Dave Nielsen of Redis Labs presents the Top Redis Functions, Data Structures used when developing Web Scale Apps]]>
Dave Nielsen of Redis Labs presents the Top Redis Functions, Data Structures used when developing Web Scale Apps]]> Tue, 15 Dec 2015 04:38:17 GMT /slideshow/redis-functions-data-structures-for-web-scale-apps/56149325 dcnielsen@slideshare.net(dcnielsen) Redis Functions, Data Structures for Web Scale Apps dcnielsen Dave Nielsen of Redis Labs presents the Top Redis Functions, Data Structures used when developing Web Scale Apps <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/austindevopstop5usesofredis7-151215043817-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Dave Nielsen of Redis Labs presents the Top Redis Functions, Data Structures used when developing Web Scale Apps
Redis Functions, Data Structures for Web Scale Apps from Dave Nielsen
]]> 901 5 https://cdn.slidesharecdn.com/ss_thumbnails/austindevopstop5usesofredis7-151215043817-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Cloud Storage API /dcnielsen/cloud-storage-api-presentation cloudstorageapi20090127-1233363624299877-3
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Cloud Storage API from Dave Nielsen
]]> 346 3 https://cdn.slidesharecdn.com/ss_thumbnails/cloudstorageapi20090127-1233363624299877-3-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Mashery /slideshow/mashery-presentation/973158 mashery20090127-1233363640409506-3
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]]> Fri, 30 Jan 2009 19:02:56 GMT /slideshow/mashery-presentation/973158 dcnielsen@slideshare.net(dcnielsen) Mashery dcnielsen <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mashery20090127-1233363640409506-3-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Mashery from Dave Nielsen
]]> 583 2 https://cdn.slidesharecdn.com/ss_thumbnails/mashery20090127-1233363640409506-3-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Google App Engine /slideshow/google-app-engine-presentation/973157 introappengine20090127-1233363620548182-3
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]]> Fri, 30 Jan 2009 19:02:56 GMT /slideshow/google-app-engine-presentation/973157 dcnielsen@slideshare.net(dcnielsen) Google App Engine dcnielsen <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/introappengine20090127-1233363620548182-3-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br>
Google App Engine from Dave Nielsen
]]> 479 2 https://cdn.slidesharecdn.com/ss_thumbnails/introappengine20090127-1233363620548182-3-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Unified Cloud Storage Api /slideshow/unified-cloud-storage-api-presentation-737192/737192 unified-cloud-storage-api-1226301564142370-9
Dave Nielsen's presentation on Cloud Computing and Cloud Storage APIs]]>
Dave Nielsen's presentation on Cloud Computing and Cloud Storage APIs]]> Sun, 09 Nov 2008 23:19:07 GMT /slideshow/unified-cloud-storage-api-presentation-737192/737192 dcnielsen@slideshare.net(dcnielsen) Unified Cloud Storage Api dcnielsen Dave Nielsen's presentation on Cloud Computing and Cloud Storage APIs <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/unified-cloud-storage-api-1226301564142370-9-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Dave Nielsen&#39;s presentation on Cloud Computing and Cloud Storage APIs
Unified Cloud Storage Api from Dave Nielsen
]]> 1096 5 https://cdn.slidesharecdn.com/ss_thumbnails/unified-cloud-storage-api-1226301564142370-9-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Integrating Wikis And Other Social Content /slideshow/integrating-wikis-and-other-social-content-presentation/737164 integrating-wikis-and-other-social-content-1226299563023454-8
Presentation of Wetpaint Injected from Silicon Valley Code Camp 2008]]>
Presentation of Wetpaint Injected from Silicon Valley Code Camp 2008]]> Sun, 09 Nov 2008 22:49:44 GMT /slideshow/integrating-wikis-and-other-social-content-presentation/737164 dcnielsen@slideshare.net(dcnielsen) Integrating Wikis And Other Social Content dcnielsen Presentation of Wetpaint Injected from Silicon Valley Code Camp 2008 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/integrating-wikis-and-other-social-content-1226299563023454-8-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation of Wetpaint Injected from Silicon Valley Code Camp 2008
Integrating Wikis And Other Social Content from Dave Nielsen
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